Well, hello everybody. It's wonderful to welcome you to the fifth of the HMI day seminars. Today we've got Assistant Professor Barbara Kivir from Stanford. I'd like to start by acknowledging the fact that I'm from Narragore lands, I work in Narragore lands and paying my respects to the traditional custodians of country throughout Australia, especially their elders past, present and emerging. So Barbara is joining us now from Stanford. She's an Assistant Professor there of Sociology. I met Barbara when we were having some doing a conference with the Human Standards Day Institute last year. And she's one of those rare creatures who has spent time with philosophers and is keen to spend more time with philosophers. So that's a wonderful thing to have experienced. And I work on the moral limits of credit scoring and credit kind of scoring practices has received significant recognition in Sociology and has wonderful connections through to several different HMI themes. So Barbara is going to be talking about the moral legibility of narrative and case comparison. So as usual, keep the intro short and go straight over to you, Barbara, if you'd like to get started and share the screen. Okay, thank you. Let's see. Does that look good? Okay, thumbs up. Okay, great. Well, thank you, Seth. I'm delighted to be here. What I'm going to present today is brand new. So I really look forward to your feedback. Please don't pull any punches in the Q&A because I want to turn this into something, something good. So I'm an economic sociologist and a lot of what I study is how different groups of people have competing ideas about what fairness means in market settings. I'm particularly interested in cases where companies use personal data to make predictions about how individuals will behave and then give them different things as a result. I'm going to start today with an example of this. About 25 years ago, car insurance companies in the US began using consumer credit scores to set prices for car insurance policies. As it turns out, credit scores are great at predicting who will file insurance claims and thus cost companies money. Now credit scoring, I should make clear, is an example of algorithmic prediction. Data from a lot of different people's credit files are used to make predictions about how particular individuals will behave. People with low credit scores are more likely to file insurance claims, so car insurers charge them higher prices. When regulators and legislators found out the companies were doing this, they did not react kindly. Credit-based insurance scores, as the scores are known, have been the subject of intense scrutiny, including five congressional hearings, 17 investigations, or rather at least 17 investigations in at least 17 different states in the US insurance is regulated largely at the state level, and four major reviews by the National Association of Insurance Commissioners, which is the professional association of insurance regulators in the US. Dozens of states, practically all states, have passed laws restricting how car insurance companies can use credit scores. For our purposes, what's interesting is that these debates have largely been about whether or not these scores are fair. One of those congressional hearings, for example, was titled simply, Credit-Based Insurance Scores, Are They Fair? I have an entire paper about this debate, here it is, I highly recommend it, but today I'm going to jump to the punchline because what I really want to talk about is where this case is leading me next. In a nutshell, the debate over credit-based insurance scores pitted two different conceptions of market fairness against each other, actuarial fairness and moral deservingness. In the case of actuarial fairness, insurance companies said if it predicts it's fair, that is to say credit scores are correlated with insurance outcomes and therefore they're legitimate to use. If you work in algorithmic fairness, predictive validity as a moral claim is probably something you've come across before. In the US, this concept of fairness is actually pretty well institutionalized in insurance law and regulation. Now, policymakers, legislators and regulators did care about predictive validity, but it wasn't all they cared about. Policymakers also got really hung up on two questions. Policymakers asked why do credit scores predict insurance claims and why do some people have low scores in the first place? Again, if you work in algorithmic fairness, this probably feels familiar. People are constantly asking why data and predictions work the way they do. People eternally seek explanations, accounts, stories, causal narratives and so on. In this case, the reason policymakers wanted to know why, I argue, is because they were pulling from a second moral framework, one that holds the people ought to get what they deserve. By deservingness, I mean that the goodness or badness of a person's past actions indicates what they ought to receive. So for example, policymakers didn't like the fact that a person could get really sick, wreck their credit when they couldn't pay off tens of thousands of dollars in medical bills, and then see their car insurance rates go up as a result. In that situation, they didn't think people deserved higher prices. Importantly, the tool policymakers used to adjudicate whether people were getting what they deserved was narrative. Policymakers search for stories about why scores predicted claims and why people had low scores. And depending on what that story was, policymakers judged the use of credit scores as either fair or unfair. Some stories passed moral muster and others didn't. So what happened in terms of public policy is that legislators and regulators created all of these exceptions in law and regulation. Times when people might have low credit scores, but they nonetheless shouldn't get higher insurance prices. Part of this was about life events outside of people's control. For example, if drivers had low scores because they'd been ill or laid off from a job or displaced due to natural disaster. But a lot of it was also about what I think of as morally laudable behavior. Things that people could control, but which nonetheless shouldn't lead to higher insurance prices. For instance, one situation that came up time and again was that some people had low credit scores because they never borrowed money in the first place. I'll share just one quote on this from a Michigan State legislator who was testifying at a hearing in that state. My grandfather and grandmother, he said, they paid by cash for everything. They did not have a lengthy credit report. Now, is it fair to say that they have to pay higher rates because they're not using credit cards or they're not taking out loans? That's absolutely absurd. The point is that people can have low credit scores because they virtuously don't take on debt rather than leveraging to the Hill. Some people save their money before buying things. What's unfair then is the market punishing them for this good behavior. So what legislators and regulators did was write all these exceptions in the law and regulation. For example, one said your insurance rates can't go up just because you don't happen to have a credit score just because you're invisible to the credit bureau. To return to this, what I want to argue, and now we're stepping beyond that original paper into my new work, is that each of these moral arguments about actuarial fairness and moral deservingness depends on a particular way of organizing cognition. What do I mean by way of organizing cognition? Well, I'm going to argue that actuarial fairness depends on viewing people as cases and thinking in terms of case comparison. While moral deservingness depends on viewing people as actors in series of unfolding events and thinking in terms of narrative. What we make from credit based insurance scoring then is that algorithmic prediction, which mechanically requires that we view people as cases and think in terms of case comparison is conducive to some sorts of moral evaluation, but not to others. So here's an overview of the argument I'm going to make today. First, case comparison and narrative represent distinct ways of organizing cognition, more on that in a moment. Second, each way of organizing cognition makes various features of people more or less legible. Third, this difference in legibility makes various moral standards more or less easy to adjudicate. And that's because different moral standards require different sorts of information about people. And fourth, this has bearing on algorithmic fairness because algorithms necessarily work through case comparison. Some types of unfairness are harder to think in, given the form information takes. Okay, to start at the top of the list. What does it mean for case comparison and narrative to organize cognition differently? Let me start with an example from the work of sociologist Carol Heimer, who in the 1980s and 90s spent a lot of time in neonatal intensive care units.   Sorry, I didn't mean to be a doctor. That's really amusing. So when doctors are presented with a new sick infant, they view the baby as a case, as an entity with a series of attributes, which helps slot it into a particular category or set of categories.  In the process of comparison across cases, doctors make determinations about how ill the child is, what its prognosis is, and what action to take.  So there's an intensive rather than extensive knowledge. For parents, the key to making sense of the child in itself is to observe change over time. Parents watch a child live its life hour by hour, day by day, and they capture all the rich contextual detail that goes with that. Heimer refers to this as organizing cognition through biological narrative. I'm going to refer to it simply as narrative. Now this distinction, which is essentially the difference between thinking in categories and thinking in stories, shows up across disciplines. Here's my distillation of some of the main differences that occur when we think about people as cases with attributes versus as actors in unfolding narratives. At the bottom of the slide are some of the sources I drew on in constructing this chart, which takes a lot of inspiration from a 2001 article by Carol Heimer. Let's start at the top. Context and circumstance. When we think about people as cases with particular attributes, we strip away a lot of the particulars. This isn't incidental. Simplifying people or any other entities is what makes them comparable. As many scholars have observed, this sort of flattening is necessary for collecting information at scale, running bureaucracies, and conducting statistical analysis. Narrative, on the other hand, not only preserves context and circumstance, but relies on it. Stories, for example, have settings. That's part of what it means to be a story. Narrative is a thick form of information that goes hand in hand with conveying particulars. Similarly, people's mental states, their emotions, intentions, and so forth, fade to the background when we render people as cases. Yet again, these details are often crucial components of narrative. A story likely includes what happened, but also whether it's what a person meant to happen and how they felt about events having transpired in that way. Narrative, for example, can include things like remorse and contrition. This next one is perhaps counterintuitive. When we render people as cases, we decide in advance what sorts of information are important. We decide which attributes matter, and then we collect those attributes. But that's not how narrative works. With narratives, we often don't know what the telling features are until we get to the end of the story. It's often only after we know how things turned out that we can look back and truly understand what was significant. More intuitive, perhaps, is that cases are atemporal. All the information is available simultaneously. Narrative, on the other hand, runs chronologically. One thing happens, and then the next. And that ordering is important. Stories mean different things depending on the sequencing of events. Getting drunk and then into a car accident means something different than getting into a car accident and then drunk. Cases typically obscure the role of other people. The information we have about a person is generally understood as about that person. Yet, in narrative, other characters often play key roles so we can much more easily interpret events as related to the networks in which people are embedded, families, communities, workplaces, and so on. And finally, the last two, which I've included because I think they're particularly relevant for algorithmic fairness. First, rendering people as cases and manipulating those cases with algorithms, as algorithms do, goes with a particular mathematical understanding of causality. If you're drawing a dag, one of those directed acyclic diagrams, then you're relying on people as cases. If, on the other hand, you're thinking theoretically in generalizable terms about how the world works, your approach to causality, I argue, is almost certainly narrative. But in articulating how one thing causes another, you