So my name is Ethel Azar, I'm the, I'm the project leader with Humanising Machine Intelligence Crank Challenge and I'm delighted to welcome you to the third H&I Day seminar. I'd like to start by acknowledging the fact that I'm, we live and work on, well, I live and work on Narago lands and I'd like to pay my respects to Elders past, present and future and acknowledge the traditional custodians of land throughout Australia. So today we have Ria de Atiago who has recently finished a PhD at the Carnegie Mellon and who's just started a job at the University of Texas at Austin as an assistant professor in the Information Risk and Operation Management School. So Maria is going to be talking about semantic representations. Maria, as we've only got an hour, it'd be great if we get started. We'll do the usual about half an hour for a talk then we'll have the Q and A and then we'll shift over to the slack. So try to go ahead and share your slides, that'd be great. Thank you so much for having me. I'll share my slides now. You should be seeing my slides. Yes. Yeah, perfect. Okay, well, thanks everyone for the invitation. It's great to be here, especially following the two fantastic talks by Claire and Lili. And today I'll be telling you about my work on semantic representation bias. This is work that was done in collaboration with people at Microsoft Research and at LinkedIn. In recent years, we have seen an increase in the use of machine learning to assist experts in high-stakes decisions. Examples of this include healthcare, child abuse hotlines, hiring decisions. And a lot of this work has been motivated by research that showed that machines were better than humans at making predictions. But these results often make simplifying assumptions about the task in hand and the data available. And in deployment context, several risks have been highlighted. In her book, Automating and Equality, Virginia events discuss how predictive models can further marginalize communities that have been disadvantaged in the past. And two examples of these that we have seen in the media include Amazon's automated recruiting tool, which was scrapped after showing bias against women. And what was happening here was presumably that the algorithm had been trained with data of historical hiring patterns. And so bias says that discriminated women were being reproduced. And in another piece, we saw how on a healthcare algorithm, Overmiguer and his co-authors published a paper showing how the medical algorithm was showing bias against blacks. And the nature of the problem here was a little bit different, stemmed from omitted pay of bias in which the label that was being optimized for did not match the label that they cared about perfectly. And that led to the bias. So they were using medical costs as a proxy for medical conditions. And one of my research focuses on the question at the center of my work is what are the risks and the opportunities of using machine learning for decision support? And as part of this question, I consider the nature of the labels available for training, including issues of omitted pay of bias, the selected labels problem. I also consider issues of human-computer interaction components that affect how predictions ultimately impact decisions. But the part that I'll be focusing on on today's talk is the data representation and the machine prediction. And in particular, how societal biases can be reproduced and amplified in this stage of the pipeline. So one domain in particular where we've seen a drastic increase in the use of machine learning is human resources where machine learning is being used as part of hiring and recruiting tools. And here, the question that drove our research was, can we quantify the risks of gender bias in automated recruiting? And can we mitigate these bias? And as I mentioned before, this was research that I did. Well, I started while I was in training at Microsoft Research. And it was also in collaboration with researchers at LinkedIn. And to summarize the findings that we'll be discussing today, our first compounding gender imbalance effect in a large-scale study that we conducted. And here, we show a general result of how this compounding effect can happen in supervised learning. And moreover, we propose methodology to leverage biases present in word embeddings to mitigate biases without access to protected attributes. So let's assume if we had a machine learning algorithm that was going to help us recruit, what would we like that to look like? So for example, if we're a recruiter who's looking for a computer programmer, we may want an algorithm that is able to pick up on the fact that a candidate is a software engineer and has experience in Java and Python and so on. Of course, we wouldn't, for example, want the fact that their name is Jane to affect the probability that we identify this person as a computer programmer. The challenge here is that even if we obfuscate information that explicitly allows us to infer these, there are other elements of the information that is included often on structure form that can leave the algorithm to infer information about the demographics.  And we would not want this fact to influence the probability that we see this person as a potential candidate. Increasingly, our online professional presence has become more and more important and recruiters invest vast amounts of time navigating through our personal websites and so on to determine what are our patients, what are our qualifications and who they should target. And presumably, this is a task that could be made easier with machine learning. These, by the way, are all of the co-authors of this work. And so what we said was, well, in this unstructured data scenario, what would be, because it's not really easy to access data of one of the companies that are actually using for recruiting, we wanted to construct a data set that would allow us to simulate what would be the risks of deploying a machine learning algorithm to do something like determining who to target with a job advertisement based on their occupation. So to study these, we collected 400,000 online biographies through Common Crawl. And the structure of the biographies were biographies written in third person. So the pronouns allowed us to extract gender. Here, it's important to note the limitation that by extracting the gender through pronouns, these results in a simplified binary notion of gender that fails to capture important aspects of gender and the races and the represents part of what we want to study. Here, so we're using the third person pronouns to extract the gender. We have the title from the Bureau of Labor Statistics. So in this biography, for example, what we would want to do is to, from the text that is highlighted in orange, we want to predict these occupations. So we want from text in orange to predict that this person is a professor. So the machine learning task that we end up having is a classification problem where we want to classify the title out of 28 potential categories from the text in the biography. And the pipeline then is we have as input data, the biographies. We have, we've scored different semantic representations.  And this is because we wanted to understand how different complexities of the algorithm would play out the bias differently. And in particular, there's a very big difference between using a bar of words and a word embedding. And it's that the word embedding is also importing bias from other sources, right? The word embedding has been pre-trained in another corpus and so there's a potential of another source of bias. And the objective is to predict the occupation. So this was my biography at the time. And so if we fill my biography into the deep neural network, it predicts that I'm a teacher. And this is a network with attention. So the words that are highlighted in red are the ones that it pays the most attention to. And if motivated by the research of Bertrand and Lenathan who studied differences in callback rates when names were buried in CVs so that the race that was inferred would be different. And they studied how callback rates when DCVs were sent out of deferred. We wanted to see how the algorithms prediction would vary if we change gender indicators. And here an important thing to notice that this is not a counterfactual analysis of what would have happened if the person changed gender in which case like all of the features would change is just looking at the effect of the gender pronouns by themselves. And so in my case, for example, if I were using male gender pronouns it would predict that I'm a software engineer. And this is something that we saw at scale. So here what we're seeing are the most common swaps in which the algorithm gets the classification correctly if we used opposite gender pronouns but got it incorrectly with the gender pronouns that were used that were originally in the biography. The reason why we wanted to do these analysis in particular is because there's a hypothesis of saying, well, maybe it's just that women professors are just writing biographies that are impossible to infer that there are professors. So the question there is, well, if they were just using different gender indicators how would this change? And these are the patterns that we're seeing here. Of course, looking just at gender pronouns, first of all, well, maybe the answer is we should just scrub the gender pronouns, right? And then we get rid of the problem. So we want to look at the bias beyond gender pronouns and look at what is happening. And here what we're looking at is in the X axis we have the percentage of women in the occupation in the data that was used for training. So for example, the way to interpret this is surgeon, for example, here is approximately 15% women in the data set. And in the Y axis, we're looking at the gap in accuracy. So if there was no gaps in accuracy and the algorithm was equally accurate for both men and women across all occupations, what we should be seeing is that these all lie on a horizontal line. What we're seeing instead is that there's a correlation. And what this correlation shows us is that the algorithm is more accurate for women in occupations that are predominantly female, more accurate for men in occupations that are predominantly male. And this is not surprising from a statistical perspective, of course, the algorithm is just inferring patterns and saying, well, if I learned that women are more likely to be nurses and surgeons and I infer that these biographies of a woman, I'm gonna say it's more likely that they're a nurse, for example. So the question is, why does this matter? And from all of the metrics that we could be considering as algorithmic furnace metrics, why is this one one that is concerning? And here, and this is not the formal formulation of the theorem, but in the paper you can find the formal formulation. If the fraction of women in occupation is less than 50% then and the gender gap is negative, meaning the algorithm is more accurate for men than for women in that occupation or in that class, this is a general result, not just for this domain, then their fraction of female in the true positives in those that are correctly identified by the algorithm as being part of that class will be less than P, less than it was in the initial data set. And what this means, this is something that we can connect to the notion of compounding injustice in political philosophy. So what is happening here that if the initial imbalance constitutes an injustice, then what the model's prediction is doing is the model prediction is informed by and compounds these previous injustice. And so let's look at what this compounding in balance effect looks like exactly. So as I mentioned before, in our algorithm approximately 15%, a little bit