Good afternoon, everyone. Thanks for coming to our webinar today. My name is Shobita Parthasarathy, and I'm professor in the Ford School of Public Policy and director of the Science, Technology, and Public Policy program here at University of Michigan. I'm also the director of the Technology Assessment Project, which is the reason that we're all here today to discuss the publication of our recent report, Cameras in the Classroom, Facial Recognition Technology in Schools. Before we begin, I want to address the ongoing strike by University of Michigan's Graduate Employees Union that began about 10 days ago. These graduate students and others are striking because they believe that the university's pandemic reopening plans are insufficient and dangerous because they want the university to cut off its relationship with the Ann Arbor Police Department and because they want the funds from the university's Division of Public Safety to be reallocated. We are sympathetic to those concerns, and we thought long and hard about whether to have the event today, but we decided to go forward because the issues that we discuss in our report about law enforcement bias against people of color and racism in surveillance technologies are aligned with GEO's concerns. Our overall aim today is to give you an introduction to the Technology Assessment Project, or TAP, as we call it, and our methods in analyzing facial recognition and other technologies to summarize our findings a bit, and then to talk about our conclusions and policy recommendations. We will have time for questions at the end, so please put your questions in the chat and we will get to them. TAP is part of the University of Michigan's Science, Technology, and Public Policy Program. Founded in 2006, STPP is a unique research, education, and policy engagement center concerned with cutting-edge questions that arise at the intersection of technology, science, society, and policy. Housed in the Ford School of Public Policy, we have a vibrant graduate certificate program, postdoctoral fellowship program, public and policy engagement activities, and a lecture series. Our graduate certificate program is the jewel of our work. We teach students about how values society and politics shape technology and science, and how science and technology shape our world. Students also learn about the science and technology policy-making environment and how to engage in it. Our alumni now hold jobs across government, the private sector, in non-governmental organizations, academia, and think tanks. Our program is unique both because of the profoundly interdisciplinary approach we take to teaching students about the relationships between technology, science, society, and policy, but also because of the interdisciplinarity of the students who participate. STPP engages students from across the university, from the School of Information to the Business School. 26% of our students come from engineering, for example. And in recent years, given the growing interests in the social, ethical, and equity implications of emerging technologies, our program has been growing. At present, we have 73 students. STPP launched the technology assessment project in the fall of 2019 because we live in a world increasingly driven by technology, and this technologically driven world is producing increased unease. As citizens, we are aware more and more of how technology is shaping our lives, and we're also just starting to see how it has disproportionate impacts, how it tends to not just reflect, but even reinforce inequalities, for example. At the same time, policy makers are often flummoxed about how to manage emerging technologies. They worry that they can't properly anticipate the consequences in order to regulate them properly. It seems like technological development moves so quickly, how can there be adequate time for policy discussion and legislative activity? On what basis should they be making decisions? It's because of these questions that we wanted to do something. But the idea of technology assessment as a general idea isn't new. Scholars and policy makers have used a variety of techniques to try to anticipate the social, ethical, and environmental implications of emerging technologies and use this analysis to inform their governance. What we're trying to do here is something a little bit different. We're developing what I call an analogical case study method in some using historical examples to inform our analysis of emerging technologies. I'm a social scientist myself, and I use these kinds of historical case study methods in my own research, so it's familiar to me. The basic idea of TAP, though, is that the implications of emerging technologies are much more predictable than we tend to think. We can actually learn from the history of technology to anticipate the future. If we look at previous technologies, we can understand social patterns in how technologies tend to be built, implemented, and governed, and the kinds of problems that arise. And if we understand those things, then we can do a better job of anticipating those consequences. Before we get into the report, I want to introduce our brilliant research team. They're all here today, and you'll be hearing from each of them. Claire Gallaghan is a recent graduate of the Ford School of Public Policy and now an associate at Kaufman Hall in Chicago. Hannah Rosenfeld is a Master's of Public Policy student and also a Graduate Certificate student in STPP, and she has a background in tech, so she represents some of the disciplinary diversity that I mentioned earlier. I want to emphasize that an important part of the TAP is its training component. We're teaching students to develop skills in this evolving analogical case study method to analyze emerging technologies and to develop key interdisciplinary research and writing skills. It's important to note that Claire and Hannah did much of the case development and writing. And of course, as the TAP report was released, they've had the opportunity to learn how to disseminate reports like these, how to engage with the media, how to write op-eds, and other kinds of commentaries for public audiences, and of course, give presentations. Finally, Dr. Molly Keiman, who is STPP's program manager and truly Jill of all trades, who's also an expert in educational technologies and therefore was an invaluable part of the research team. And we have a number of people from the Ford School helping us today as well, and I want to thank them too. So why facial recognition and why schools? In so many ways, the idea of using a digitized image of your face to identify or verify your identity seems like the stuff of science fiction, but it's increasingly used today from China to Europe to the United States. It's used in a variety of settings, including most notably and famously for surveillance and security purposes in law enforcement, but also even for identity verification in our smartphones. Here in Detroit, we have Project Greenlight in which businesses send footage to police and these digital images are checked against