almost necessarily default to a story. And finally, how we get to better predictions. If people are cases and we want to know more about people, then we add more cases. If, on the other hand, people are actors in unfolding narratives and we want to know more about people, then we add more details to those narratives. So for example, as the years go by, I get better at predicting how my husband will react to new situations, not because I've gone out and collected a larger sample of husbands, but because I've spent more time observing the one I've got. Okay, so that was a lot. What does it all add up to? It all adds up to this. Depending on whether we render people as cases with attributes or as actors in unfolding narratives makes different aspects of those people legible, more visible. And now transitioning back to what I started with competing notions of fairness. I argue that these differences in legibility mean that some notions of fairness are easier to adjudicate when we render people as cases, and other notions of fairness are easier to adjudicate when we render people in narrative. So now, in a way to switch gears, but it'll all come back together. Here are four notions of justice slash injustice. Desert cumulative disadvantage disproportionate impact and what I'm calling predictive parody. There are many more ideas about what constitutes justice or fairness that I could have put on this list and I should say I'm using perhaps luckily the terms justice and fairness interchangeably. These are meant to be examples only I'm not saying these ought to be our top four priorities or these are the only things that matter by long shot. So let me run through my definitions of each. Desert, I mean a standard which holds that virtue to be rewarded and vice should be punished to return to the example of credit based insurance scores. This would suggest that if you have bad credit because you're irresponsible, then it's fair to raise your insurance prices. But if you have bad credit because you're unlucky, then it's not. In fact, that's exactly what many policymakers effectively argued. With cumulative advantage and disadvantage, I'm getting at the notion that starting advantage should not turn into additional advantage, nor should starting disadvantage turn into additional disadvantage. For example, this seems to be what motivates at least some people to argue that hiring decisions should not be based on criminal histories. That it's wrong for one serious disadvantage to beget another to continue reverberating through one's life across new domains. This can also work in the other direction with advantage unfairly compounding over time, you hear people lamenting how the rich get richer and so on. By disproportionate impact. I mean the idea that certain social group should receive the same proportions of benefits and burdens. So in the US context, this is often about whether black Americans and white Americans see similar aggregate outcomes, or whether men and women do. By predictive parody. I mean that predictions which lead to allocations should be wrong about different people in the same ways. So here I'm rolling together a lot of different definitions that appear in the algorithmic fairness literature. For my purposes, a bunch of the distinction to get made between say, I guess I should say for my purposes here, not necessarily all my work. A bunch of the distinctions to get made between say predictive validity and balance between false positives and false negatives don't really matter. At least I don't think they do. For me, what salient is that all these definitions assume that fairness is something that can be determined by examining the rates at which mathematical predictions are inaccurate across individuals. Okay, note that everything I have on the slide is about distributive justice in that each definition articulates a moral standard for allocating benefits and burdens. What's being allocated might be jobs, loans, prison sentences, feeding tickets, transplanted organs, college educations or something else. My empirical work focuses on the market allocation of economic goods, but here I'm trying to cast a broader net. The important scope condition is that all of these conceptions of justice assume that it's right to give different people different things in the first place. Debates about algorithmic fairness often presume that there's nothing fundamentally corrupt about differentiating among individuals. But of course, in plenty of situations that's totally the case. Trial by jury isn't just for defendants who deserve it or who are predicted to use it in a particular way. It's for everyone and a story. I'm also limiting my discussion to situations in which a decision about who gets what information decision makers have about individuals. So for example, a school that allocates seats by lottery is giving different people different things, but it's not differentiating based on the traits or actions of individuals beyond I suppose signing up for the lottery in the first place. Okay, here then is my argument in table form. In the first column is each notion of justice in the second column are some of the key questions people operating under each conception of justice tend to ask in order to decide whether the moral standard has been met. And in the last column is the style of cognition, most conducive to adjudicating whether or not that standard has been met to answering the questions in the second column. You can see that the first two notions of justice for easier to adjudicate with information about people organized into narrative. And the second two notions of justice, I'm claiming are easier to adjudicate with information about people organized into cases that can be compared. Okay, to start with moral dessert. In the second column we have, what did this person do was the act intentional was the person in control of the situation to society consider the act to be good or bad. Here we see the importance of mental states, the role of other actors, and broader context and circumstance. Importantly, I can't deserve something by virtue of something that someone else has done. So it's important to know what transpired to make me look the way I do. It's really hard and apply myself at school than I deserve my good grades, but I don't deserve those grades if I get them because my parents bribed my teacher. So how events unfold over time matters. The same is true of cumulative advantage and disadvantage. In the second column we have questions such as what leads people to show up in the data the way they do. Will this decision compound life trajectories is this moment a turning point. This sequence of events is key. These concepts imply certain life trajectories. The point isn't that it's wrong to make an unfavorable decision about a person down on his luck, but that it's wrong if that unfavorable decision plays into a broader ongoing arc of ever increasing disadvantage. When I come up, my first two rows, my argument is that narrative is the better way to organize cognition. If these are the questions we want to answer. And now for the conceptions of justice that more easily map onto the logic of case comparison. Importantly, both disproportionate impact and what I'm calling predictive parody are statements about forms of equality or inequality, which I'll return to at the end of the talk. So addressing disproportionate impact relevant questions include at what rates do various socially meaningful groups of people receive benefits and burdens. Does anything justify differences in those rates. So by socially meaningful groups I mean groups such as black people white people men women and so on. You need people rendered as cases because the question is essentially do cases that are similar on all except for one salient attribute get treated differently. It doesn't matter why any given person has been assigned the attributes they have what matters is given those attributes are decisions being made equally across cases. And finally, predictive parody. In the second column we have due predictions that determine allocations work the same way for all people are predictions wrong about some people in novel ways. And the algorithmic fairness literature. This approach is often framed as being about socially meaningful groups but I'm actually not sure that needs to be the case. After reading some recent work by Debbie Hellman I started thinking about the following example. If we live in a society with only two criminal defendants, then the standard for conviction should either be preponderance of the evidence or beyond a reasonable What would be unfair would be using one standard for the first defendant and the other standard for the second defendant. So this is still about cases. Each person has been categorized as a criminal, rather than as a civil defendant, which is what necessitates their quality of treatment. But the defendants raised sex and so on doesn't come into play. Of course, in many real world examples, those things do matter greatly. I'm just pointing out that I don't know that they necessarily have to for there to be concerned about fairness in this way. Okay, so where does this leave us. We're turning to the overview of my argument. I started out by arguing that case comparative and case comparison that is in narrative represents distinct ways of organizing cognition. I then showed you how each way of organizing cognition makes various features of people more or less legible. I next moved on to how this difference in legibility makes various moral standards more or less easy to adjudicate. And now I'm here. And that's to argue that this has bearing on debates about algorithmic fairness, because algorithms necessarily work through case comparison, which means that some types of unfairness are harder to think in given the form information takes. So let me go one step further and say that starting starting a conversation about algorithmic fairness with algorithms and the data that go into them is inherently limiting. My proposition is that the form information takes shapes our cognition in ways that not only make some definitions of fairness easier to adjudicate, but more salient in the first place. Which is that this then crowds out other competing definitions of fairness that rely on other ways of organizing cognition and knowing about individuals. I'll end then on the issue of equality. Based on my not comprehensive reading of the algorithmic fairness literature, I'm a sociologist who's tries to keep up with it, but it's not the literature I work in. That's my caveat. It seems to me that equality is often taken as a definitive statement on fairness. Now I don't want to tell Aristotle his business treating like cases alike. That's great. Like I buy into equality. I'm not saying that it doesn't matter. I just don't think that it's the only thing we need to be paying attention to if we're going to truly grapple with the morality of algorithmic decision making. But I get why so much of the literature lands there. Cases and the logic of case comparison organize our thinking in a way that makes equality a very legible notion of justice. Nonetheless, I think it's incumbent upon working in this space people working in the space to not let the nature of their tools dictate the nature of their thinking. So the other day I was reminded of that saying, if you have a hammer, everything looks like a nail. That probably overstates my own position, but it does point in the right direction. Algorithms clearly let us see more than we could otherwise, but they also only let us see certain types of things. So we need to not forget to continue to look in other ways as well. So thanks so much. That's all I have, and I really look forward to your questions. That was fantastic. Thanks so much, Barbara. Okay, so what we're going to do now is we're going to have Q&A. I just wanted to say a quick welcome to some friends from the Center for AI and Digital Ethics at the University of Melbourne who are joining us on the panel. So the people who I unceremoniously transferred from participant to panelist. Very nice to have you with us. And a few other friends are joining us too. We have a nice queue of hands already. The first one I saw go up was Serita. So Serita, would you mind starting us off? Yeah, sure. Hi. Thanks so much for this talk. So I have a question that has to do with where you were going from the sort of conclusion that you just gave. In particular, so as I understood what you were saying, you were talking about this sort of like this case-based logic and this narrative-based logic that are different ways of sort of understanding the fairness and decision-making processes. And what you were saying, like what I heard you saying with this was that the narrative-based logic was, while the case-based logic is more sort of algorithmically tractable, the narrative one is more nuanced and sort of responsive to the morally salient features, contextual features that people experience. And so what you were sort of heading towards I thought perhaps was, and maybe I'm reading too much into this, was that we should try to shift towards framing things in that