less than 15% of surgeons are women and the algorithm identifies 71% of male surgeons correctly as surgeons, while only 54% of female surgeons are surgeons. And so as a result among true positives, the percentage of women drops to 11.6%. And so again, why does this matter? Well, if we were using these for a task such as determining who to target with an ad for a job, then what this would mean is that those that were targeting correctly, so those for whom the ad is actually relevant have a further underrepresentation of the group that was already underrepresented in that occupation. And this is something that we find across the different representations and both with and without the gender pronouns. So what we're looking at in these plots is the same plot that I showed before ex-access percentage of women in the occupation, why access the gap in accuracy, the gender gap. And what we're seeing here in blue is what happens when we use all of the biographies, so including gender pronouns and so on. And in green, we see what happens when we drop explicit gender indicators. So he, she, Mr. Miss, and so on. And there's two key things to take away from these results. The first one is that if you look at that curiosity plots we see that we actually have substantial gains in terms of dropping the compounding in balance effect by removing these explicit gender indicators without having lost in accuracy. And that is a topic that is a theme that we'll come back to later in the talk. But additionally we see that this compounding effect remains and so this is something, so all of the details if you want to look more are in the paper including some simulation in which we discuss how if the algorithm is encountered multiple times, then you end up having this phenomenon of kind of like a leaky pipeline in which at each iteration of encountering an algorithm that has this property then a group becomes further underrepresented. But this phenomenon we can relate it to what happens in orchestra auditions. So in orchestra auditions at some point add some way to trying to mitigate the biases that played into the judges assessment of musicians. They said, well, maybe we can ask musicians to play behind a curtain and that way the judges won't be seeing the musician and they'll just be judging the music. And what they found was that this response was insufficient to counter gender bias because they were still able to hear the shoes of the people playing. And so we can see these as analogous of the algorithm even though we can drop these explicit gender indicators the algorithm is still able to kind of hear the shoes and infer these information.  So the question then becomes can we mitigate the compounding balance effect in a way that doesn't assume access to protected attributes? And this is important because these are attributes that are often unavailable or illegal to use under certain jurisdictions for certain applications regardless of whether we're trying to incorporate these attributes in the data for the purpose of mitigating biases. And additional, we want to account for intersectionality and here we want to account for two things. First of all, the phenomenon that we analyze on gender is something that we were able to analyze in this data set by using these proxy of extracting gender indicators but this phenomenon and what is giving rise to this compounding effect is likely to be happening on other demographics that we cannot test for like for example, rates. And in addition to this intersection of these attributes and how groups at the intersection may be affected in specific ways that are as different as Lilly mentioned in the talk last week is different to just considering like one access and the other access and that same. So how can we go about doing this? In previous work that we did, we looked at biases in word embeddings. And so previous work had showed some word embeddings are semantic representations of text. Basically each word has a vector representation and then proximity between words in that vector space is meant to capture semantic representation a semantic similarity along other properties of word embeddings. And so there has been research that showed that word embeddings contain biases for example, gender bias. And in this work, what we did, this work that we presented at AIS, we presented an algorithm to enumerate potential biases in a word embedding in a non-supervised way. And we showed that widely used word embeddings contain harmful biases associated to people's names. And through crowdsourcing, we also showed that these are lined with societal stereotypes. So for example, cluster of Latinx names were associated in widely used word embeddings with words such as drug trafficker, cartel, undocumented and illegal immigrants. So the question there becomes if we know that word embeddings contain these biases, can we use bias to fight bias? And so the underlying idea of the work that we presented at NACL last year is captured in these fraction of Shakespeare's from Anjulia that is what's in a name. So here, for example, the probability that we correctly classify someone as an engineer should not depend on where their name lies in the word embedding. That's another way of putting it. And so one way that we can go about preventing this is by directly penalizing a correlation between errors that the algorithm in classification is making and the location of the name in the word embedding. And we do this during training and then during these have the advantage that during deployment or testing, you're not using the name for prediction. So you don't need access to the name. And furthermore, an important thing of this is that this doesn't rely on whether the benefits of mitigating biases and the benefits of mitigating biases that the algorithm brings for a given individual don't depend on whether the demographics of that individual are well-proxied by their name. Meaning what we're doing is during training, we're nudging the algorithm to not focus on signals that are predictive of the names that lead to these parties in the classification errors. And then during testing, those signals are no longer used. That's like the underlying idea. And so those games extend beyond to those individuals whose demographics are poorly proxied by their name. So for example, a woman named Alex would not, like the fact that we can infer from their name, their gender is not a prerequisite for the games to be had. So how do we do this? So typically we minimize a loss function L. And so we propose to regularize the accuracy gaps in two ways. So two loss functions that we propose is first add cluster constraint loss. And so here basically what we're doing is we have clusters of names in the word embedding and we penalize whenever different clusters have different, there's differences in the error rates in different clusters. So for example, if a certain classification leads you to be very good at classifying engineers in one cluster of names and not in the other one, that is penalized. An alternative is to penalize these directly and we do this through the covariance constraint loss. And so here we're simply directly penalizing when the location of the name in the word embedding correlates with the probability that we correctly predict these individuals, the individual occupation, for example. And here this is something that, these are the results on a semi-synthetic data that we created using the UCI adult data set. So here the task is to predict income and you have the demographic attributes. So we use census data to create semi-synthetic names and look at how the algorithm would work. And we also applied it on the bias and bias data set and those results are in the paper. But here what is important to note that is a result that you have throughout different fairness and forcing algorithm is that the way to read this by the way is the X-axis is showing us the gender gap or the race gap in the way that I defined before. So this is basically like the differences in accuracy. And in the Y-axis we have the balance, accuracy. The balance is just capturing the fact that there's, the classes may be in balance in terms of professors. It's a much bigger class than rappers, for example, in this data set. And here the unconstrained, so if we don't enforce any constraint, we are in the top left for me, of the plod and as we enforce a larger constraint we see how this progresses. And this curve, the way that these accuracy fairness trade of curves look like is actually quite general to fairness enforcing algorithms. And the important thing to note here, oh, it's not circle A, is that these phenomenon here in which we see how we have gains in mitigating the gap without a cost in accuracy is actually quite general. And the same that we saw before in the other results. And so the important thing here is that we need to explicitly encode these values in the models because while there's not a cost in accuracy, it won't happen by chance. And well, all the details of this paper, of this work are in the paper and I'm also happy to discuss more in the Q&A and over Slack. And to summarize, we characterize the risk of compounding injustices in supervised learning and through a large scale study of automated recruiting we have showed both these compounding effect and the fact that it is not enough to remove explicit gender indicators to get rid of these effects. And we have proposed methodology to mitigate biases without assuming access to protected attributes. Some limitations and open questions from this work are first, how can data representations be leveraged to penalize this proxy discrimination without relying on names? So the underlying idea of the methodology here is how can we move past assuming that we have access to protected attributes, but we're relying heavily on having these access to names. And this is something that is A, not feasible in many domains and it can have privacy considerations and so on. So are there ways in which we can leverage data representations and the bias that is captured in them to penalize this proxy discrimination in other ways? Another question is, how do we mitigate this compounding bias effect when it stems from bias labels? So throughout the methodology when we're doing things like penalizing a correlation between the errors and the location of the name in the word embedding we're assuming that we can actually capture the errors and that if we're correctly predicting the label that we have in our data, that's what we want to do. But often cases the bias is in that label that we're trying to predict. So how can we mitigate this compounding and balance effect when we have that problem? And here clearly the methodology that I presented is not the right way to go about this. And then the other question is how do we mitigate this compounding bias effect and how does it actually play out when we're not making predictions in isolation or when we're actually giving a recommendation to a human expert? And there then the prediction is interacting with the way in which the human makes use of the information to impact the decision. Thank you very much. Hey, fantastic. Thanks so much, Maria. I think maybe we'll leave the slides up for now if that's all right, just so people can answer clarifying questions if they need to. That's great. If you don't mind me starting. So people in the broader audience, if you'd like to put questions into the Q&A panel and then please do and I will sort of pass them on. We'll start with one from there and ask you to sort of expand out a bit on something you said at the start of the talk. So Jessica called asks what happens to people using the then pronouns? Do they get that correctly classified? Now you mentioned obviously at the start the limitations of using pronouns. I'm