law enforcement databases. The new use on the block, so to speak, is facial recognition technology in schools. We've seen it increasingly used across the United States in an age of concern about school shootings, monitoring who comes onto school grounds via facial recognition seems like a great solution. Perhaps most notably in 2018, Lockport, New York near Niagara Falls announced that it would install a facial recognition system in its schools. The way it works is that cameras installed on school grounds would capture the faces of intruders, analyze whether they matched any persons of interest in the area, have that match confirmed by a human security person, and then if there was a match, it would be sent to district administrators who would then decide what to do. The system was approved by the New York State Department of Education last year and then it finally became operational earlier this year. Since then, it's been a subject of a lawsuit by the New York Civil Liberties Union and in July, the New York State Legislature passed a two-year moratorium on the use of biometric technology in schools. It's now awaiting Governor Cuomo's signature. So these really are live debates. At present, there are no national level laws explicitly focused on regulating facial recognition anywhere in the world. In fact, quite the opposite. Many countries are expanding their use of the technology without any regulation in place. Mexico City, for example, recently invested $10 million in putting thousands of facial recognition-enabled security cameras to monitor the public across the city. Huawei's Safe City system has been installed in 230 cities around the world, from Africa to Europe. But we did do a comprehensive policy analysis of the national and international landscape and we wanted to look for policies that might be interpreted as being related to facial recognition. And we classified both proposed and passed policies into five categories. The first two are consent and notification policies which focus on data collection and data security policies that focus on what happens to the data once it's actually been collected. A number of policies in these two categories have been passed around the world, most notably the General Data Protection Regulation or GDPR in Europe. Similar laws have been passed in India, Kenya, and also in the U.S. in Illinois and California. And European courts have found that the GDPR covers facial recognition. The third category is policies that tailor use, that is to prescribe acceptable uses of the technology. We see this in the case of Project Greenlight, for example, where the city of Detroit has limited the use of facial recognition to investigate violent crimes and also banned the real-time video footage that was part of the original approach. Fourth, we see oversight, reporting, and standard-setting policies. These mandate different ways of observing and controlling the operations of a facial recognition system, including their accuracy. Most of these have only been proposed. And then finally, we see bans and temporary moratoria on facial recognition's use. These have been proposed around the world as well as at the national level in the United States. But where we see a lot more policy activity in the United States is at the local and state level. Some states have banned law enforcement's use of facial recognition in body cameras. And a number of cities, from Somerville, Massachusetts, to most recently, Portland, have enacted bans of varying scope and strength. So, we have some policy activity, but a lot of it is in progress, and it's pretty piecemeal and non-explicitly at the national level. And this raises some questions. When we think about developing policy for facial recognition, what should we be thinking about? And how do we know what we should be thinking about? This brings us to analogical case study analysis. By analogical case comparison, we mean systematically analyzing the development, implementation, and regulation of previous technologies in order to anticipate how a new one might emerge and the challenges it will pose. So, when it comes to facial recognition, we looked first at technologies that seemed similar in both their form and their function to facial recognition. We looked, for example, at how closed circuit television and school resource officers have been used, what kinds of social, psychological, and equity implications they've had. We also, once we started to develop some ideas about the kinds of implications that facial recognition might have, we looked at other sorts of technologies that had those implications and then tried to expand our understanding in that direction as well. So, for example, as we began to realize that facial recognition might create new kinds of markets in data, we looked at markets that had been created in biological data like human tissue and genetic information and tried to understand the implications of that. We did all of this iteratively and we ended up with 11 case studies until we could clearly see five main conclusions from analyzing these case studies. Claire, you wanna take it from there? Hello, everyone, I'm Claire Galligan and I'm going to talk to you a bit today about the first three of our five findings about why facial recognition has no place in schools.  Not only will it discriminate against students of color, but it will do so while appearing legitimate and fair because technology is so often assumed to be objective and highly accurate. You may be thinking, but isn't technology objective or at least more objective than humans? The answer to that is absolutely not. Technology does not exist in a vacuum. Facial recognition is developed by humans based on data sets compiled by humans and then used and regulated by humans. Human bias enters the process every step of the way and discriminatory racial biases are no exception. We came to this conclusion that facial recognition is racist by studying the analogical case of stop and frisk, the policy that allows police officers to stop citizens on the street based on an incredibly relaxed standard of reasonable suspicion. Stop and frisk is like facial recognition in schools in that they are both seemingly neutral, but they actually discriminate against people of color because bias enters their use. Many would argue that stop and frisk is neutral because, hey, if you're not acting suspicious, you should have nothing to worry about. However, in practice, that bore out to be incredibly untrue. Stop and frisk has been consistently proven to be waged disproportionately against black and brown citizens. Take New York City, for example. Throughout the use of this policy, people of color were stopped and frisk at far higher rates than white residents compared to both their share of the overall population and compared to the rates of crime that they actually committed. Stop and frisk was a policy that because it was susceptible to racial biases of officers, it criminalized and discriminated against people of color at high rates, even though it was supposed to be a fair and objective