way, even if it sort of sacrifices some of that algorithmic tractability. And I'm wondering if there's another way of thinking about it, which is having as a goal, ensuring a kind of inter-translate ability between the case-based reasoning and the narrative-based reasoning so that we can continue to sort of leverage the sort of computational capabilities of that case-based reasoning while being able to interpret it in a narrative way and criticize it from that lens. Yeah, definitely. Thanks for that question. So it's interesting sort of the idea of how interoperable are these two like logics of, I guess the way I would think about it is like cases and narrative are like two competing ways of making sense of people that then sort of lend us to sort of more easily see different types of moral arguments. And so I guess for me I think that the example I started with with credit-based insurance scoring was a really good example was in that, I mean not that I'm holding that up as like this is how policy played out in this setting is what we should try to emulate in other settings. It was largely, it was problematic in other ways. But basically regulators cared about predictive validity. They cared about the sort of moral standard that case-based logic was really good at shedding light on. And then they also cared about moral deservingness for which they had to start reaching for all these like causal narratives about basically why people showed up in the data the way they did. So to me it's perhaps, I really like the direction you're going, but I might just tweak it a little bit and say instead of the two being interoperable that we have multiple moral standards that we think ought to be met in various situations. And to meet some of those moral standards, you know, we need the logic of case comparison and for others we need narrative. And so we need both, not necessarily that we're ever going to be able to reduce them into like some third new thing. I think that they might actually be fundamentally at odds. I want to go back and read a lot of like cognitive psychology, but like, I don't know, people thinking categories and they think in stories. It seems like very fundamental, perhaps fundamentally at odds and different and distinct, but both very important, like try to live your life only thinking in categories or only thinking in narratives. I don't think we would function. But thank you. Yeah. So yeah, I guess basically I'm just a quick follow up here. So I'd like to the language you're using in terms of like how you're rendering people you're rendering them at categorically or in a case-based kind of way. And it seems like you could think of that as just fundamentally different like paradigmatically or you could think of that as a shared like trying to do one of your goals is trying to develop a shared framework so that they're not different. Yeah, I think there might be like, um, like mathematicians and I presume like computer scientists and engineers like always want elegant solutions like sort of like, like most reduced form and I think that ultimately, like, like, you know, figuring out what's fair, I think is a political question which I think is often like the opposite like reduced form is super dangerous like what you actually need is like constant competing ideas clashing against each other. So that might actually be another way of thinking about like the extent to which like interoperability is even desirable. But thanks. I really appreciate that. And I'm glad to know that the word render wasn't odd or off putting. I couldn't think of a better one. So I'm going to, we'll go to a computer scientist next part of the point of the seminar to bring all these different disciplinary perspectives together. I just wanted to throw in that I think that the the recent theory over a level exam results in the UK. It's just a really, really clear example of the two different types of thing that you're talking about. So in a quite different context, but you read any number of op-eds and they would basically just be perfect Chris for your meal. So next question is going to be from Michael Yang. Hi, thank you for that great talk. My question is still going to be about interoperability in some way so I particularly I think I think actually causality like is one potential way I think that computer scientists have tried to sort of try to make things more interoperable. I don't have any love for causal based definitions of fairness. I probably agree pretty wholeheartedly with one of our early speakers, Lily, who about like, like the weird, the probably bad metaphysics of using causal based notions of fairness to advocate between demographic variables. But I think if there's any benefit to causal reasoning, it should be that it helps people to reason about like the variables, the data that we can measure in a more narrative form right. And on one of your other slides, you explicitly said that you think that kind of cause out the computer science version of causality is still pretty case based. And yeah, I think I may disagree. So yeah, that's thanks for flagging that I need to really think that through I think, like, coming up in sociology all the statistics class I took always sort of. There was always a very big emphasis on theory is why you build the models the way you do. So for example, you might have variables in your model that wind up not being significant. Do you keep them it's like of course you keep them if you had a theoretical reason like a, you know, like a prior reason to think that this is how the world works. And so I need to think a lot more about that but I think that there are. Yeah, my sense is that they're that they're distinct but I don't have a good articulation of how or why yet, but I guess one thing I should have clarified is I don't think that these are like I don't think like, like here's someone building algorithms like they're working they're in case based logic, you know, like 24 seven like I think these two things are constantly working with with each other like I think that you can't. You can't decide to like look for a new sort of variable to put into your algorithm without narrative like why would you have like Google that to see if there's a data broker that can tell you that data in the first place you know like even if like the thing you're creating on the logic of cases so. Yeah, I guess I'll maybe just sort of say I agree with you that I, I have failed to articulate how they're different and I'll sort of add another problem that you did not articulate to my argument which is they're not actually perhaps, like these things that don't constantly appear at the same time. I definitely need to think more about about how to articulate that instinct I have, which comes from my training that these are like very discreet ways of thinking about about thinking. Yeah. Yeah, and for sure I think a lot of the, not all like I said my general fan of causal based notions and I think some of the times to use it to come up with a way of the fairness is like in the reductive way that you're just talking about earlier so I don't think that's helpful but. And I'll say one thing like one of the takeaways from that. The paper that I have written about credit based insurance going which I'll post to the Slack channel after this is that, at least in this case what policymakers were doing they weren't looking. They weren't looking for a story that said this variable is causally related therefore it's legitimate. This one is spurious therefore it's not legitimate. They were using causal stories in order to adjudicate whether or not something was fair so like the the the causal story wasn't the thing determining fairness it was the tool that was being used in order to adjudicate fairness. So, for example, you know, and this is all happening sort of like in this political policy realm. So like one strong narrative was that, you know, your responsibility is what accounts for this relationship between credit scores and car insurance claims that if you're irresponsible, you don't manage your financial obligations well your lack of days ago about borrowed money, and at the same time your reckless in your driving so that is sort of what's causing you know if you think of a dad like it's simultaneously causing both of these things. And a lot of policymakers like okay well that's the story that's great. But then this competing causal story of, okay but what if it's not responsibility what if it's income, because really what's being measured isn't car accidents but insurance claims. And so people who are rich who get into accidents, probably don't file claims because at least in the US context, their rates will go up the following year, like your insurance broker tells you don't file small claims. So there you have, instead of you know, responsibility sort of causing this connection between scores and claims, you have income. So, in that case, policymakers like well that's not legitimate if that's if that's what's causing those two things. So in both cases they were sort of willing to agree like yes, this is these are like both positive, possibly causal, but one causal story, sort of past their, like moral standard of, you know, dessert and the other didn't. Okay, so I'm going to keep the disciplinary cycling going and we're going to go to Lorna. So, Jeannie Patterson, who's the director of the Center for Digital Ethics Co-Director of Central AI and Digital Ethics at the University of Melbourne. Jeannie. Thanks, Seth. And thank you so much for this talk Barbara. It was incredibly interesting and I'd really like to read your paper, in fact, so I'm looking forward to that. So listening to you, I had two thoughts from a sort of legal perspective and one was I think in law this distinction you're talking about case-based fairness and moral deservingness or actuarial fairness and moral deservingness. We would talk about in terms of procedural fairness, I think, and there's other lawyers in the crowd who might correct me, which is the idea that you that the fairness is about having the same process for everybody, even though they're underlying circumstances are different, but you have the same process and therefore that's your definition of fairness and substantive fairness is where you take the narrative into account and in particularly look at just dessert. So that's that sort of relationship between those two aspects of fairness is something that we grapple with a lot, particularly in the field of credit, in fact, because and this was my question to you. It's almost as the institutional factors play a role here because from the perspective of the insurer, I don't actually think they're interested in fairness. They're interested in risk, credit risk, and therefore actuarial fairness is, is what resonates for what they're doing processes and the way they can operationalize the process. And that's very analogous to the doctors who need a process they can follow. And the fact that sometimes you get it wrong in terms of moral deservingness is almost part of the institutional arrangement, because in most cases you'll get it right. And in most cases you've got this element of replicability, which also goes to the institution's concept of fairness. Totally. Yeah, no, thanks for that question. Yeah, so I mean, much of my work, even though you probably don't sort of pay up from here is like, I think that fair, like, coming at it as a sociologist like fairness standards are never sort of things that exist in the abstract they're always sort of like embedded in certain types of institutional arrangements or go with certain actors that have lots of different motivations for arguing that this is the right standard of fairness, or even that this is a standard of fairness in the first place. I mean like predictive validity, which is essentially actuarial fairness, if it predicts it's fair, like, I have definitely conversations, you know, with like philosophers who are like, that's not, that doesn't fit into the category of fairness. Like that's sort of like a corrupt understanding, but I think it's very intentional that it gets framed as a type of fairness, like for like rhetorical power like in these realms. So I think, at least in the, you know, in the US, I think that, I mean, my experience with like talking to companies both in insurance and also in credit scoring, they actually do really care about like how people perceive things as fair. But because they don't want to wind up on the front page of the Wall Street Journal when someone finds out, you know, like, oh, look, targets predicting whether or not you're pregnant, you know, which totally blindsided them right. So like they actually do really want to have like some sense that doesn't mean that they're trying to like enact a fair society in their corporation and I don't know that that's sort of the standard we should be be holding them to, but I definitely take, take your point that that that idea like as a standard of fairness is being articulated by an actor that might, you know, sort of be, and I don't want to say that they don't care about like what's good and right like I think that, you know, I think the idea that like our main purpose is to, to like make money for our shareholders gets internalized as an idea about like what's good and right as well. But, but I definitely take, take your point in terms like the procedural distributive justice I hadn't really thought about it that way but I should, I should think about that. I think I'm not even saying that corporations are cynical or insurance companies are cynical. I'm saying that they have to process many, many claims. And as soon as you start bringing in a narrative about just desserts, you're putting a lot of discretion on individual players for an institutional perspective is very hard to operationalize and can in fact lead to more fairness from an institutional perspective, because you don't, you know, you may run into a situation where you're not, you can't operationalize treating like cases like I'm not saying that that's correct, but that's the that I think is a driver for this very sort of formal approach. Yeah, I think, I think there are some ways and you can it's certainly not perfect like I have other work about how employers in the US employers use credit reports and making hiring decisions. And some companies, a lot of this happened at the back end so it's sort of all within the realm of like discretion and storytelling, but some companies do sort of screen up front, but they'll like write in rules like, you know, like in the credit data don't count collections that are coded as medical. So like they do write in some sorts of moral exceptions but I definitely take your, take your point that sort of like that level of discretion is is tough to scale up which is, which is one thing I try to point out in the beginning is like it's not incidental.  