curious to know how you think, how you think sort of thinking about gender in a less binary way would influence the sort of practical upshot of the kind of research you're doing here? Yeah, so I think that there's two, three things to note here. So the first one is that clearly in the characterization that we did, these words doesn't accurately capture how individuals that do not identify with binary gender pronouns would be affected. And so that is a clear limitation and there when you apply method, so that is goes for the characterization portion. Then when you look at methodologies of how do we mitigate these, then if we assume that there's a portion of the bias that could be mitigated when we penalize for the correlation between clusters of names in the word embeddings and accurate classification. So in that portion we're no longer relying on gender pronouns. However, we are relying on attributes being captured by names. And so in the work that we did here we found that word embeddings, captured biases that concern individuals, races, gender along binary dimensions. It's whether, here for example, a question that is open is for example whether a bias is targeting transgender individuals would be captured, right? And so here you can assume if there were some, because of a social phenomenon there are certain type of names that individuals were transgender individuals were more likely to adopt. And so this cluster and this was captured in the corpus that was trained in the word embedding then that could mitigate this. But of course that's a lot of if, if, if, right? And so that is to say how the way that we approach gender, what are the limitations of the ways in which it may or may not mitigate biases. And the other thing there is that the, it goes towards intersectionality and is the fact that the dynamics of the bias there we cannot assume will be the same. Like the social dynamic, for example, one of the things that we found in what words they, like what are the type of words that make it makes it likely for the algorithm to predict whether someone's a woman. Is there are certain words that like, certain like very confident words that would leave the algorithm to predict that it's a man. Because there's, so here I should mention that in the paper we have an additional part in which we're just looking at the algorithm's ability to infer the gender alone. And so for the purpose only of looking at whether this is something that is being captured in the rest of the data. So again, the social dynamics that lead to these type of data looking a certain way and the way that the different biases are represented and how people choose to represent themselves in the biography is going to likely be entirely different. And so that's another component there. Fantastic, thanks Maria. Okay, the next question is from Lixing Xie in computer science here at A&E, Lixing. Hi Maria, thanks for a very interesting talk. I have two questions. The first one is about word representation. The second one is about notions of fairness. So the first one, as you know that in the last two years or so the language models are increasingly moving to subword units such as biparing coding. Basically frequent for our physical science friends who may not have heard of like substrings that are frequent and they are below the word level. I was wondering whether the biases you observe still exist on the subword level and maybe, and whether this could be one thing, one representation that could hopefully mitigate the gender bias that you described. Yeah, so there's, I know there's some work that has been done on trying to look at bias in contextualized word embeddings. So I think that that may be the type of work that you're interested in. There's some from, I can look up the pointers. I believe that there's an LTI group at CMU that has been doing work on this. So Angelie and Julia. So I'm gonna look up their work and I can send you a pointer afterwards because yeah, that's a great point that is the word representations that are being used now have changed dramatically. And so the way that biases is being captured there may be significantly different. And yeah, that has been addressed at least in part by that word. So I can send you a pointer afterwards through this slide so that others have access to it. Yeah, we could do that on Slack. My second question is that the algorithm be proposed to mitigate the biases is trying to equalize the classification accuracy across say gender groups. So as we know, there are a kind of variety of fairness measures in like that's been proposed in the past few years. Do you think like your algorithm or similar ones are amenable to take into account other notions of fairness? Yeah, absolutely. Yeah, so what we're doing here so one tiny clarifications that here we're no longer looking at the gender groups because all the things that we're wanting to do there is not rely and that connects to the previous question that is we did not want to rely on the single attribute. And so there we're penalizing with correlations with the word embedding with the location of the name in the word embedding. So that's just a tiny, a tiny clarification and then with respect to other measures of fairness the reason why we were focused on mitigating the differences in accuracy was because of this compounding effect that was the justification that we were using for why isn't that we were cared about this measure but you can replace the laws for anything else. So any other group fairness measures. So for those who are not familiar with these there's different notions of algorithmic fairness in terms of the metrics of fairness. And so any group fairness measure that is looking at different statistical disparities in groups you can plug in into. So maybe here it's useful that I have the laws. So yeah, basically like these laws here could like how you're computing the difference here, right? For example, here it's easier to see you don't need to be looking at the differences in the predicate probability but you could be looking at other metrics, right? So for example, instead of looking at here what you're doing is you're looking at the true probability for that case, right? So you could be looking at other metrics you could just be looking at these priorities and you could just be looking at statistical priority for example. And so group notions of fairness you could plug in to the laws very directly. Oh, thanks a lot. Then you have thoughts on who might tell you which one, which loss function is the right one to use. Sorry? So which loss function is right for certain application scenarios? Have you had thoughts about how to go about that? Given that this is so flexible and we can essentially plug in any group fairness loss. Yeah, yeah. So the reason why we focus on the disparities in accuracy is because we were concerned with this compounding effect. And so in this case, that is the justification that led us to focus on this one in particular. And there's other cases where for example as I mentioned the limitations if you're concerned with bias in the label, right? In the target label that you're predicting then penalizing differences in accuracy is not going to solve your problem. And so there may be reinforcing something like statistical priority may be more desirable in certain contexts. So definitely in addition to why certain notions of fairness may matter because of elements like their compounding effect and so on, depending on the nature of the bias of the data and the domain expertise that can also lead you to choose different fairness metrics. Thank you so much. Thanks for those questions, Leshing. Okay, the next question is going to come from Sarita on this topic. I want to push back a little bit on the constraint that you're imposing here of assuming that the ultimate goal is to not use these labels at all or not have them inform the ultimate outcomes. And so I mean, that makes sense. Like it's a pretty common thing in a fairness measure. But it also in the context that you're saying you're applying it in this like a hiring decision you admit that this is the sort of thing that happens in the context of this is informing a human decision maker who was in turn acting in a situation in which there's underlying injustice. And I'm wondering what happens if you let go of the need to talk about fairness in terms of there being no information from the label to the outcome and instead sort of allow. Yeah, so this is a great question and also like a great opportunity to make a clarification that is in no way am I advocating for trying to forget about the protected attributes and trying to do a fully data driven approach to fairness in which we can completely ignore the context and the domain. So yeah, I know the things here is that the motivation for how can we mitigate biases without assuming access to protected attributes is more coming from constraints in a real world applications where you have for example, in certain jurisdictions in the US you cannot use protected attributes for making for example, like hiring related decisions. So if you're deciding who to show an ad you cannot use these attributes regardless of how you're using it, right? And so of course in the US in particular you get into very yeah, like whether you can do affirmative action type approaches and so on is something that is very different to other countries. So that is a big portion of the motivation here of why we want to think about how do we mitigate these? And another thing as you mentioned here is that there's a difference between the context that we were thinking about in large part was also in automated systems where you're for example just choosing who to show an advertisement, right? Or something like that. And so there it's not to say that you should that they protected attribute and the asymmetries of these doesn't matter, right? Like of course whether for and to begin with whether there's a compounding in balance effect that constitutes a compounding injustice depends on whether this initially in balance is an injustice, right? And so there what is the protected group that you're concerned about matters? And so that is an important thing to note in these characterization, but yeah in these case we're looking also at automated systems but I fully agree with you that once you're taking to account the dynamics of the human then again what the protected attribute is is also something that plays a super important role. Yeah, just one quick follow up there. It sounds like in terms of trying to actually abide by the letter of the law if you're thinking about the agent who's not allowed to discriminate on the basis of these protected characteristics as inclusive of both the algorithm and the humans involved then you might think for example that given that the humans are going to bring their own biases to the table the augment of the algorithm might be one of counteracting rather than sort of being a neutral presence if that makes sense, does that? So what you're saying basically is that if the effect, so I think that what you're saying is that you cannot be using these in the in the ultimate decision and so you think that it's legitimate to use it during the prediction that is informing the decision because you're countering the effect, is that capturing that correctly? Well, yeah, essentially I'm curious if the law applies to like basically what do you think of it as applying holistically to the decision maker in general or to each individual aspect? So, because if you're thinking about it as applying holistically, you might say, well, the decision system isn't bound by this requirement because the decision system is really there to help make the entire decision process more balanced in the end. Yeah, so I'm not a law scholar so