policy. Because facial recognition is similarly susceptible to user biases, we expected to, like stop and frisk, unfairly target children of color. Finally, in addition to facial recognition subjectivity, it has also been proven time and time again to be technically inaccurate. Facial recognition algorithms consistently show higher error rates for people of color. White and male subjects consistently enjoy the highest accuracy rates with facial recognition, while black, brown, and indigenous individuals and especially women are consistently misidentified. This will create barriers for students of color because they are more likely to be misidentified by facial recognition. They're more likely to be, say, accidentally marked absent from class, locked out of school buildings, or even flagged as an intruder in their own school. Altogether, facial recognition is racist because it is both technically inaccurate and completely inextricable from user bias. It will discriminate and create barriers against students of color at school and so we believe it should be banned. This brings us to our second finding. Facial recognition will bring state surveillance into the classroom. We expect that administrators of facial recognition at school will use this technology liberally, conditioning students to think it's normal to be constantly watched and have no right to privacy at school, an environment which is supposed to be safe and constructive. This has proven to have negative emotional effects on children. The analogical case study of the use of closed circuit television in schools led us to this conclusion. CCTV is used in most secondary schools in the United Kingdom and because facial recognition is virtually the same technology as CCTV, just with added and much more powerful capabilities, we felt this case was a perfect example of how putting facial recognition in schools will play out. This case revealed to us that when administrators are entrusted with powerful surveillance systems, it is hard to control how they use them. We call this mission creep. The use of surveillance technologies outside of their original agreed upon intent. Interviews with students at these schools revealed that though these systems were originally implemented for security purposes, they were ultimately used for behavior monitoring and control. CCTV was supposed to be used to detect school intruders, but rather was used to punish students who violated dress code or tardy or were behaving out of turn. Students reported that this use of CCTV as a quasi all seeing eye at school made them feel powerless, criminalized by their school and mistrusted. They reported that they would even change how they act or dressed at school in order to avoid punishment. And this heightened anxiety in reduced feelings of safety at school is extremely likely to degrade a child's educational quality. Because CCTV is just like facial recognition, except for the fact that facial recognition can not only surveil but also automatically identify students, we are confident that just like CCTV, administrators of facial recognition will be unable to resist the temptation to use it outside of its agreed upon purposes. And we're confident that the presence of this constant surveillance will make students feel anxious, stressed and afraid in ascending where they are supposed to feel as safe as ever. And this brings me to our third finding. Facial recognition in schools will punish non-conformity by creating barriers for students who don't fit into specific standards of acceptable appearance and behavior. As I've already touched on, facial recognition is less accurate when used on people of color. However, this is only the tip of the iceberg. Facial recognition also has higher error rates when used on students with disabilities, gender non-conforming students and children. Yes, children. Particularly problematic if this technology gets placed into K through 12 schools. We're confident that these higher error rates will mean that instituting facial recognition in schools will create barriers for students who may already be part of marginalized groups. We drew this conclusion from thinking about ADHAR, India's nationwide biometric system. This is the largest biometric system in the world, having collected fingerprints and iris and facial scans from a majority of India's over one billion citizens. Enrollment in ADHAR is required to access many public and private services, including welfare, pensions, mobile phones, financial transactions, and school and work enrollment. However, like facial recognition, ADHAR is designed in such a way that it excludes certain citizens. Specifically, citizens who cannot submit biometric data, such as manual laborers or leprosy patients who may have damaged fingerprints or eyes. This means that these individuals who are already disadvantaged are now also unable to access food rations, welfare, or pensions. Therefore, because these groups don't fit a certain acceptability standard, they face even more disadvantages in society through no fault of their own. We expect that facial recognition will replicate this in schools. Finally, we know from the CCTV example I discussed earlier that it is likely that facial recognition will be used in schools to police behavior, speech, and dress. We expect that this will police students' methods of personal expression and perhaps even get them in trouble for not conforming to an administrator's preferred appearance and behavioral standards. All together, because facial recognition is likely to malfunction on most students who are not white, cisgender, and able-bodied, we could expect facial recognition to cause already marginalized students to be incorrectly marked absent for class, prevented from checking out library books, or paying for lunch. We also expect that children will be directly or indirectly discouraged from free public expression. Overall, facial recognition systems in schools are poised to privilege some students and exclude and punish others based on characteristics either outside of their control or their chosen method of personal expression. And with that, I will turn it over to Hannah. Thanks, Claire. I'm Hannah Rosenfeld. And there's so much detail and so many great examples of everything that we're talking about today in the report. So I do really encourage all of you to read it. But for right now, I want to focus on just a few highlights for our cases for the last two major themes that we touched on. So first, let's talk about how companies commodify and profit from data. So obviously a company can't own your face, but they can own a representation of it, like the one that's created by a facial recognition algorithm. Now, a data point may not be particularly valuable on its own, but companies can create value by aggregating data, either with a lot of other data of the same type or with different information that gives it context. So individuals typically don't own any of the rights of their biometric data despite this and they often don't have a meaningful way to give and revoke