Like if you want credit scoring people are going to have to be cases like it's like a lot of the systems that that we know and love that are deeply flawed and imperfect like we wouldn't get to have it all. If you like we're always insisting on all I want to know the story of every single person. Anyway, thank you so much but I love the way you unpacked all the different ways we may look at the more substantive questions I think it's tremendously useful. Thank you so much for this conversation. So thank you so much. Thank you. Just on that last point again I keep thinking of the levels thing. The alternative that they've gone back to is individual teachers predicting students scores.  Because at the end of the day like I don't think that there's a perfect solution to this that we just need to like rationalize our way into like I think part of the point is we constantly need to be having very public fights about what fairness means because, you know, people have competing ideas and in my own head like I'll cycle through, you know, even about the same situation like I can see this I can see that so I think having it be be public. So to your point about procedure, it's actually for this paper about credit based insurance scores, I forget how many 10,000 pages which was not even like a drop in the bucket of what exists about public policy debate on this topic. But the only reason why it even happened is that in 1970 the US passed a law the Fair Credit Reporting Act that said if you give someone something different, a different loan, a different insurance policy, a different job or you deny them those things because of their credit data you have to tell them. So there was this mechanism that was asleep all those years waiting for this moment, so that when insurance companies started using credit scoring at scale, people started getting these letters in the mail saying Fair Credit Reporting Act, you know, notification your insurance rates are going up because of your credit score and those people called their insurance state insurance departments which had consumer complaint lines. So we also need consumer complaint lines broadly, and then it turned into a public policy issue like the issue itself was only visible because of all these sort of like pre existing procedures that that existed that like let the debate bubble up so in a way I think maybe like the point we are at history it's more important to build structures to enable debate than it is to have particular debates right now if that makes sense. Okay, so I'm going to go to one of the questions from the Q&A and I just I'm seeing a lot of good stuff going up in the chat we'll try and make sure that we copy the chat over to the Slack so we keep it after the broadcast closes. So a question from Moomin Malik in the US. So this sort of draws a little bit on some some things that you were asking you're talking about in your response to Sarita but maybe there's more to say. So he writes from what I see in machine learning. It's not just the people trained in case comparison reject narrative but they don't even recognize that narrative forms of organizing cognition are possible. Yeah, thoughts about how to make it intelligible, not necessarily through intellectual learning to learn that rhetorical techniques or even community building. Yeah, yeah, that's a great question. I think, I think there's something about like how different. Well, I don't want to knock professions like we live in sort of an era of a certain type of calculative rationality. So I think that I think I think people do if they sort of stop and reflect like on their own thinking they will quickly realize like oh I'm constantly morally reasoning in narrative as well just like with like a N of one. But I think that what's maybe difficult is sort of seeing that is like valid like a valid sort of evidence which I think is because it isn't just about like scientific professions that might be a little bit about that and certain types of knowledge you know being seen as you know legitimate to the exclusion of other sorts of knowledge. But, but I think it's also sort of the point at which we are in the evolution of our society that certain types of, you know, especially quantitative case based knowledge are sort of given more legitimacy in terms of like knowledge claims like I think there's a sociology of knowledge that's operating in the background as well. So yeah, in terms of getting people to realize that like I think I mean it might be interesting to start like just like through introspection because I think that it's it's really difficult to make decisions about what's right and it's like, like why, like why do we care about disparate acts with white people and black people like explain to me why that, and I would argue we care deeply as we should about that because of history, because of how events unfolded over time, because of the crimes that unfolded over time against this part of the population. Like why do we care about that particular type of inequality. In the first place I think relies on a narrative understanding of you know how these two groups have been treated in society over the centuries. Okay, so we're going to rotate back to philosophy now and get a question from clever. Thanks Barbara. I was wondering, given that I assume sometimes these different conceptions of fairness are going to be not mostly realizable, like how do we work out which ones we should be prioritizing and how do we trade them off against each other. Do you think that it's like the answer that will be because certain kinds of case studies like cases lend not certain times of problems lend themselves to particular types of thinking like different types of thinking are appropriate in terms of case comparison or narrative, or do you think that different kinds of fairness are going to be appropriate, and thus we need to divide the systems that prioritize the kind of thinking that aligns with that kind of fairness. Yeah, so this is definitely stepping far outside of my lane. I study how people, how ideas about fairness, especially market fairness get institutionalized, not so much like what's fair in this case. I, I'll like sound probably like a first year like PhD doctoral student I read this book by Michael Walzer about spheres of justice and that seems so smart to me. I don't I mean I think like a lot of like the like Hellenism bomb stuff is sort of inspired by that and the privacy, you know, like I don't, it, it feels right to me like intuitively that like different domains of social life run on their own, logics in lots of different ways. In, in sociologists, in sociology, there's this great but like unreadable book called on justification by these two French scholars, that sort of gets at the same, there's a whole like institutional logic literature that sort of gets at the same thing that, you know, like how you think and part of how you think and like what you value within the domain of like religion or the family or Christianity or capitalism or democracy or like fundamentally different and incompatible so I don't think I have a good, a good answer I sort of feel like that's why I always want to talk to philosophers is like I don't know how do you decide against like competing, you know, moral standards how do you know that this is a moment for, you know, equality and this is a moment for, you know, you know, how do you know that it's not like I can treat two people like really crappy ways that doesn't seem right, but I don't, I don't have I think like the, the language for articulating like when you would realize those, those distinctions. Yeah, I don't know that we philosophers do either we just don't let stop us. Okay, leshing from computer science. Thanks for a fascinating talk. Right, basically, in the distinction between cases and narrative, in general, from example the big cable you presented, right, so for us can work with data it seems that one of the distinctions is that cases can be seen as a form of data reduction, where narrative is basically all the relevant pieces of information combined. Um, I, yeah, I don't know if there is an useful sort of outlet from that and how we should do. And the other observation is, which was a parent your baby's example but also in the legislature in that there is another distinction outcome of how people deal with them, you said narrative elicit emotion, but cases don't. Oh, so treating something, I'm sorry treating something as cases is deliberately abstracting away from emotion, but narrative is sort of the opposite. Almost the goal is to elicit other people's emotional involvement so that they agree with you and work on the bill. Yeah. Yeah, I mean, I agree with that and it's interesting to hear you talk about cases is data reduction cases definitely reduce data, but you're still selecting detail and narrative like I don't think that the difference is like cases are. I mean, in a way it's like cases are thinner than narrative but it's just, you're selecting on different sorts of detail. I think that's what and then sometimes the detail works like the point about, like what's important in a story is often emergent, like in cases it's like well what do you want to know what you know what data do we collect you know it's very like let's start at the top and go to the bottom whereas narrative you often don't know to the end so I think there are differences beyond like the sorts of details that are important to pay attention to but that's definitely part of it but hearing you sort of talk about data reduction. This is maybe like a good example of how like they like a logic of case and narrative they they intertwine all the time like so for example. Like what about like genres in narrative, like this is like mystery you know this is romance this is man versus nature this is nature versus nature I mean that is an act of classification that is like taking narratives and putting them into cases. So I think that those are narratives you know hiding within sort of like a taxonomy that would be, you know, easy to like manipulate like through processes of case comparison so in a way it's maybe it's it's like the the level of detail and how you know what's important to pay attention to is what is what varies. Because I don't like I like cases, you know, like I'm not against cases I like having bureaucracies I like having statistics these are good useful things. So, yeah, I guess I want to try to not be. I don't want to make it seem like oh cases are just like lesser in some way, because I think they're actually more different. So, well, again the data reduction of language cases are explicit data reduction, but narrative. Yeah, Larry. Yes, I think so. Yeah. Thanks. Okay I think that we don't have quite time for a last question so I think we'll stop there. So before we, before I summarily into this broadcast and we will sort of disappear. Thank you so much to Barbara for a wonderful talking for fielding those questions and it was great to have questions coming from all of these different different angles, and you dealt with them all beautifully. And we're going to go over to the slack now. What I would love for Michael to do if you don't mind can you be able to copy the chat over and loads of resources thanks in particular to moment for all of the references that come up there. Thank you so much for your time too, but hopefully you'll go over to the slack and keep the discussion going there.