that would that be caveat in the legislation in the US according to my understanding, there are many cases in which you just cannot use the protected attribute. Doesn't matter how you're using it or how that is affecting the ultimate decision. You just cannot be using it as one of the features. And so there, of course that opens a lot of questions of how not using that can actually lead to discrimination as you're saying and as you're pointing out but there's still that constraint you just cannot be using this feature. So we, one of the nice things about this team is that we have people from lots of different disciplines and we have a lawyer who has a hand in the cube that's a couple who we'll be getting to in a moment. But first we have Atuza Kassisa there, Atuza. Hi Maria, thank you so much for the great discussion. So my question is more about what if in the, like what if you have good reasons to assume that in the data set we have two different sources of bias. For example, in word embedding or like the first, the first onto like identify gender bias and mitigate on the gender bias by like kind of changing the loss function, but like we have gender bias and for example, racial bias. And so then it seems to me that in such cases even in the first case when we have just one sources of bias, it seems to me that there is no principle like systematic answer to how to go about choosing these loss functions. So what if we have these two sources of bias, how are we going to choose the loss function? Is it gonna be something really random? Is it gonna be something that we just, yeah, like or fine tune somehow to get something that we want to get the results or is there more like theoretical insights behind the choice of the loss functions? Yeah, that's a great question. And that was actually a big portion of the motivation for these methodologies. So here what we're doing is if you look at it, for example, the clusters, right? What we're doing is we're penalizing correlations between the cluster. So here we will completely beyond the portion of looking at gender and we're penalizing whether clusters of names in the word embeddings are correlated with errors. And so there's, I don't think I have it in these slides. So maybe I feel like we see, no, I don't think I do. So, but in the AIS paper, we have the tables looking at the groupings of names that arise in the clusters of the word embeddings. And so there you do capture not only different demographics, but you capture intersectionality. So you'll have, for example, a cluster of young Latinas, so like female, young, and from like certain areas, right? So like you do capture a lot of these intersectionality. And interesting thing here is that depending on the corpus that you're using, so depending, and this is part of the followup work that I'm doing, is looking at how the biases that you capture change drastically depending on the context of the word embedding. So for example, when I was looking at it in Spanish, and so where names end up, or like what the clusters end up looking like are picking up in entirely different societal biases that are present in the corpus, in the context that gave rights to the corpus that you use for training. But so that is what you're saying is precisely and what motivated this methodology that is how do we go about thinking about multiple dimensions of a bias. Again, caveat is here your constraint to bias is capturing, start capturing multiple and intersection. Could the same idea also apply in the first work that you presented, and not the word embedding case, but when you are trying to minimize gender bias or something? Yeah, so here what we're doing is, and you could do that directly without the use of the word embedding, but think on the side you care about. So for example, class A occupation, right? And here you could do it directly. So if you knew that you can act, you only care about gender bias, for example, and you have access to this information, then you could just directly penalize that. This is thinking about what happens if you care about penalizing, mitigating the bias that we found in the first portion, but you don't have access to gender data or you care about other aspects as well. And so that's how you go about this, but eventually what we're doing here is precisely addressing the bias that we characterize in the first portion. Well, thank you. Okay, next question from Marple Yang, a PhD student in computer science working on individual and group fairness. So this topic is right on point for you. Yeah, thanks, Seth. Thanks for the talk, Maria. I think it's always wonderful to see case studies because they help motivate why all this work is important in the first place. I think my question is related to the current, what we've been discussing so far. In the first paper, could you explain why we saw differences in accuracy for identifying the same occupation for two different gender groups? I assume my assumption is that it's about how the representations of how people write their biographies differently. And I just, I building on that, I wonder if there's something like about the way in which the biographies are gendered, right? Even for the same occupation, like how people write their own descriptions. Yeah. Yeah, so this is a great question and there's several parts to my answer. So one part is in terms of what gave rise to these bias, right? And so there what you have is that the data is in balance, right? So as long as you have differences in how the different genders choose to represent themselves, then that is what is giving rise to or are represented in their biographies. If they're written by someone else, then that is what gives rise to these like, like you have this like statistical, the algorithm has this statistical incentive to say, well, if I can infer the gender and I know that women are less likely to be surgeons than I'm more likely to not classify the women as surgeons. And in terms of what you're saying of the choices of representation, that is something that we started a little bit in our analysis and we found some interesting things there. So for example, well, the portion of swapping the gender pronouns allows us to see that it is not just that one of the genders just write biographies that are impossible to tell what they are, right? And so that's kind of like the area that we were covering with doing that. But then when looking at, and that's why we build the model to predict gender from the occupation, one thing that was interesting was at one point that this is a little bit anecdotal, but it's something that we found at scale. So there was, at scale there were certain words, for example, whether the word husband or wife appeared in the biography was much more likely to be the biography of a woman. And then there were certain words like renowned that were way less likely to be a woman. And so there, when I was running some of our biographies through the network and looking at the prediction, I run mine that I showed you, and when I run Jennifer, Jennifer, who's that called, Jennifer Chase, I run that one and the word renowned was marked as one of the words that the algorithm paid attention to to predict that Jennifer was a man. So, and of course, using attention to infer what the algorithm for explainability has like very clear limitations, but it still allows you to, in that because the algorithm is a small parenthesis, because the algorithm is not paying attention to something doesn't mean that it's not using it, but you can see that it is using something for these predictions. So that, I think, I hope gets to your question of what type of patterns we saw of what gave rise to this. Yeah, that's great. Thank you for that. Okay, so we've only got time for one more question, but those of you who have questions that are hanging over, please do take them over to the Slack channel and carry on the discussion there. It's been a super presentation discussion and a really wonderful model of how to present this kind of work for a general audience. So thank you for doing that. So to bring it back to a question in the Q&A, so Joe Hopers raised the comparison between this kind of work and the use of recommendation algorithms in particular for things like Amazon, what have you, where there's a narrowing effect that it seems that everyone wants the same things each time, leading to a very narrow model of the person. And he points out that the work was being done on building recommendation algorithms that can sort of avoid this bias and incorporate, I suppose, a certain measure of exploration as well as exploitation. Do you see kind of analogies between what you're identifying here and the same sort of problems that arise with recommendation algorithms? Yes, I think that here we had something that allowed us to make these assumptions that is not too well, that is people are interested in job postings within their occupation, for example. And so that is how, what you're predicting is evidently relevant to the individual and also you, that allowed us to decide that we wanted to focus on true positives. So that also like the effect of true positives, which is something that may be very different. And even here, there may be caveats to these that is different types of errors may have a different effect, right? And I think that here is where you start also having parallels with a lot of the personalization and targeted advertisements are things that is, if you're showing an ad for a job that is slightly above yours, and so that incentivizes you to apply, it's very different to if you get consistently shown jobs for something that you're overqualified. So looking at, looking at that I think is something that it could be done as follow-up from this work looking at how not all mistakes are the same here. And so the type of mistakes and the effects of like, for example, showing a job advertisement for something that you're overqualified versus underqualified may be different. Thank you for that, Maria. Okay, so my little helper has come to tell me that it's time to finish. So thanks so much, Maria. I think Michael is the one who could end the broadcast right now. I think only by ending the whole recording. Thank you to everybody who's stayed on. Thanks for all the great questions. Wait for it. And let's head over to Slack to continue the discussion.


In this video, we'll find the Taylor series for natural log of x centered at x equals 1. We begin by finding several derivatives of the natural log of x. So the zeroth derivative is natural log of x, and if we evaluate that at 1, we get 0. The first derivative is 1 over x, evaluated at 1 gives us 1. The second derivative is negative 1 over x squared, evaluated at 1 is negative 1. The third derivative is 2 over x cubed, evaluated at 1 is 2. The fourth derivative is negative 2 times 3 over x to the fourth, evaluated at 1 is negative 6. And the fifth derivative is 2 times 3 times 4 divided by x to the fifth, evaluated at 1 gives us 24. So in generating this Taylor series for f of x being natural log of x centered at x equals 1, here's the general form evaluated at x equals 1. We generate a few terms. Times we have 0 divided by 0 factorial times x minus 1 to the 0, plus 1 over 1 factorial times x minus 1 to the first, minus 1 over 2 factorial times x minus 1 to the second, plus 2 over 3 factorial times x minus 1 to the third, minus 6 over 4 factorial times x minus 1 to the fourth, plus 24 over 5 factorial times x minus 1 to the fifth, minus, and so on. Of course, remembering that each of these coefficients, numerators, were generated in the slide before.  And ultimately, we can write this in summation notation as the sum from k equals 1 to infinity of negative 1 to the k plus 1 over k times x minus 1 to the k. Verify this on your own paper.