consent to collect and keep that data. These new databases are vulnerable to exploitation via theft, subpoenas, and mission creating. So one example that we talk about in the report is a surveillance company called Vigilance Solutions. They started out by selling license plate scanners to private companies and they collected all of the license plate data in information from all of their customers in a cloud. They also knew where all those scanners were placed so they had that geolocation information and they were able to recognize that they could use that to build a new product that gave real time information about the location of cars around the country. They packaged that product and they sold access to it to law enforcement. The law enforcement product was so valuable that they started to actually give away their original license plate reading systems for free to get even more data. So they created a new data market around license plate data that didn't exist before and then they were strongly incentivized to expand it. We talk more about the implications of behavior like this in the report but generally we do expect that facial recognition companies operating in schools will also have a strong incentive to find secondary uses like this for student data and to get that valuable data they will try to expand their opportunities for collection as much as possible by pushing to get into as many schools as they can. Paradoxically US courts have traditionally held that individuals do not own property they don't have property ownership over their own biometric data. So in part that's because the data doesn't have a lot of value when it's considered on its own. So one case that we used to look at this in the report is the Moore versus the Regents of the University of California 1990s case. In that case, Dr. Gold of UCLA used samples from his patient John Moore to develop a valuable research cell line and he sold that profiting off of it without initially telling more about the research or potential profit. Though after he sold it, he did after he patented he did eventually notify more of this use. So when Moore sued the court determined that Dr. Gold should have told him about the research upfront but that Moore's tissue was considered discarded even when it was inside him because he couldn't really use it for anything on its own. So therefore he had no property right fluid. And following that and a number of other similar cases today generally a company or researcher cannot use biometric data without consent but they don't owe anyone a property stake and something that they develop using their data. However, meaningful consent is often limited when it comes to complex technology systems and especially in situations like schools where you can't really opt out of it. In the US in particular, students really don't get a chance to engage with any of those questions because the Family Educational Rights and Privacy Act FERPA actually allows schools to consent on behalf of their students. So consent isn't really a topic for the students who may be surveilled. This push to expand surveillance by companies is bad because it'll expand the reach of all the problems Claire already mentioned today but it also introduces some new issues of its own. First, without strong data protections this information is vulnerable to being stolen and it's impossible to replace things like fingerprints and faces. So once it's stolen, it's out there. Second, data collected for one purpose will be used in other ways often without the same level of scrutiny that the original use received. That's the mission creep that we talked about. Thinking back to vigilant technologies many of those private customers who use those initial license plate scanners may not have signed up to share that information in the cloud if they knew that they were going to be generating data that would be sold and packaged to the police. And even if a company doesn't actually package that data and profit off of law enforcement use once it's been collected the police could often subpoena that information and it's a lot cheaper and less politically difficult to subpoena it than building a similar database on their own. So that's what happened last year with ancestry.com when police subpoenaed information from their DNA database to solve a crime. It would have probably been very difficult for the police to put that database together on their own. You can easily imagine that police could subpoena facial recognition information from schools to identify a student or our trackers child's whereabouts if we're looking at the facial recognition case. Our last theme is institutionalizing inaccuracy. Again, in the report we really systematically unpack what accuracy actually means and what it doesn't mean for a technology like facial recognition. And we really talk about all of the ways that this idea is complicated for a socio-technical system. But accuracy could be a webinar in and of itself. So right now I just wanna directly tie what Claire said earlier about technologies being inextricable from the human societies that they are in. And that's what we mean by a socio-technical system directly to the questions about accuracy. And I wanna show how these questions often get overlooked leading to poorly functioning technologies becoming entrenched in daily operations. So proponents of facial recognition often answer critiques about the technologies by pointing out that these algorithms they learn. So they will get better, they'll be improved by being used. Actually, facial recognition's accuracy problem begins during the learning stage and that's early in development. So to build and test an algorithm, first you build a training dataset of facial images, the system learns how to identify faces, then you apply that algorithm to a testing set of faces where the researcher knows the identity but the program doesn't. And you see how often the algorithm gets it right. That's the accuracy measurement that you're getting from most companies. So the demographic mix in the training set is gonna determine how strong the algorithm is in various demographics. But problems can be really easily hidden if the two datasets, the training and the testing set have the same deviations and they're different from the real world population.  