Quick question for you, which will set kind of the theme for this video, because if you resonate with the, I'm not enough, and what is this feeling of enough-ness? How do I even get there? Why am I feeling what I'm feeling? Let's go through that together today. Let's go through it. This is what this channel is all about. Let's take a topic. Let's break it down. Let's be human about it, okay? So try to answer this question. At what point in time in the future, what's scenario, envision one? Envision a time where you will finally be able to say to yourself, I'm enough. What does that point in time look like? What do you look like? Picture it. Close your eyes right now. Do you have a different body? Do you have more money, maybe a different job? Do you have more friends or a romantic partner? Yeah, maybe you live in a different city or somewhere else, or you're getting more sunshine, or you have a better relationship with your siblings or your parents, or you finally get that bicycle you always wanted, or the new skateboard. What are you thinking about? It's like when you can finally say I've arrived, you know what I mean? You're sitting on that park bench in the future and you're like, oh my gosh, am I ever enough? I did it. I really did it. Now you having that vision of you in the future, listen, listen, listen, where you can finally say that you're enough, that means today, while you watch this video right now, you can't accept the version, the person that you are right now until you get this, this, this, this, and this, then you're finally enough. You could only accept yourself the way you're going to be in that moment once you have all that.  You'll only accept that you're enough once all the achievements are checked off, once the boxes are checked. Doesn't that seem a little ridiculous? Can't you sense that? It's like, wow, that is kind of messed up.   I'm not enough.  And then the next heat comes in, right? And they smash the record. They said a new world record. And what is that person who just finished the race think? Well, I'm not enough anymore. That lasted about 13 minutes before the next race. It can't last like that.  The feeling that you have that you're not enough doesn't come from everything that you have in the material world. Things help for sure. But that's not what this is about today. Can I tell you a few stories? And I think they'll really help because they really help me. And listen. Oh, you all think I have this thing figured out, eh? Or maybe you don't. You're like, yeah, Scott's a mess like me. I got a lot of stuff figured out and I've learned a lot over the years, but it doesn't mean I don't have the most common thought in the history of humanity where these electrical impulses and neurons fire and somehow the neurons spell out in all our brains. I am not enough. Sometimes it's innate within us. That's something we deal with, man, because the world shows us how much we lack, doesn't it? Since the beginning of time, even since the advertising revolution in the forties and fifties, right? With the Industrial Revolution, you need the washing machine. You need the dryer. You need all of these different things to make your man happy because all these ads were really sent to women to make their man happy to be the garter and woman of the household. So, yeah, you need the dishwasher to be happy. You need the new iron. Here's the new toaster from GE. Here's the new coffee maker from Blibberd. Advertising today. The point of it is to say you lack this thing and without this thing, without this course, without this degree, without this piece of clothing or this new game system, this new technology, this new iPhone, you're not enough. You're not enough. And that's the advertising of material and objects. But the same is true for the soul and the spirit and the emotional quality of ourselves, too. What do you see on Instagram? You're not happy enough because look at who I am. Look at the business I've built. Look at I'm smiling all the time in my images. My life is perfect. You must lack something. People who are watching my videos. You must lack something. And I have the secret. I hold, listen, listen, listen, Scott St. Marie on YouTube right now. You watching this, I'm smiling. I got the white teeth. I hold something that you're lacking. I have the secret to your enoughness and you are going to pay me. And you're going to watch my videos in order to finally feel what I have and you don't, the contentment. And you can have it for six easy payments of $133 and 33 cents. That's what the world tells you. That's what Instagram tells you. That's a lot of what YouTube tells you. That's not it, man. That's so not it. If you've been struggling with the feeling of enoughness, can I put your shoulders at rest a little bit and put your mind at ease that it's not all out there and I don't have it for you. And nobody can sell it to you. Nobody can sell it to you. You can call it the new covenant and it's written on all of our hearts. And it's not for sale. GE can't sell it to you. Apple cannot sell it to you.  You know this from when you were a kid. Oh, the nostalgia of N64 and getting my first GameCube and Xbox and all of this stuff and these toys brought me so much joy and the memories are there. But the happiness fades. And the N64 wasn't as fun when I didn't share it with other people. You know the road I'm going down. So let me tell you a few stories, my friend. And I know this will click for you.  They can give me and it can give you. So where does the enoughness come from? What does it actually mean for me and what does it mean for you? So Scott, it can't be bought. It can't be sold. Okay, it's not in some version of me in the future. So I can't accept myself in the future if I don't accept myself now. Okay, you got it. We're getting somewhere. Cool. Scott, how do I accept myself now? What do I need to do? Who do I need to be? What thoughts do I need to change? You're complicating things, dude. You're complicating things. Miss, mister, the person who's listening. I got this out of my collection for you. For those listening on the podcast, it's Frank Sinatra's album. Nice and easy. One of the best songs he's ever written in my opinion here. I love the way he sings it. Look at his hands are behind his head. My brother said that's the way he wants if he doesn't get cremated. That's the way he wants to look in his coffin. He wants his legs crossed and his hands behind his back behind his head. That'd be hilarious. Nice and easy. The way you get to the enoughness, man, it's not all the effort you think it is. It's nice and easy. You were so present as a child because everything was novel and new, and it was easier to take that stimulus and be in awe about it. You didn't have to think about a million things. And what do we do today based on what we see online and the ads we see and what we're told? It's like, is this going to make me happy? Is this going to bring me peace? Is this going to bring me contentment? Should I go skiing? Is that going to make me happy? Should I go to my friend's house? Well, if I stay home, I get to be on the couch. I've always wanted to watch the thing on Netflix. So will they bring me actually more happiness?  And then that'll make me more happy. So I'm not going to go to that. They had the free wine, but they have the free beer. I like beer more than wine. So you know what, I'm going to say maybe to both because maybe I might want to see the backup, stay with Netflix. And maybe I'm going to go there. Friends, we're looking way too hard. We're thinking way too much about it. Does this, am I clicking with anybody? Cause I have that mind too. I'm like, what's going to make me the most content? How can I plan my present to make me more content in the future? How can I plan my evenings and days to be the most peaceful possible? I remember the Usher album, 87.01.  Where did that come from? Hold on, hold on. You know how these podcasts go?  Okay. We're getting to the center 87.01 Usher came out and it was cool because you had a DVD with the CD and you put in your computer. And this was like the first CD burner and DVD player we had on this PCU. Is it called an IPC Pentium 3 32 megabytes of RAM and you put in the DVD and boom, you don't have to call came up and boom, you remind me came up and it came with two videos on that CD, DVD set. You don't have to call. It's OK, girl, because I'm gonna be all right tonight. You don't have to call. And then a bit later, Ludacris comes into the scene and he does a remix for it. He does a remix.  But the lyric is you're looking for love in all the wrong places. But the way he says it is you're looking for love in all the wrong places. And we're gaining wisdom from Ludacris in this episode. You're looking for love in all the wrong places. Maybe we are in the things we're sold in the things we're told. What we're told we should be. You should do that in order to get the happiness, right? You should do that in order to get the enoughness and the contentment. You're looking for love in all the wrong places. My friends, we think and I thought and sometimes it tricks me that I need to achieve in order to fill the cup of enoughness. I need to travel more. I need to skydive. I need to go to Burning Man. I need to rock, climb in Greece. I was talking to friends yesterday about Burning Man. I'm like, yeah, yeah, yeah, add that to the list because that will make me better because I'll have more experiences.  Are they more enough than you are? Is their soul larger than yours? Do they have more value than you do? Hell no. That's not the way you were created. That's not the soul that was put into you. It's not smaller than anybody else. Absolutely not. So why do you think they're more enough than you are? They've done more. They have more.  