Have you ever wanted to learn how to use Unity? Have you ever wanted to make your own idle game? Have you ever wanted to learn how to make any game in Unity in fact? Have you always wanted to learn how to code in C sharp? Have you tried my past Unity idle game tutorial series in the past and it really sucked? Well, this new series is for you. Well, I welcome all of you guys to a brand new Unity idle game tutorial series is the 2021 edition and I'm going to be starting from scratch. So some of you may ask, well, why am I redoing an entire series when I could just watch the other series? Well, first of all, the structure was really okay. Anyways, the structure was poorly done in the last series. I'm going to be honest, I just kind of threw a bunch of ideas just kept adding on to it as I kept making videos. And I think that was just a poor way of doing it. I also wasn't the best at explaining some things throughout the series as well. And the videos were ridiculously long sometimes like sometimes we had like 50 minute videos hour long videos and I don't want you guys to suffer like that this time. This series I plan to structure things perfectly in the right order and I want to explain more than I have in the past and just so you guys can learn something new. I also want to prevent some bugs too because that was always an issue when people were reporting little bugs and I had to make a follow up video to fix that what I will tell you is that it is a little more over 14 videos with let me look real quick up to 15 topics and a few bonus ones as well. Like the bonus ones are going to be like monetization stuff like that. I'll tell you guys that that will be at the very end. So yeah, I can't wait to start this with you guys again. It was really fun last time and I really wanted to start this again just to do a better job and I know how to code properly. So I'm going to be sure to start from very beginner and work our way up to the very end and do it properly at the same time. No adding things in the middle of the optimization video and doing all that little stuff. We're going to be doing that throughout the entire series but in little steps. So there's 17, like I said, there's like 15 topics and there's going to be 40 videos. So there's going to be a lot of in between videos that it's going to explain some parts of it or that's just going to build onto it kind of like extra episodes in a way. Anyways, thank you guys for watching and if you're excited for this series, make sure you smash the like button. If you're new, subscribe to my channel and turn on notifications so you get notified for when the series gets started. And I'll see you guys in episode 0.1, which is where we're setting up Unity, our IDEs and preventing any future errors. Anyways, catch you guys on the next one. Peace.


Ashwajubola metinubu, the Jagaban Bogu and the Jagaban of the Universe and his dear wife herself a notable political phenomenon ranking senator Uluremi Tinubu. Ashwajubola metinubu, the Jagaban Bogu and the Jagaban Bogu and the Jagaban Bogu and the Jagaban Bogu and the Jagaban Bogu and the Jagaban of the Universe and his dear wife herself a notable political phenomenon ranking senator Uluremi Tinubu. Ashwajubola metinubu, the Jagaban Bogu and the Jagaban Bogu and the Jagaban Bogu and the Jagaban Bogu and the Jagaban Bogu and the Jagaban Bogu and the Jagaban Bogu and the Jagaban Bogu and the Jagaban Bogu and the Jagaban Bogu and the Jagaban Bogu and the Jagaban Bogu and the Jagaban Bogu and the Jagaban Bogu and the Jagaban Bogu and the Jagaban Bogu and the Jagaban Bogu and the Jagaban Bogu and the Jagaban Bogu and the Jagaban Bogu and the Jagaban Bogu and the Jagaban Bogu and the Jagaban Bogu and the Jagaban Bogu and the Jagaban Bogu and the Jagaban Bogu and the Jagaban Bogu and the Jagaban Bogu and the Jagaban Bogu and the Jagaban Bogu and the Jagaban Bogu and the Jagaban Bogu and the Jagaban Bogu and the Jagaban Bogu and the Jagaban Bogu and the Jagaban the fire me of a kitty state is excellency o lu wa roti me akere do lu of on those state is excellency is excellency governor along of plateau state former governor of Oshun state o gweni raouf aregwe shola the governor elect of lega state governor elect of lega state miska babajin de san wu lu I'm sorry deputy governor of a do state comrade philippe shwaibu members of the national assembly present members of the federal executive council present service chiefs and I believe the inspector general of police in particular is here your excellency is of the diplomatic com are royal fathers present imperial majesty the only of ifen oba o wu zi o ja ja the second is royal majesty the o wa wu ku who is also present today is royal majesty the way of a doing it oba rufos adijube is royal majesty the way of a doing it oba rufos adijube is royal highness kitaro the second of bogu kingdom barista muhammed dantoro is royal highness the honor of abadji dr. adamu baba yunusa and all the representative the representative of my own cabiezi the representative of the karibu of remorland the aladu abaddu is royal highness oba amisu shola abaddi tijani and all other royal fathers who are present here today distinguished guests ladies and gentlemen we are here for the 11th edition of the Bola Tinubu Kologa this is a yearly development gathering a yearly gathering where we try to question and interrogate some of the difficult questions on economic and social development not just in Nigeria but across the world we do it in celebration of a man who has spent the last 30 years of his life in creative and catalytic public service he has from these days as governor of legal state provided clarity of thought and vision pioneering vision in all aspects of governance he is not a lawyer as many of us know but there are few Nigerians who have provoked so many legal controversies and constitutional challenges resulting in several landmark judicial rulings especially in the area of federalism and what today is loosely described as restructuring many of us know of course that he is not an engineer but a lot of his vision is what is responsible for what we see today in Lagos the BRT many know the bus rapid transport transport the leki industrial zone it will be a co-atlantic project which is a private project but which he initiated as governor of Lagos state and of course the reform in the reform of the tax system of Lagos state today Lagos as we know earns more revenue more Nigeria than 31 states of Nigeria put together that by itself began in 2001 with a very innovative way in which he reformed the tax system in Lagos and created an independent Lagos inland revenue service several African countries and states of Nigeria have engaged the LIRS as consultants for their own reform efforts but how about electoral reform in 2007 when our party then the ACM was rigged out of elections in Oshun, in Akiti, Nondo and in Ado state I was asked he invited me to his residence as I should have invited me to his residence at Bodilom and he said to me the only way by which we can possibly reclaim the states that have been taken away from us by this region is going through the courts and he said to me that the only way is by proving that there was multiple voting in other words proving that a few people simply from printed ballot papers dumped the ballot papers into ballot boxes and they were counted in favour of the opposition then of course the PDP he then said to me that the only way we can do this is by somehow he says I don't know how you are going to do it but somehow proving by forensic evidence that this is exactly what happened I said to him I said that nobody has ever proved an electoral case, an electoral petition by forensic evidence there is just no history of it and I said what is even more there are over a million ballot papers if we consider all of the ballot papers in each of these states there are over a million, in fact at the time there are over 1.3 million ballot papers putting together all the states aha can we prove multiple voting he said you know I have told you what my own views you just go and look for a way of sorting this out so I went to the UK and met with possibly the most experienced fingerprint expert in the entire United Kingdom a gentleman called Adrian Forte introduced to me by some of the QCs who I worked with in the UK and when I told Adrian Forte the enormity of the problem he laughed and almost fell off his chair he said in all my own almost 50 years of experience as a fingerprint expert I have not even done up to 4000 fingerprints you are now saying that I should come and do 1.3 million fingerprints come on that's almost impossible anyway so we parted that day that was the first day and then he called me back on Monday and said let's talk again about this crazy thing and we sat and talked about it and somehow designed a way he said of course we used a bit of technology but most importantly we got we were able to get fingerprint experts he said to me he said the only way is to get the number of fingerprint experts that have never been assembled before I said about how many he said a minimum of 50 fingerprint experts in the end we hired 63 fingerprint experts 53 50 of them were from the UK police what was even more interesting about that was that even to get the UK police to support us actually we had to come all the way from Nigeria to come and talk to friends and people who knew to try and persuade the authorities to let us use the UK police in the end they allowed us to of course in the spare time of those policemen we got an additional 10 policemen altogether 63 policemen working flat out for almost 6 months we were able to put together for the first time definitely in the history of electoral petitions anywhere in the world we were able to put together a solid forensic case that showed that all of the places where we were defeated it was on account of multiple thumbprinting and we also demonstrated that it was impossible for those who said they voted for instance we showed that if you cast 20,000 votes in a particular unit when we calculated the time that it takes to cast one single vote it's usually above 5 minutes but we discovered that our friends in that other political party were able to cast their votes in 5 seconds because all they were doing was simply thumbprinting they just simply take out a booklet and thumbprint as many as possible so we were able to demonstrate all of that in court called a long story short one by one we were able to get back the states that had been taken away from us the only way by which that could ever have been achieved is surely not just by the vision but by the determination of the leader of our leader at that time it was of course the leader of ACN at the time and he showed the kind of leadership that was very very rare of course not only defining what he thought was the way forward but also supporting it in every way and backing it in the very end the last state that we finally won was Osho state after almost 3 years of battle we finally won Osho state and I think that one of the things that you will say that all of us will agree with about our celebrant today is just that his dogged attitude his refusal to accept no for an answer actually you also pioneered merit in cabinet appointments in particular and I want to emphasise this because one of the major drawbacks that we have as a nation is that way by which we are blindsided into thinking that we can avoid merit and expect to achieve great things we simply cannot avoid merit I think as you already demonstrated in the years when he was governor that political appointments especially cabinet appointments he simply looked for