But when you apply that system in the real world where black women exist, you're gonna have a problem. And this is exactly what we're seeing. So the most common facial recognition data set, testing set is 77.5% male and 83.5% white. And NIST, the main US agency involved in assessing facial recognition accuracy hasn't disclosed the demographic makeup of the database that it uses to test software. So it's difficult to contextualize their reports about accuracy, but we do know that they built another database that was intended specifically to test how well facial recognition performs across racial groups and it used country of origin as a proxy for race. And it didn't include any countries that are predominantly black. So a high accuracy can also be hidden by poor performance in one group. If that's outweighed by very high performance in another group, you can begin to get the sense that it's very difficult to tell from one or two numbers that a company might provide or that a school district might have access to, how accurate is this going to be across the populations in your school? Another answer to critiques like this is that there should always be a human making final matching determinations. However, when we looked into other forensic technologies that use similar human backstops like fingerprinting, predictive policing and CCTV, it turns out that across the board, when there's uncertainty in the process, forensic examiners tend to focus on evidence that confirms their expectations. So from CCTV studies, we can get a sense of how well humans might actually perform as safeguards for facial recognition and research shows that observers who had been trained, the trained observers to identify individuals from footage made correct identifications less than 70% of the time. And that number drops even lower when observers are asked to make cross racial identifications to identify someone who's in a different race than their own. As a reminder, even if a person correctly rejects the misidentification, that student has already been exposed to additional scrutiny by administration, rendering them hyper visible for other critiques and sanctions that might have gone unnoticed. And it also opens up a new opportunity for human biases in enforcement and punishment. In a facial recognition pilot in South Wales, the police revealed that in the process of making only 450 arrests, the algorithms falsely identified over 2000 people. So we can get a sense of how big this problem is or it could be. And this is another way that technology is fundamentally part of human society. Students who are most susceptible to misidentification are also those who are the most likely to face outsize punishment if they are subject to scrutiny. Another issue is the question of who determines what level of accuracy is acceptable. When governments don't set the standards and we found in our cases that for forensic technologies they tend not to, courts end up being the main arbiter. So in the US trial judges in the federal court system and most of the state courts use a Debert standard to determine whether or not an expert witness testimony about a forensic technology is scientifically valid for the case. And that's done on a case by case basis. They consider things like potential error rates, the existence of a maintenance standards and the technologies reputation in scientific communities, but they do not consider, they do not have a minimum criteria for determining any one of these categories. So as a result, the accuracy of fingerprinting is ultimately determined in the legal system by the quality of the lawyer and the experts involved in a given case. So right now some states currently accept facial fingerprinting testimony as expert scientific witnesses, while others in the federal court system don't, suggesting that this is not wholly reliable. And it also creates essentially two separate standards of evidence, one for those with the means to mount a strong legal defense and the another for those without such means. Further law enforcement may still have incentive to use weekly support technologies if they are consistently able to get wins in court with them. So obviously there's a lot more to talk about, but what I'm getting at here is that accuracy is much more complicated than it seems at first. And these processes translate human biases into the software and into the system of the software is part of. Despite this, people tend to perceive as Claire said earlier, technology as objective and therefore inherently free from bias. So police have a long history of leveraging this idea of objectivity in court to argue that some arrests could not have been possibly been biased because they were based on an algorithmic prediction and that does tend to hold up in court. That can make these systems even more dangerous because stakeholders that might usually be sensitive to human biases can overlook similar critiques and safeguards when it comes to technology. So now I'm gonna pass it over to Molly to talk a little bit more about what we anticipate going forward. Hi, thanks Hannah, I'm Molly Kleinman. So now this brings us to our recommendations. Our essential recommendation is straightforward. Countries should ban the use of facial recognition technology in schools. So in all of our research, we were unable to identify a single use case where the potential benefits of facial recognition would outweigh the potential harms. And this is true for the kinds of in-person facial recognition systems that we were mostly considering in our research, but it is also true for the kinds of uses that are expanding now in the kinds of online education tools that are being used during the pandemic. So we have kids sitting in front of cameras all day and these companies must not be allowed to collect facial and other biometric data from children who have no choice but to use their products. Furthermore, again, in this COVID-19 situation, we're seeing other kinds of biometric surveillance expanding such as thermal tracking, and many of these systems have the same risks and dangers as facial recognition. And we believe that they also, it would be best if countries banned them. So in the absence of an outright ban, at a minimum, we are recommending nationwide moratoria that would be long enough to give countries time to convene advisory committees to investigate and recommend a regulatory framework. So, and when we say an advisory committee, we're talking about something that would be really interdisciplinary and it would include experts in facial recognition technology, but also in privacy, security, civil liberties laws, the social and ethical dimensions of technology, race and gender and education, and child psychology. And a moratorium should only end when the work of the committee is complete and the regulatory framework has been fully implemented. Separate from a moratorium or ban, countries should enact comprehensive data privacy and security laws that address facial recognition and other kinds of biometric data if they're not already in place. So in the EU, the GDPR does not explicitly address facial recognition, but courts in several European countries have ruled the facial recognition data is included under the GDPR's definition of personal data and therefore it is not permitted under the GDPR. We're not going to go over them all here, but in addition to these nation-level policy recommendations, our report also includes recommendations for state-level and district-level policymakers to help them provide effective oversight in the absence of a national ban or moratorium, which is the situation we find ourselves in right now. As we discussed earlier, there's very little regulation happening at the national level. So we hope that you'll take a look at the report and read these other questions. So as a part of that