I'm taking the thought right from you. I'm spitting it out in my room here. I'm taking the thought there. It's gone. It's absolutely gone. Remember the simplicity. It's not in the future. It's in the now. Let me tell you two stories here. And I know you're going to resonate with them. And then we're going to have some homework for you for this week. And I know you're going to love it and it's going to be super helpful to you. And you can even meet me this Saturday if you want online. And you can chat with me there. I'll tell you about in a bit. Oh, I feel so good today. I feel so good today. And I feel so grateful to be able to speak with you about the things that I've been thinking of and how it's helped me a lot. So here's one. My friend Joel. We've known each other since we were two years old. We've been through everything together when I was six, seven, eight, nine, 10, 11, 12, you know, the sleepover started, right? We had sleepovers. We, we went to school together. We'd walk home from school together. I have three older brothers. He's my fourth brother. Text him like every day. And we hadn't seen each other in a while. So we met up last week. But all through growing up, but especially when we were 13, 14, 15, 16, 17, all through high school and even through university. When I graduated university, moved back home. He did too. So we were back in the hometown together. And every single time, since I was 13 years old, when I walked through the door of Joel's house, his mom would be there. His dad would be there. The smell of a friend's house, you know, you can be blind. You can walk in blind. You know that it's their house just by the smell, just by the energy and the feeling you get. His dad would sometimes be in the family room. And he'd be watching the Jays game or something on the weekend. And right when I walk in, his mom said, Scott, the kettle's on. You want some tea? And I'd be like, obviously orange pico every time. A little bit of milk, a little bit of milk. Can you imagine it for yourself? Would that bring you safety, peace and contentment? Let me keep going. So Joel and I have our cups of tea, man. We're just young, but we love talking about life. And every time we grab the cup of tea, head into the family room, talk to his mom, talk to his dad. I'm always in love with older people. I get better, get along more with older people than people my age. So I'm just loving asking them questions. His dad's a priest. So I'm asking him questions about religion at such an early age. And we always head to the basement with our teas and we play some pool. And it was a snooker table. And we just had the regular size pool ball. So the pockets, right in a snooker table, so narrow.  We're playing with the big one. So we got really good at pool because you have to be so accurate. One year after another, after another, after another.  This is part of my memory, part of who I am to this day. And can you recall memories like that that brought you to today? So I hadn't seen Joel in a while. I haven't seen his mom in a while. And I go to his mom's new place downtown Toronto. They sold the place and there's that that that home is still in my hometown. My parents lived there. So when I visit, go for a walk around the block and I pass Joel's house. And I see new people in the kitchen right where that kettle used to boil. Right when his mom would ask me that the kettle's on. Do you want some tea? And I wonder if that family's making tea in that kitchen because my memories are in there, but the memories are in the soul now, too. So last week I go over to his mom's house and I walk in the door and Joel's there. I haven't seen his mom in about three years. She looks the same. And I walk through the door and what do you think she says? Say it out loud, my friends. Yeah. Scott. Good to see you. The kettle's on. Do you want some tea? I'm 34 now. No time passes. We sit on the couch. We chat like nothing's changed. And in that moment, my friends, in that moment, in those moments sharing time, do you think I went to Joel's thinking of, will this make me enough? There's probably something better to do. Oh, I hope that she asked me about tea and that we can sit and there's milk in the tea and it's going to be like old times. I didn't have that thought at all. He just go expect nothing. Keep expectations at a minimum. You go and see what happens. And I promise you when you keep those expectations at a distance and you just do good things for other people, things that feel right to you, your cup of enoughness just happens. I promise you, it just happens. A lot of you try too hard. You don't need to try so hard. I admire you for trying hard. I admire you for fighting. I admire your courage to get up and figure this stuff out. And that doesn't mean there's nothing to figure out talking to someone about it. You know, if you're in a deep slump, you know what you got to do. But for the people that there's like an urge and a stickiness and a prickliness and a sense that I just don't feel it, there's always an emptiness. There's always a longing. I know that I know that longing and you think that there's a place on earth where you'll finally feel that enoughness and it's just here, man, you don't have to travel somewhere to get it. It's right here right now. And it'll hit you when you least expect it. So one more share and let me see if you kind of feel this one. And there's a constant theme and I hope it's kind of ringing. And these threads are connecting.  And I'll have more memories and therefore I'll be more of a person and therefore more, more enoughness will be filled my cup. I thought about this with my parents as they're getting older and I'm sure yours are. And if they've passed or they're still around, you may be able to definitely feel this is that you think the times with your parents and what you remember most would be the trips you went on, the big moments, the big conversations. Right. The real emotional times that you had. And sometimes yes, absolutely. But then what takes up more space surprisingly to me is what I'll remember most and what I do remember most with my parents, especially the last few years is is sitting with them in the family room watching Jeopardy. That's it. Nothing crazy. Nothing out of this world spectacular simplicity, like putting the kettle on and having tea and good conversation and enjoying a show with two people, my parents.  And I felt so much enoughness like I was enough in those moments because I wasn't chasing anything because I wasn't chasing a version of myself that I had to create in order to accept myself as I am. My parents accept myself as I am. Therefore, I'm accepting myself as I am as I present myself to the world in this moment to you in downtown Toronto. Right now, as I go to fresh co and get groceries, as I run up to people on the street, I present to them as best I can. Just Scott, sometimes I put up a front. Like I got to be happy, like I got to be this guy. And you catch yourself and you practice this over time. I don't have to be anybody else. Right? When you feel like you got to be somebody else, it's like you have someone else's cup and you can't feel somebody else's like that. You can fill your cup of enoughness. My friends, the moments where you feel safe and you feel content are the spaces and places you will feel like enough. It's not complicated. I want it to be complicated because then I could sell you something really complicated, but it's not. It's pretty simple. So your homework this week, right this down, right this down. This is all about being human and what we learn as we go on through our days. I'm practicing breathing more as I speak. So please excuse the deep breaths. Your homework for this week, if you want it, is just think about times in your past this week, last year, the last 30 years, the last 20 years, where you felt like enough, where you felt safe and peaceful, where you arrived in a moment. Think about those. How did you feel? Where were you? Who were you with? What were you doing? OK, that's step one, step two to feel like what you need to to feel like enough. If you felt that. And then you were pulled out of it. This happens, you're pulled out of that enoughness. What pulled you out of that? Because I get I know the I know the reasons for myself. What pulls me out of that is pulling me out of the moment. Like, oh, I'm not thinking of the consequences here. Right. Oh, I got to think about something else. Oh, I got to plan the future. Oh, am I standing straight? Oh, what are they thinking about me? Oh, the the the rumination comes in. So if you have the capacity or the memory to think about step two, what pulled you out of that? I would love for you to try that. I think it would really give you some insight into, oh, my gosh, this is where I feel safe. This is where I feel loved. And that'll help you look for love in all the white places and feel like enough. Thanks, Ludacris. Thank you, everyone, for watching. The link to Patreon is always in the description. We meet once a month. We meditate together. We share things this week.  And I want to tell you something that I feel is important. And I reiterate this in a few I will in a few podcast episodes and YouTube videos. But I love to teach. I see myself as an educator using what I read, what I experience the day to day and to give you a new perspective and things that I learn. I love that.  I wonder if that can be a thing like comedians do this, right? Oh, that happened to me. I wonder if that's a thing. I wonder if that can be a joke. I wonder if that can be a bit. And I think about, I wonder if that can be like taught. I wonder if that can help someone around the world feel good about themselves. And maybe they're in a bind and this will kind of release them from a bit of tension. I love doing that. And I do it for you and I do it for me. I'm an educator. It doesn't mean I'm perfect. As a teacher, right, father, please take this cup from me. I suffer the same. Man, do I suffer the same. And I love how we're in this life thing together. I really do. This is the Being Human podcast, not the Being Perfect channel. This is the Being You channel. And I would love it if you wrote a comment, maybe you wrote a review or put a star on the the Spotify or iTunes. And again, I'd love to meet all of you. Hopefully we do some meetups and stay tuned one video a week this year, maybe more. So watch out. They're coming and I'm looking forward to engaging with all of you. Take care. Have a great week. Enjoy your homework and have some fun reflecting. And I'll see you next Monday. Bye. Oh, check out this. Does it sound like a motorcycle? No, wait, it's all right. All right, see you.