those that he considered the best possible material at the time wherever they were from so for the first time in the history of Lagos state we had cabinet members from other parts of the country beginning with other parts of the south west Raouf Ahebeshola of course is from Osho state Ai from Ogustate Wali Edun from Ogustate Athawari from Delta he became the first commissioner for lands Lai Mohamed from Quara state Delia Lackey from Aikiti state and so many others for the first time we appointed judges from other in fact I have forgotten Benakabu Ize Benakabu Ize of course from Nambra state was commissioner for economic planning and budget after Yemika Dusso and for several years after that he served as commissioner for budget and national planning but also judges the appointment of judges from everywhere else in the country Justice Izou Yabbo and Justice Sibyl Mwaka were both appointed to be judges of Lagos state there is no other state in the country except some states in the north where we have seen the appointment of individuals from other states and I should argue for how many have that just speaking the best people no matter where they are from is a very important part of our story as a country so every once in a while I think that history gives us one or two persons who are gifted transformative leaders and I believe that our country has been gifted by this great transformative leader Ashwa Jupola Metsinubu I believe very strongly I believe very strongly that this man's political trajectory has only just begun he is a man who has tremendous giftings and is a man who I believe God has a great plan ahead for I pray for you Ashwa Jupola that as your days so shall your strength so shall your wisdom so shall your favour with God in Jesus mighty name amen I just say a word or two about the next level as you know the expression next level itself was a political campaign slogan in these last elections and what we were saying was simply that there was a next phase to what our country had seen there are many who would say that there were several things that were promised in 2015 which had not yet been realized but one of the I think the best way of putting it is to simply say that our country for the first time is experiencing a type of leadership that is bound to lead us to where it is that we have we have purpose for ourselves as a government the honest in my view the honest leadership a leadership with integrity of our president Muhammad Buhari is a very important component of getting anywhere at all in all of our development plans just before at the last colloquium that Nigeria's main problem is not a lack of ideas is not a lack of projects or programs it was most of the time especially with the leadership in the past was a lack of integrity in leadership and corruption in particular was the reason why we were finding it difficult to make any progress and I explained that that's why we earned US Dollars in four years 2010 to 2014 the highest ever in the history of our country the highest ever and yet Lagos in Badua Expressway not down Lagos Carno Rail all of what is being done today not down we cannot point to a single major project a single major infrastructure project that was completed in that in a ten year period including power so a government coming after so many years of waste must be a government first that emphasizes fiscal products a government that emphasizes integrity in public finance so that we manage the little resources we have to achieve the maximum that can be achieved and that is what President Muhammad Buhari set out to do the president has never has never claimed to be an orator and we all know that he is not an orator he just gets things done he was under President Muhammad Buhari that three of the four refineries that we have today were built when he was minister for petroleum 3,500 kilometers of pipelines were built by him as minister of petroleum in a three year period so there is a track record here not of talking but of just getting things done and this is what we have seen in the past four years that is why we are able to put aside the money for the legal supervision expressway and that is going on in our place at the moment that is why we opened the first phase of the legal scannery way and that was commissioned just a few weeks before a couple of days I think it was just last month but that was commissioned the Lagos Abeghutai Badon phase of that project the Takwari Railway now completed after 35 years of being on the books the Lagos the Abuja Rail the light rail project here in Abuja started in 2005 budget after budget never completed was completed also at the end of 2017 now we have that light rail now we have that light rail from the airport to Idu and same as several other projects several other major infrastructure projects Manbila Hydro has been on the cards for 40 years we have just put together the resources to begin that project and we will complete this project so for us infrastructure is critical at the next level you cannot have power you cannot have the situation where you have it where you have the kind of power situation you have today and expect transformating development that is why we are focused on power and other infrastructure we are focused on fixing the infrastructure in our country and we believe that if we are able to fix the infrastructure the roads, the rail and especially power we will be able to make significant progress today our rail project starts from the Appaport so that we are able to clear the congestion in that port because the legal scandal rail begins from the Appaport and we can start taking out cargo from the Appaport using rail as an alternative means of transportation the same is true of agriculture and we just heard our partners from Brazil talking about the agro industrial phase of agricultural development and we are committed to that project because we believe that that is what will transform the agro industrial phase of our planning and that is what will transform agriculture in this country and create the additional jobs that we require there is not enough to have farmers everywhere we need the refining capacity we need the processing capacity and that is what our partnership with the Brazilians will provide for us same is true of the reforms that we are making in education and healthcare today the emphasis is on digital literacy the emphasis is on training our teachers to be able to train the next generation of children in school and we are focused on doing so using some of the methods that we have described very comprehensively not just in the ERGP but in our next level document which I hope will be made available today so we are focusing on science, technology, engineering arts and maths not STEM but STEAM because we have added arts to it we are focusing on training young people on training young people in digital skills critical thinking and various other skills that are necessary for the sorts of jobs that the 21st century will provide the same is true of healthcare our focus on healthcare will be on national health insurance because we cannot we cannot finance healthcare by the budget alone composing national health insurance that is going to open the doors for financing healthcare for us finally we are also talking again about social investment our social investment program at the next level social investment program at the moment the program is the biggest of its kind anywhere in the African continent where we are feeding every day in our school feeding program 9.2 million children across 26 states every single day that program is important not just from the point of view of providing nutrition for many of the young people in public schools many of whom come from very poor parents and many who may not be assured of a decent meal a day that is a very important program for us also because of the multiply effect on agriculture and also on distribution of those products and of course those we employ as cooks and other services the same is true of our trader money program the government enterprise and empowerment program G trader money, market money and all of that where we are giving money to the bottom of the trading pyramid in Nigeria we lend them 10 it's a program of course as you know of the bank of industry we lend 10,000 Naira to this bottom of the pyramid traders once they repay we give them 15,000 Naira all of this is done on an electronic platform and it's on their mobile phones and they get 15,000 Naira when they pay back 20, 25 all the way up to 100 the same is true of our market money which we give to cooperatives and we've done so far trader money almost over a million and market money which is to the cooperatives almost 500,000 and we intend to expand the scope of that we also intend to expand the scope of our empowerment which is our jobs program for young people young graduates coming out of school and some non-graduates we are engaging 500,000 of such young people I'm paying them every month most of them now have a device such as I have which contains trading materials for enhancing the employability skills and that also that device also helps us to track several things that we want to track and also helps them to provide services especially survey services marketing research to people who may engage their services we intend to expand that program because we must expand the program 1.7 million people come out of school and are looking for jobs every single year we are able to bridge that gap we are engaging 500,000 of them as soon as they get out of the youth group but we need to expand that if we don't expand it of course the numbers continue to increase so the next level promises to be exciting it promises to be as challenging as possibly where we are coming from but as the president has said defeated we have no other focus we have no other commitment but the progress of this country and we will ensure that step by step we get to where it is that we promise the country that we are taking the country to so I want to say to you all the next level is for all of us and I urge that all of us co-operate to take us to this next level so on behalf of the president Muhammad Mubarak who I represent here today again I like to wish a happy birthday and say thank you all very much for making the time to come to this event God bless you all