work, we compiled these lists of questions for individuals who might be dealing with potential implementation of facial recognition, including school administrators, teachers, parents and guardians, and students. So our goal with these lists was to help individuals ask critical questions so that they can make more informed decisions for whatever their zone of influence is, even if it just may be only a single school building. So now I think I was gonna hand things over really briefly to Shobita or Na to wrap up, and then we'll start taking questions. Yeah, sure, thanks, Molly, and thanks, Claire and Hannah, for participating in the presentation and talking about your results. So that, I think, is a pretty good place to start in terms of giving you a general sense of what the report talked about. As Hannah mentioned, in the report, we talk in much more detail, of course, about those recommendations and the different, each of these conclusions. We looked at a number of different, a number of different cases for each. So you'll find those resources in the full report, and we also have a shorter executive summary. And then finally, we have those maps that I presented available also as separate supplements. And then, as you folks are asking questions in the chat, I'll also just say that if you are interested in STPP and its work, you can visit our website, you see the URL here, and also you can follow us on Twitter. And then finally, if you want to keep up to date on what we're doing and get our newsletter, you can email us at stpp.umich.edu. So, Molly, do we have questions that we wanna... We do, they are starting to roll in. So the first question, I think this is a big one, but I think it'd be good to talk about. So someone has asked us, do you think that facial recognition should be banned across the board or just in schools? I'll start, I have, of course, I always have opinions, but Claire and Hannah, what do you guys think? I mean, based on what we learned in this, in our research, it's obviously, students are particularly vulnerable, but I can't really imagine a situation in which, in which the drawbacks of facial recognition are going to outweigh the benefits. It's going to be just as inaccurate in all these other places. It's part of the same feedback loops in all these other places. And a lot of these things, like feeling surveilled and avoidance, a lot of the cases that we looked at, we drew on cases that weren't just in schools. We learned a lot from people out in society and adults. And so a lot of these learnings, I think map pretty directly onto a lot of other scenarios. One thing, I mean, so I would generally say that I agree with that. What I would just add, I think, is that, of course, in some ways, this is one, of course, as someone who thinks about case comparison, right? Facial recognition use in schools is one case. It's a hard case, I think. And I agree that there's a lot of vulnerability among children that has to, therefore, I think the use of the technology in that context has to meet a really high threshold. But I also think that, you know, as I said, as Molly said, we've offered a lot of recommendations and they're pretty detailed. And I think that for perhaps use cases that are more complex, you know, use in law enforcement, more broadly, for example, or even identity verification, we need to, we've provided those resources that I think can be useful for those uses as well. So, you know, I think, as Hannah said, this idea that humans are part of the technology and we have to address that in some way, the idea of data markets. I mean, these are things that we all have to be thinking about in a really serious way, regardless of the use of facial recognition. And my concern is that we're not thinking about those sorts of things enough, regardless of the use. And you see that even most recently in the context of Project Greenlight in Detroit, which of course is not the use in schools, but a black man was misidentified because his image was captured and it was linked to some old driver's license photo. So that kind of problem that we're identifying, I think, is something that we see across facial recognition and uses. All right, so the next question I have. Given that private and public sector advocates of this technology are likely to use the charge of inaccuracy and bias as leverage to highlight how the tech industry is improving and training, or improving and training the human backstabs. Sorry, let's try this again. Given that advocates of the technology are likely to use this charge of inaccuracy as a way to highlight how the technology's accuracy is improving and how training for the human backstabs is improving and diversifying, the arguments against using the technology are being addressed and will soon be less relevant. So why ban its use outright? So I'm gonna take the director's privilege and say that I think that Hannah did a great job of answering that question. Which is that the fundamental point that we're trying to make is that it is impossible to reduce the inaccuracy in this technology. We cannot tech our way out of this. There is no tech without humans and society, and that introduces systematic bias, structural bias, and individual bias. So I'm just using different words than what Hannah said. So I just don't think that that's an argument. I hope that our report provides some details to explain why that argument isn't adequate, but that's a sort of top line conclusion that I think I draw anyway. So this is a question that's actually related to that one. So where do we think the attitude of technology's objectivity comes from? And is there any sort of shift that we're maybe seeing generationally in this belief that technology is objective? I'm seeing lots of nods. Yeah, and Claire, did you want to say something? Cause I jumped in last time, so. I mean, that's a great question. I definitely couldn't tell you where the attitude comes from. I mean, I think there's a belief in science, second public policy field. We talk a lot about the black box of science innovation that people assume what's done by scientists in a lab is perfect. I mean, you assume if it's informed by science and research is not likely to be inaccurate. So I think it comes partly from that black box fallacy. And this is also what STPB argues for that we need to get ahead of this and regulate these things and not just blindly trust that they're accurate. But I mean, I don't know exactly where it comes from and is it changing generationally? I think you're talking to not the most representative group to answer that, cause we would all say yes, but that's because we talk to STPB scholars all day. But I would like to think yes. I mean, obviously generations are just getting more and more technologically entrenched. And that makes me think that with further use, I mean, people use facial recognition every single day. And I think people, there is a better understanding that the technology is not perfect. I do wanna say that I think that some groups have always seen this as not objective. And so it's not