Hey guys, it's Ashley again and I wanted to come back on and showcase my favorite probably of all time, Back to the Future. Stay tuned. If you like this video, hit the subscribe button and throw us some comments. Stay tuned. Hey guys, welcome back to Comic-Ed and TV where all geek culture collies, if you're new to the channel, don't forget to hit that subscribe button so you don't miss out on future videos. Okay guys, here we have the first one, which is Doc Brown in his suit from the beginning of the first Back to the Future when he got ready to send Einstein back. He's very cool. He's got the orange and he's got his little trigger button, his little tool belt. I don't know if you can see his tool belt very good. There it is. Very cool detail on the tool belt. We like him a lot. Then we have Doc Brown from the 1955 when he went back and he had to do the spark to get Marty back. This is the Loot Crate Exclusive, which is very cool. You can see the battery on the back. He's very, I like his goggles, they're very intricate and you can see a scar on his head, which is where he got the idea for the flux capacitor. Just a minute and we'll have the awesome car for you. Okay guys, my favorite piece here, I'm not kidding. We found this at C2E2 last year. The only one we've seen in all the Funkos and everything that we looked at and as soon as I've seen it, I was like, we're getting it, I don't even care. As you can see, he's very detailed on the car. Out of time. I don't know if you can see that. There it is. There's the hole outside of him. Sorry. There's all that. It's very cool. Marty comes out pretty easy. He's pretty cool looking. Very to detail of what he's supposed to look like. We wanted to mention that this is very hollow, which is a very big surprise, but it's really cool and my husband informed me it's a ride, not an actual Funko. I mean it's a Funko, but you know what we mean. It's a Funko ride, not a Funko pop. Right, Funko ride, not a Funko pop. Because the driver comes out of it. Yeah. Anyway, this is my favorite collection. We're very proud of this collection. Here they all of them together. Get in there. Very cool guys. I hope you enjoyed the show. Hit the subscribe, hit the like, and stay tuned for more videos. Thanks. Bye.


Good morning. Welcome to CMC markets on Friday the 18th of August and this quick look at the week ahead beginning the 20th 1st of August 23 It's not been a good week for the footsie 100 Five looking on course for five days of declines on top of a for on top of a poor finish To the end of last week With the index down over 4% Over the last five days down at five week lows and More broadly while the performance of the DAX has been slightly better the narrative for this week has been pretty much a case of Stock market weakness So what's caused it? Well, there's a number of factors at play concerns about the health of the Chinese economy The the the banking system the property sector country garden Potentially restructuring There you've also got Evergrand filing for chapter 15 Bankruptcy Protection in New York And a missed coupon payment or a missed payment from Zhongji, which is a Chinese wealth trust fund which suggests that perhaps it has significant exposure to the Chinese real estate market, so There's there's an awful lot of concern about the growth prospects for the Chinese economy So more disappointing economic data early this week in the form of retail sales industrial production all coming in well below Expectations unemployment rate in China ticked higher and they didn't publish a number for youth Unemployment which at the last count was around about twenty point nine percent So that's probably gone up in Chinese youth unemployment Measures the unemployment rates of those between sixteen and twenty four So we've got Weakness coming out of China That's a double-edged sword to some extent because ultimately I think if there was a big China rebound Then you could well see oil prices Starting to head back to was $90 a barrel the fact that we're getting a weak recovery out of China Could well temper some of the upward pressure that we're seeing in inflation Over here in Europe as well as the US. We've also seen Higher bond yields this week. That's no better illustrated than in these particular charts here This for example is the UK 10-year guilt yield Which is at its highest level Since 2008 what's been notable? I think recently About the rise in yields that we've been seeing on the bond market as it has been the fact that we've been seeing a Significant increase in the longer end of The guilt curve as opposed to the shorter end But that's not to say that the shorter end isn't rising to it is we've got UK two-year guilt yields here On the up over the course of the last five days. We're seeing a little bit of a pullback now That's largely as a consequence of this week's Wages unemployment and inflation numbers today. We got UK retail sales, which were disappointing Coming in much worse than expected But that's also got to be put in the context of the fact of how they're measured Measured very much on a volume basis If you actually look at the value basis of UK retail sales, they're actually up So essentially what's happening is because consumers having to pay higher prices and having to pay more to get less If we looked at the July retail sales numbers in terms of spending Barclay card sort of big 15.8% rise in spending in July Largely on the back of entertainment expenses, so Stuff like Cinema visits music venues live events including Taylor Swift Taylor Swift concerts also saw big movie releases during July and the Anna Jones and the Dala destiny mission impossible dead reckoning Barbie and Oppenheimer so there was no Barbie bounce in retail sales, even if spending Was higher than the previous years Consequently that disappointing retail sales number has prompted a little bit of a slide in Guilt yields though. I would argue that that could well be just a consequence of a little bit of haven demand for bonds today On the back of the weakness that we're seeing in equity markets if we look at say for example US to year yields, you'll probably see a similar sort of trend taking place Just bring that up here. There we go Though we've got and retested that 5% level on the two year pulled back a little bit if we go US 10 year We can see that even though we've seen a move higher in two year And we're seeing a little bit of a pullback again today in terms of yields. If you look at the 10 year We're back at levels last seen in 2008 So again seeing a little bit of a pullback after some strong moves higher in yields and the same applies to The German German 2 and 10 year as well And I think that speaks to a slight change. I think in emphasis when it comes to Interest rates. I think for so long The debate has been about high How high Interest rates would be likely to go and I certainly think there is potential For more rate hikes, particularly from the Bank of England, even though those retail sales numbers were disappointing We're still pretty much priced for a 25 basis points rate hike by the Bank of England at their September meeting although I would caveat that with The fact that we still have another CPR report And a wages and unemployment report between now and then but certainly the wages data that we saw this week 7.8% 8.2% if you include bonuses Obviously an awful lot of that bonus number was as a consequence of an NHS and public sector bonus Which is very much a one-off and is unlikely to be repeated but certainly 7.8% wages Coming against a backdrop of an inflation rate that in June was at 7.9 Means that we are slowly starting to claw back That's a real term real time real earnings real earnings drop that basically has been that has hit UK consumers over the course of the past 18 months We are now potentially starting to turn positive in terms of wages Relative to inflation and I'm not necessarily I don't necessarily think that is a bad thing It's certainly not a wage price spiral Which is what some people on the Bank of England are suggesting it might be what's happening is that wages are slowly starting to catch up to the 15 20 percent increase in food prices and other prices That have been squeezing consumer incomes over the past 18 months that for me is not a reason to hike interest rates aggressively further ultimately we still have quite a bit of lag to come through in terms of tightening in terms of Consumers rolling off the fixed rate mortgages and going on to new much higher fixed rates And I don't think that we've seen even the beginnings of that start to Take effect and that is likely to be a potential drag going forward on the plus side however Savings rates have been edging higher over the course of the last six months So if people have been on a fixed rate mortgage, they will certainly have been getting a higher return if they do have any savings And inflation has been slowing and if you shop around You can certainly I think get much lower prices relative to what food price inflation is currently trending at at 12.4% Particularly if you've got a loyalty card or what have you so I would imagine that in some respects you are already seeing price cuts on Staple goods like milk eggs and what have you looking forward It's going to be a fairly well it's shaping up To be a fairly light macro week coming up. We've got the Jackson Hole annual symposium, which starts on the 24th This week's This week's Fed minutes just gone didn't really Add to the add to the sum of overall knowledge When it comes to what the Fed is likely to do in September The bias I think for me is still erring towards another pause a hold if you like, um, I think one of the Things that stood out from this week's minutes that there was only two US policymakers who favored a hold or erred towards holding rates in july I would have expected it to have been more than that and essentially I think that's what also helped push rates higher, but certainly in terms of the framing of the debate um around How high interest rates are likely to go I think people are now more concerned about not by how much further they have to go but how much How long are they likely to stay at current levels? And I think that's why you've seen rates at the longer end Start to go higher because I think there was a perception Which has now started to shift the Once we'd hit peak rates we'd see rate cuts quite quickly That no longer looks to be the case However, obviously that doesn't That doesn't include the scenario whereby china exports deflation Because of an economic slowdown There so I think we need to obviously keep that in the back pocket We've already seen ppi in the uk Head into negative territory in the july numbers And I think the bigger concern going forward It's not so much about headline cpi. It's about really about how resilient core cpi is And that remains steady in july at around about 6.8 percent But certainly I think on the headline numbers We should start