Hello and welcome to the Grand Line Review, your source for everything one piece. Last week on the Arc Review we infuriated a yonko by crashing a wedding, and this week we are ascending the red line and landing at Marijoua to attend the Reverie. Reverie is the 30th arc in the series consisting of a quaint six chapters and a completely unknown amount of anime episodes because well, who knows when that will be a thing. But before I begin talking about anything whatsoever, I need to give a very strong warning for the anime only viewers. If you have somehow managed to remain spoiler free to this point, this review will contain HUGE spoilers you have been warned. I should also say that this review is going to be a little different from the rest, primarily because the Reverie arc has only very recently been completed in the grand scheme of things, and I don't believe we've had the appropriate time to digest it and its overall effect on the series, like we've been able to do with the previous arcs. So what follows are my very raw and initial feelings regarding the Reverie arc. First and foremost I feel a bit weird calling the events of Reverie an arc of its own because it's more of a brief interlude between larger arcs that has a whole ton of setup involved with little to no payoff, at least not within this portion of the story. Instead it leaves us with revelation after revelation intended to generate a healthy dose of excitement and reward after a fairly lengthy whole cake island arc. And the first of these gigantic events is Luffy's new bounty of 1.5 billion berries. Now after defeating Kartakuri there was no doubt in my mind that Luffy's bounty would rise to at least 1 billion, but I never even dreamt that it would triple to 1.5. Furthermore he became an unofficial fifth emperor, which you know despite characters like Blackbeard claiming that it is still too early for him to take that title, is huge progress. This development brings the eventual end of One Piece within sight really. Whether you think Luffy is truly ready or not he's playing on the same stage as figures like Kaido and Shanks now. But we also have very exciting developments outside of the world of piracy which is what the bulk of this art consists of. What the reverie does exceptionally well is bring together a whole host of characters from the entire history of the series and their interactions are really cool to see. For example I very much enjoyed the gathering of Vivi, Rebecca and Shirahoshi and how they immediately just started discussing their appreciation of Luffy, very surprisingly openly within the territory of the world nobles. And we have similar moments for a whole ton of recurring characters, so many so that Oda had to actually invent something new which are the little trivia panels that popped up briefly explaining for certain characters history with the straw hats. And yeah when you think about it they're actually pretty essential. One Piece is over two decades old now so there are fans who weren't even alive when it began publication. Given the long span of the series there are also probably fans who started reading or watching at a certain point in the series, let's say the New World for example, who aren't even aware of the pre-timeskip events. And as for the people who did their due diligence and started from chapter one or episode one and have just blasted through 900 chapters or episodes, well they absolutely need a refresher because there is no way in hell they're going to retain all of that information. And for people like me who have been following the series for well over a decade, these panels act as a nice bit of nostalgia. There's just something really cool about Oda drawing the pre-timeskip character designs in his current style because it has evolved a hell of a lot over the run of the series. But one thing during the reverie arc that I have some pretty mixed feelings on is the whole revelation of Eem. On the one hand I'm pretty damn excited that there is an all powerful figure like this essentially running the world in the shadows, but his existence also does some damage to the story, mainly to the Gorase really. Ever since their introduction in the Jaya arc these guys had represented one of the biggest mysteries of the series. Who are they? What kind of power do they have? Are they immortal? And after the introduction of Eem I really couldn't care less about them. I want to know more about the mysterious Eem, not his five man servants. Especially since with Eem comes the associated mystery of that giant straw hat, which for some reason exists within the vaults of Marijua.  I still really don't know what to make of it. And even thinking about it feels very surreal and weird. It's like the sort of thing you might see in some sort of questionable fanfiction. But hey as with everything I'm going to trust that Oda is going to make this strange relic into some pretty cool piece of story. I'm not going to theorize on it here because that's not what ArcReviews are for and honestly there's a gazillion other YouTubers out there happy to cater to those needs. Something else I'm not going to theorize about but should probably mention is that the reverie revealed very vaguely how Garp attained his title of Hero of the Marines which involves the era prior to Roger where allegedly Big Mum and Kaido were part of a crew known as the Rocks. And that is quite literally all we know.  I say you completely did not talk about Garp's conversation and the Lock Rock Pirates. That is highly surgical. I love your videos but missing that vital information makes me to not approve this review. And I got a lot of comments like that in fact that was probably the most civilized comment I could find. So I'm going to address it in this ArcReview. It was at best an intriguing piece of information that currently leads us nowhere into the realm of pure and poor speculation. When and if we do eventually learn more about the Rocks what was stated here is going to be entirely redundant. So chill guys, chill. Instead let's get to talking about something we have some actual substance to grasp onto which is the return of Saint Mosgard whose name was revealed to be Don Quixote Mosgard. Twist. A character I had all but completely forgotten about. But I was very keen on his reappearance because it went a long way to enhancing the retrospective view of one of my least favorite arcs Fishman Island. The flashback portion of Ultihime and Mosgard always felt very off to me because their story was never actually resolved and we just moved on. But the fact that Mosgard appears to have been affected by Ultihime gives her sacrifice new meaning. By converting even a single celestial dragon, Ultihime has set in motion a change that has the potential to truly shake the world. So I very much appreciate what the Reverie Arc did for Ultihime's story and character. Plus Mosgard got to smack Saint Charles in the face which is always nice to see. The Reverie Arc also holds the first proper post-time skip appearance of Bartholomew Kuma or at least the shell of what he's become. As well as a surprise shelling from Jewelry Bonnie.  But the flagged connection to Bonnie is something that was entirely unexpected to me and it makes me very keen to see the eventual Kuma flashback which, and I'm calling this now, I think will be one of the most tragic stories in the entire series. But back to what we have going on here, this is the 30th Arc in the series and the first in which we actually start to zero in on the Revolutionary Army. We learn more about their members and their incredibly ambitious goals which has been long overdue in my opinion, although the commanders as wonderfully quirky as they are were slightly underwhelming. At the moment I just really don't see them as a threat to the entirety of the marines and the world government but at the same time I try to keep in mind that the revolutionaries don't conduct business in the same brute force manner as every other power faction in the world. Their people were very specifically chosen for their particular method of contribution to the cause like Bellow Betty who actually has a truly terrifying power of being able to inspire people into action. So as with most things for me during this arc, the jury is still out on them. And now we come to Shanks. Look every time this dude makes an appearance the one piece world comes to a complete halt and this is certainly no exception. In fact it left us with one of the most puzzling questions of the entire arc. I mean we do know why he's here it's to speak about a certain pirate which could be Luffy Blackbeard or a third party entirely, but it was quite strange that the Gorace would actually accept an audience with one of the most dangerous pirates in the world. And what made Shanks's appearance at Marijoy even more unexpected to me was that Shanks had already had a cameo earlier on in the reverie arc reacting to Luffy's new bounty. So these six chapters contain a good old one-two double punch of Shanks which hey I greatly welcome. But in essence what this arc accomplishes is the gathering together of the one piece world on a scale we've never before seen while simultaneously expanding it far beyond our current knowledge base. Through various aforementioned teasers as well as the hints I haven't even touched on like the silhouetted appearance of Greenbull the reverie arc poses more questions than it answers in typical one piece world building style and that is why I love it so. But that pretty much does it for the reverie. Next week will be, wait you thought the arc review was done? No not quite because next week we've got some work to do as I will be using all of these insights we've gained over the past 30 videos to begin a comprehensive one-piece arc ranking. If you enjoyed this video then feel free to like, favorite or subscribe and if you are in any way keen on supporting this independent channel then please do feel free to check out my Patreon discord server or Twitter the links to which are in the handy description below. Finally please do comment with your thoughts on the reverie This has been the Grand Line Review and I'll see you next time.


After 8 months of halted production, American Horror Story Season 10 is about to begin filming again, and there have been a ton of updates regarding the season since my last update video. So if you want to stay as up to date as possible with American Horror Story, stay tuned to this video and make sure you're subscribed. Hold on. It's been almost 3 years, I think it may be time. Get ready for the real American Horror Story. Okay, so hi. My name is Skyler.  Yes, this is my face, and as you'll notice, I talk with my hands a lot. If you haven't unsubscribed already, I thank you. But let's just move on with the video. This video was made with the help of my channel members, as always. And if you would like to become a member, you can hit the join button below for as little as $0.99 a month and become a member. One, it helps me out a lot, and two, you'll get a ton of exclusive perks, such as exclusive videos every month, access to my Discord server. But I know you are all here for American Horror Story news, and I've got a ton of it, so let's just move on with the video. In case you haven't seen my previous videos regarding this season, the main cast for season 10 is Kathy Bates, Leslie Grossman, Ily Lord, Evan Peters, Sarah Poulson, Edina Porter, Lily Raib, Angelica Ross, Finn Whitrock, and McCulley Colkin. Recently, Angelica Ross revealed some details about her role, so let's roll the clip. I have not started shooting American Horror Story yet. We just started production, pre-production. And so I don't want to give anything away. I've read the first three scripts of season 10, and I'm like, oh my goodness. Ryan Murphy loves me, and I love Ryan Murphy. And I just, that's the only thing I can, because every time I look at my roles coming from Ryan Murphy's world, I'm like, what did I do to deserve such amazing roles? So this season coming up is going to be yet another powerful role that he has written me into. So I just stay tuned. I mean, am I going to blur the lines of what's right and wrong? Probably in this role, but you best believe that I'm going to be playing a powerhouse in this next season of American Horror Story. So how do you prepare for it? What do you have to do because of COVID? Do you have to quarantine, testing? What have they told you? Yeah, so in order to go back to filming, we pretty much have to be in a cycle of being tested every other day. So we get tested about three times a week. And even when we're not filming, they want us to still stay in that bubble. And so if we're not, let's say we still just have to test like three days prior to when we go back on to sex. I believe that's sort of the turnaround time of getting results. It seems our cast for season 10 is expanding with two new additions potentially being revealed. Keep in mind, these will very likely