always news to every group. I think in general, a lot of people, especially scientists in tech, when I was in tech, I think, I spent a lot of time in Silicon Valley and I still talk about my research now with people. And I do really always find myself, I very frequently still find myself bringing up, like having to bring up that idea that tech is not objective. So I don't see there being a huge generational shift at this point, but I do wanna point out that some groups who have often been harmed by things that are called objective have always known that it's not objective. And so the idea that we're just now learning this is really like we're just now able to elevate this using it academically. And not just now, this has a history and ST in science, technology and society studies. And we're just now seeing some of this get elevated in a more mainstream way. So I think that there's actually a lot of power in the idea that your discipline is objective, the idea that if we can build a new facial recognition system or if we can build a bailbound algorithm, the idea that it's objective gives us a lot of power to not only avoid some questions, but also it gives you kind of economically a lot of power to get funding for projects like that. There's a lot of power that goes along with that objectivity. So I think that that doesn't mean it's intentional that people come into, that people are trying to build up that myth of objectivity, but I think it's a product of over time. There's a lot of funding that's condensed around that. I'm calling this next question for myself. What do we make of tech that uses facial recognition not in the name of security or law enforcement, but more for specific teaching and learning contexts that are often driven by faculty demands such as online exam proctoring. And I don't like those either. I think anytime you have a situation where you're treating your students as probably trying to cheat or as probably criminals, you're disrupting the relationship with your students. That's not a pedagogically sound way to approach education. And I would argue that if you need this kind of policing technology in order to assess your students, you need to think about assessing your students differently. And I realized that's a lot easier said than done and that there are disciplines where it can be difficult to always come up with the kinds of assessments that would work in an entirely remote situation. And of course now we're dealing with a humongous expansion of remote education that people weren't prepared for and these tools can seem really convenient and like it's gonna make life easier in a time when nothing in life is easy. But I wanna think about for whom is it making life easier? And I wanna center the students experience when those kinds of technologies are being used. I don't know if others wanted to add to that. No, I think you, I mean, no, I'll just, I guess I'll just briefly say that where we are, facial recognition technology is already being used in schools across the US pre-pandemic. And then as you said yourself, it's only expanding now and it's expanding at colleges and universities too. And I think the things that seem like seductive solutions, there are actually other kinds of problems potentially that we need to be thinking about. Exactly. So this next question says, I'm from a place overseas where the government is beginning to roll out smart sensors capable of being used for facial recognition. And there has already been resistance locally. Are there any simple to understand resources that we could recommend that can be used to spread awareness of the issues raised? I mean, so our report is a great place to start. There are some questions at the very end that are tailored for individuals facing these kinds of deployments. And it's focused again more on the kinds of questions that they can ask and less specifically about advocacy, but I think that it's a pretty short step from some of those questions. Yeah, I would just add at the end of our report too. So we have the questions that Molly talked about that can sort of equip hopefully articulate some of the kinds of questions that people might ask. We also have, I believe it's on the website, if it's not currently, we'll be sure to add it. We have a one pager that's sort of a one page summary of the five conclusions that might be good, just a one pager to kind of distribute in terms of resources. And then finally, at the end, before the references, somewhere in that report, I know it's lengthy, but all of the sections are hyperlinked. At the end of that report, we, Claire, gathered a number of resources. So think tank reports, but also advocacy groups, others who are doing work in this area. And so that might be a useful resource for you to find out. And we tried our best, I do international research. So I kept pushing everybody to make sure that we were thinking about these things internationally. And so there are some international resources there that might be useful for those folks around the world. So a number, this is another question, a number of technocratic solutions are focused on diversifying datasets to better identify black people, which unfortunately represents a form of predatory inclusion. Are there strategies that policymakers and advocates can use to highlight the racist aspects of many of these surveillance technologies? Hannah, you wanna take that one? Strategies that we can use. That stuff, I did see a lot of that. I mean, I know that while we were doing this research last year, there was a big story that came out about Google paid a company, it had this critique about not being good at identifying black faces and it paid a company to go out into Atlanta and give homeless people $5 to use a, supposedly use some kind of phone game that they were actually doing was capturing their faces with a very complicated consent that really they were not explaining. And so obviously if the response to the criticism of your being racist is to go out and do something like that, that can almost be, it's hard to say what's worse, it's very bad, it's also a bad outcome. I'm not sure what I would suggest as a solution. I think one of the things to understand is if we can get this idea out there that those kinds of simple like adding more people to your database is not sufficient to fix this problem that it would take a societal change to fix this because these technologies are part of, they're part of society and they're totally inextricable from that. When you start to see those kinds of actions you can very clearly say that company is not ready to make a not racist technology. So I think that I don't have a specific solution should be the way that you do but there are, but certainly I think a better understanding generally of how that technology does not and will never stand on its own may be helpful in setting up the success in advocacy battles like that. So I am often accused of being a pessimist when it comes to technologies but I'm gonna try my hand at being an optimist. And also maybe kind of self congratulatory