to see headline inflation head back towards 5% by the end of this year Which would obviously be in line with the government's target, but ultimately That has absolutely nothing to do with anything the government's done It's just basically that will that should happen organically. So it's an it's really it's really a fairly easy claim for The government to make because ultimately inflation should Should come back to around about 5% by the end of the year anyway Um But anyway looking looking ahead to the looking ahead to the the jackson hall symposium Um, I think the fed's going to be fairly comfortable with the idea Of being data dependent. I don't think That we're going to hear a victory lap from pal I think what we've seen from a number of fed policy makers in recent weeks is that There are increasing splits and there is some concern about the effects that the current rate hikes Have had and will have over the course of the next few months And I think similarly there are concerns on the bank of england monetary policy committee Leaning in a similar direction. So I think while we can sort of speculate About where the peak rate is for the uk And the markets have still got 6% seriously. I I really do doubt that But that's that being said I wouldn't rule it out either But certainly I think we're going to get 25 basis points in september. I think the bar is very high for us not to get that But as we look ahead to next week, I think the main focus will obviously be on jackson hall We do have flash PMIs Which are due out on the 23rd for august And we already know that manufacturing has been very very weak Um on the manufacture on on pretty much across the board for france Germany the uk as well I mean the germany pmi for july came in from manufacturing pmi came in at 38.8 I mean, that's the worst level since the covet locked down in 2020. Yes just over a three and a half year low so Germany is pretty much the sick man of europe when it comes to its economy Luck could well see a third quarter of economic contraction there France was in negative territory for both services Or contraction charity below 50 in other words For manufacturing and services being twisted to see whether or not that remains the case uk PMIs have been slightly more resilient Saw 51.5 in july for services 45.3 for Manufacturing we've also got the german ifo survey as well which Saw the business climate fall to its lowest level since october last year in july at 87.3 so not expecting to see much in the way of optimistic data In the silly season of august or the summer season Because I think you'll find that an awful lot of businesses may well shut down for For holiday for for that for that particular period So before we go on to the earnings numbers, let's have a quick look at some of the key support levels We've been talking at as I say the footsie 100. Is that a bit of a shocker? On course for its worst run of declines On a daily basis since october last year In the wake of the quasi-quarteng budget here are the six declines here one two All the way back here So six six daily declines there On course for a similar sort of run through here the big level On the footsie 100 7200 Right there between That low there and that series of lows through there so Coming up to a very key support level on the footsie 100 similarly V-dex Also the 200 day moving average But the lows back in july so that's around about 15 1,400 also the 200 day moving average So that's going to be a very very key support level over the course Of the next few days We have already seen significant breakouts On the s and p 500 and the nasdaq. We've both seen them break below Their 50 day moving averages But we've also seen them break below key support areas For the s and p 500 Broken below 43 80 This this series of lows through here and also below the 50 day moving average So momentum is definitely stalling there Be interested to see whether or not That Is maintained And we see further declines, but certainly the move higher in yields Is not helping The nasdaq and it's not helping the s and p Fortunately, we are still in the uptrend on the nasdaq that we've been in since the start of this year So there is certainly potential for further weakness And a test of this key support area back in june Which is 14 200 there or thereabouts So approaching some key support levels on the nasdaq But the fact that we've broken below the 50 day moving average on both Would appear to suggest that we could well be on course For further declines and perhaps us markets Need to be we need to be thinking more in terms of Sell the rally Then buy the dip But we'll have to see how that plays out over the course of the next few sessions The dollar's also seen a fairly decent bid On the back of the recent Concerns about higher yields for longer But also concerns about a broader slowdown On an economic activity more broadly Euro dollar Looks as if it's going to be heading back to support At on on that trend line that I've drawn in there But as well as a 200 day moving average at around around about 107 30 at the moment We're holding above 108 30 so that's these two lows through here and That support just below the lows that we saw yesterday. So 108 30 Perhaps headings of towards the 200 day moving average and that trend line support from the lows back in March But i'm actually quite surprised that euro dollar isn't weaker Given the weakness that we're seeing in the data And I think some of the data that we're seeing out of europe has prompted Some on the governing council Um to perhaps reassess where their terminal rate actually is Um, not in kazakhs not in kazakhs. I believe is the latvian ECB Governing council member who's previously fairly hawkish Um, this suggested that perhaps Um more rate hikes May not be the answer and perhaps they need to basically wait and see what transpires um over the course of the next few months And perhaps they shouldn't push their luck on pushing for more aggressive tightening going forward pound however As a slight has had a slightly more positive week. Yeah, we're seeing a little bit of weakness today Um, that's not surprising come off the back of four days Of fairly these five days of gains We've held that key support area Through those lows from the 30th of june Which currently comes in around about 126 126 30 So if we get a break below 126 Then that could well complete the potential Head and shoulders reversal perhaps left shoulder here head here But of a messy right shoulder here But if we do break below then we could head back towards the 200 day moving average there But at the moment there's a fairly decent area of trend line support through there And while that holds We need to see a break back above the 50 day moving average, which we're currently struggling to get back through In the short to medium term Euro sterling So quickly look at that Back at the lows back in july. This is a range trade. This is a range trade all day every day Fairly decent support and around 85 resistance at 87 pays your money. It takes your choice There's not really much going on there And dolly n is A currency pair that i've really got massively wrong I really thought that with the bank of japan looking to pivot on its monetary policy That would be beneficial for the n it could well be it could well be but at the moment Any dollar shorts are getting squeezed and at the moment we still i'm still not convinced That um, we can't potentially go back higher towards 147 115 The bank of the bank of japan has shown no inclination that it wants to intervene And the last time it intervened it intervened at around 145 146 where we're there now And we haven't seen much evidence of that thus far However, if equity markets do go full risk off that should be positive for the yen And push the yen higher and the dollar lower will have to wait and see whether or not That transpires, but japanese inflation is still at 4.3 percent and starting to look a bit sticky It remains to be seen how long the bank of japan can afford to ignore that um in terms of the earnings numbers We've got harbour energy Obviously there all their profits got wiped out by the windfall tax um last year They there was some speculation earlier this year that they were in talks With a us oil company called talos Energy, I think it was talos energy us based talos energy um They're already partnered with talos in an oil and gas field in the gulf of mexico And this move if it does play out could well see harbour energy um potentially Drop any future investment in the uk and just carry on with basically Refocus its efforts elsewhere and to be quite honestly who can blame them If we look at the key support and resistance area on this particular chart We can see that 270 is a key resistance level I'd be interested to see whether or not harbour energy is able to generate any semblance of profits from It's largely domestic oil and gas producing 90 percent of his production takes place through five key hubs here in the uk Obviously gas prices are lower oil prices are lower So revenues are also likely to be lower um last but not least We've got invidia That's been a big AI play artificial intelligence play still very much In the up friend that it's been in pretty much since the start of This year It's due to announce its second quarter earnings on the 23rd of august As a quick recap back in may the shares got a significant lift After surpassing expectations on first quarter revenues coming in at 7.2 billion dollars Raised its revenue guidance for q2 To record breaking 11 billion dollars now That's a huge increase on its q2 numbers of previous years or any other quarter. It would be a record quarter The improvement is expected to be driven by A big increase in sales of data center chips along with investments in artificial intelligence In q1 data center chips alone generated 4.28 billion dollars Followed by gaming which generated 2.24 billion dollars The q2 data center revenue is expected to come in at 7.78 billion dollars Which is more than the entire revenues that they generated in q1 Gaming expected to remain fairly flat at around about 2.4 billion dollars With profits expected to come in at two dollars and seven cents a share Um Nvidia Nvidia CEO jensen huang um laid out a statement of intent In june announcing a raft of new ai related products which help which could help shape Our the company's network and advertise. So there's potentially huge growth prospects there The bigger question is is whether or not all of that is priced in and whether or not their q3 guidance Um, we'll be slightly more modest. We've also got numbers from q2 numbers from snowflake As well and zoom video on the 21st Um, so that's I think I think I pretty much covered everything that I need to Um cover for this week Once again, thank you for listening ladies and gentlemen. I hope you all have a great weekend And speak to you all same time same place next week. Thanks very much for listening and have a nice weekend