not be main roles. They might be recurring roles throughout the season or even one episode characters. The first of the two new additions is Ryan Kiera Armstrong, whose IMDb page has been updated to show that she is in the first episode of season 10. Now, this has happened in the past where smaller actors may have their IMDb pages updated with American Horror Story roles. And we do actually end up seeing those actors in the season. So there is some basis to this actor potentially being in the season, but IMDb is not always the most reliable source. So this actor is not confirmed, but she is a child actor who has had a lot of roles in film and television, including it chapter 2 and with an E on Netflix. But the next new cast member after her is actually confirmed because this actor was posted on Ryan Murphy's Instagram. He is a contortionist by the name of Spencer Novich and Ryan Murphy shared his audition tape for season 10 on his Instagram. So let's watch that and then we'll discuss it because there are some potential hints towards the season. Okay, so obviously Spencer Novich can do a lot of crazy things with his body and he has also had roles in circus related films. So Ryan's caption to this post on Instagram was congrats to Spencer Novich who is joining the cast of this season's American Horror Story. This is the audition that got him the job. Guess the title yet? No Ryan, we can't, but there are a ton of fan theories that have come up in the past week and I will share the major one with you after this next major update that we have gotten for season 10. The first official poster for season 10 has been revealed and it features an extreme close-up on a mouth with some gnarly teeth, a tattoo pen dripping ink onto the tongue which has the letters ahs10 tattooed onto it. I worded that really weird but I think you get what I'm saying. Lots to discuss about this poster but let's just start off with some clarification. A lot of people including myself initially thought that this was a like sort of dentist tool that was being put in this person's mouth because of the emphasis on the teeth and the rubber glove. I thought it was a dentist the first time I looked at it but upon further inspection it is actually a tattoo pen. I wouldn't jump into saying the season is going to be about dentists but I think the main prevailing theory regarding this poster is body modifications and especially after Spencer Novich was added to the cast a lot of people think this will be a major part in this season and I think we're pretty close to cracking the theme and I think the theme will be revealed pretty soon so subscribe. The teeth in the poster are very similar to the stock photo that Ryan Murphy shared over the summer and fans still think that this could be a very major part of the season but I feel like I've talked about the sirens and mermaids thing a lot on this channel so I'm not going to get into that anymore. So as you may know ahs10 was initially supposed to begin filming back in March but due to obvious circumstances it was pushed back to a fall start date and we even got confirmation at one point that filming was set to begin on October 31st. Obviously that has now passed and believe it or not American Horror Story has not begun filming so yes production has been pushed back yet again and we're not quite sure as to win but we do know that sets are still being constructed for the season so I would still expect American Horror Story to start filming pretty soon most likely next month December or even January at the very latest but with sets nearly being completely constructed I think I think they're most likely gearing up to be filming in the very near future. Here's here's why I think there's been so much shuffling about the start time for American Horror Story season 10 and that is Ryan Murphy productions tend to reuse a lot of the same crew members for the different shows that they are making so like hairstylists, makeup artists, grits, those types of crew members they might be working on American Horror Story, 9-1-1, Ratchet all at the same time and in a normal year these shows would be filming at the same time or there would at least be a few of them that overlap but with COVID obviously the crews have to be limited and really who knows what restrictions there may be regarding one crew member working on multiple sets like is that still allowed so with COVID procedures in mind I can imagine scheduling crews for these shows would be a massive headache especially if they can't be worked on simultaneously or there has to be different crews on each set but the good news is American Crime Story season three about the impeachment of President Clinton is filming and we actually did get a first look of Sarah Paulson as Linda Tripp. Alright so let's pivot from American Horror Story to the brand new AHS spinoff American Horror Stories. We just not only got the first official poster for American Horror Stories but we also got some new cast members and some new details about American Horror Stories.   I'll talk about the poster more in a bit but let me just explain what the show is in case you are new here. American Horror Stories was green light earlier in the year and it is an episodic anthology set in the AHS universe with many AHS cast members returning. It appears it's going to be an FX on Hulu exclusive which I hear may be a bummer for some international fans but we currently don't really know what the international distribution for the show will be. We also just got confirmation that the first season set to air next year in 2021 will have 16 episodes and like I said before each episode will be a standalone story exploring new themes in the American Horror Story universe. So I have a bit of a theory regarding this poster and I am 100% sure that it is 100% off base but it does tie into something we talked about in the summer. So obviously the poster has this spider theme uh there's a black widow so that immediately makes me think that maybe one of the 16 episodes will deal with arachnophobia or something relating to spiders. But beyond that I think the first thing a lot of American Horror Story fans noticed was the latex that the model is wearing on the poster. So will one of the episodes of American Horror Stories explore the idea of a rubber woman perhaps? Keep this in mind as I explained the rest of this theory because it's about to be a little bit in the world of delusion. But anyway the the model's hair in this photo and the model's hands positioned on her face and the black and whiteness of it all visually reminds me of a very specific Ariana Grande tour visual specifically Be Alright from The Dangerous Woman Tour. If you recall this summer there were a lot of rumors that Ariana Grande would be joining the cast of season 10 because Ryan Murphy followed her on Instagram. You may be thinking that's not a big deal but for the past however many years that Ryan Murphy has been on Instagram he has only followed one person and that is his husband and then this summer that number switched to two and the other person was Ariana Grande. And for quite some time he was only following those two people but now he has since let a lot of people in. He now follows people on Instagram. So maybe Ryan Murphy was just learning how to use Instagram after all these years but the day that he did follow Ariana Grande he also posted a throwback photo to the set of American Horror Story Murder House with the title coming soon. And this photo from Murder House is of the rubber man. So do you see where I'm going with this? When this happened American Horror Stories had not been announced yet. So with these two Instagram activities happening on the very same day people were like oh Ariana Grande must be joining the cast of season 10 because at the time we only knew about season 10 but now that we know about American Horror Stories obviously that was in the works around that time because the announcement was very soon after. And the first official poster features potentially a hint towards a rubber woman. And if you recall obviously Ariana's worked with Ryan before on Scream Queens and she was initially cast in Ryan's adaptation of the prom but she had to drop out due to touring conflicts. But let me know if you think this theory has any grounds at all. I think it's a possibility but let me know if you would like to see Ariana in an episode of American Horror Stories. But moving on with some more big news regarding the spin-off two cast numbers have been added to the project. After many fans were disappointed that he would not be in season 10 Ryan Murphy revealed that Cody Fern will be featured in an episode of American Horror Stories. Obviously he previously appeared in Apocalypse 1984 and American Crime Story for Sochi. And then also straight from the murder house itself we have Dylan McDermott who will also be in an episode of American Horror Stories. Like I said he has had roles in Murder House, Asylum, 1984 and Apocalypse and I personally enjoyed his last role on 1984 a lot and I also enjoyed him in Hollywood so I'm looking forward to a new character in the American Horror Story world. I'm also very excited that Cody will have an episode he is definitely one of my favorite newer actors that has been added to American Horror Story over the recent years. I loved him as David in Versace and obviously Michael Langton in Apocalypse. I do hope his new character has a little bit more meat than Xavier did in 1984. Well there you have it that is absolutely everything you need to know about American Horror Story season 10 and obviously what was supposed to kick off earlier in the year is finally about to kick off so make sure you're subscribed because I will be updating you guys on all the filming updates once cameras do start to roll on American Horror Story season 10. Give this video a like if you learned anything new because it really does help my videos get recommended to other people. Leave a comment about what you would most like to see in American Horror Story season 10. I will be trying to respond to every one of your comments. Thank you guys so much for watching and I'll see you next time.


It's been a dramatic Saturday here at Black Mountain as players moved into contention, no fewer than 28 within three shots of the lead going into tomorrow's final round. Amongst those who are in with a chance, Kyradek Appy Barnrat who struggled with a hip injury halfway through the back nine, but then made a run of four birdies in a row including this one at 16 to be at 13 under, only two back. Richard T Lee of Canada, how about this one from Richard at the 17th? Nicely hold out a 65, 13 under today, Richard T Lee. Yongjun Bae of Korea in a little bit of trouble you might say at the 17th. This is what happened. Even his playing partners had to applaud that one stunning stuff. No bogeys in the round and he's on 14 under, one behind the four players who lead jazz. Jin Watanandong had a lot of support on his home course. This for a birdie at 17, a 69 today for jazz, he's on 15 under. So too is Zach Murray, the tall Australian looking to finish with an eagle and take the lead on his own. Didn't quite manage the three, but the four was good enough and he too has a share of the lead. Chikaranga part at the 13th, how about this? Again, absolutely beautifully judged. What about that for a bonus? Terrific stuff from the Indian, a 67 and he too is on 15 and the fourth member of the leading quartet is Siddhika Raman of Bangladesh, nine birdies in his round today, setting one up with this approach to the 15th, a 64. It's going to be some day tomorrow, the final round. Here at Black Mountain.