a little bit. But I think this is why when you see the report we talk about a ban and then we also offer policy recommendations. It's because on the one hand, our instinct is to say there's something deeply wrongs systematically and socially that can't be fixed really easily. And so we really need to think about stopping this full stop. But we also understand what the real world looks like and so we wanna address it. And so when you think about a question like this and I have to say I'm gonna write down predatory inclusion because I think that phrase is really evocative. But I think that my initial instinct is to say we can use the kind of analogical case study approach that we're trying to pioneer here. And as I was thinking through this I thought to myself about two kinds of examples. So one is the infrastructure for human subjects research. It is certainly not perfect. It has a lot of problems but when you think about the inclusion the sort of dealing with exclusion by some sort of forced inclusion or preferential inclusion or the creation of messed up incentives well, we have a human subjects research system that has evolved that has certain kinds of institutional buy-in that we can take as something that has worked in society and maybe we can innovate beyond that, right? So maybe that's at least one place to start if it's not a perfect solution. One of the ways that people have found that not to be a great solution is the fact that it relies on individual consent and not community consent. So then there have been in recent years methods for trying to get community consent when it comes to biomedical research. So that's another place where we might say, okay, we can think about how might we get community consent when it comes to issues like trying to diversify data sets. Of course, again, the diversification of data sets is not gonna solve this problem, but if that's what we're thinking about we can use these kind of analogical cases. And again, I also wanna emphasize one of the things that I'm trying to do when I think about this is to look across tech sectors and to look historically. That is key because I think too often we're siloed in different areas of technology when we actually can learn across them. And one of my hopes is that in this kind of effort that we can sort of start to look at examples that might look like they're really different but actually can teach us some interesting things. And I think we're at time. So we have some questions we didn't, we weren't able to get to today. We might see if we can find a way to follow up with folks to answer those questions. Please feel free to reach out to us and get in touch. Our contact information is all in the report. Should we, I don't know if you had any last words. Thank you all for coming. And as Molly said, you know where to find us if you have further questions. Thanks everyone.


All right, hello track chairs and welcome back to phase two of our track chair selection process So I'm logging in I've put myself as a track chair for case studies temporarily, of course and I'm going to go ahead and log in and take a look at The various presentations so you can see that I had already marked this one interested note We have our our keyboard shortcuts here so arrows will take us left and right and I can mark a Presentation guess Interested or no, thanks using keyboard shortcuts So I'm just going to stick these as interested Interested just going to run down with those really quick I'll say no, thanks and of course I will change all these back before The end and here I'm going to go ahead and select a couple. So these are the ones I like It's just a quick note that You can now filter by the ones you've selected You're interested in No, thanks in case you want to go back and review in fact, I'm going to call that one interested again just for kicks And remember I can always sort by team selections and the ones I voted on So, okay, I've made a few selections now. I'm going to go and take a look at What I'm up to and what my team is up to so you can see here I've got a few that I'm interested in And I've got these two selections now if I want to I can go ahead and Add this to my team list. Okay Remember the team list is Up to you and your team. So everybody gets together to decide on what's best for the team So if the team decides now, that's a terrible idea We're going to go ahead and remove it if I decide actually I'm not so into the selection I'm going to go and remove it from my own list so there we go and I can also clear out all the things all the decisions I've made up to this point so that I can have a clean record should I desire Remember that you can also contact your fellow track chairs through the track chair directory You can export this if you'd like and you should be able to Send an email directly to any of these folks so That is About it Change requests can can still be made although it's a little dodgy since people have already voted So do try to be careful about that and make sure you're communicating that back to your track chairs additionally You can still email speakers if you need a little bit of clarification on something and you can still leave comments for Your fellow track chairs You just leave a comment here, and then it will be noted Here that comment has been left as we can see for this one Someone left a comment here asking if this is a duplicate I believe we took care of that, but it's possible that it still is I Think those are the basics for now If you have any questions, please send them to speaker support at open stack.org and Most importantly if you should desire to combine presentations remember that there are a limit of Three people per presentation if you want to set up a panel We ask that you have at least one female on the panel and remember that panels are limited to Four speakers and one moderator. All right. Thank you very much for listening Again, if you have any problems speaker support at open stack org or feel free to use the track chair mailing list Thank you so much


In the last class, we were talking about the braking distance. We had looked at how to calculate the braking distance. We had derived from a very simple dynamics relationship what should be the distance that the vehicle would travel when we go from velocity v1 to velocity v2 or v2 to v1. Now, what we are going to do is to in order to calculate the braking distance, we would now integrate it or rather v2 to v1 or I think that is what we wrote. We would like to look at what would be the braking distance when the velocity or the final velocity is equal to 0. Obviously, you know it. So, now you can write down the integration what we did and then you can calculate what is the distance that you would travel before we break the vehicle. Now, this is the formula which we wrote. If I remember right, this would be the I know whether I think that this is how we had given. So, that should be v1 to v2. I think this is how we wrote. vdv divided by fb plus frw, we wrote also cos theta s and you can make cos theta s equal to 1 plus or minus w sin theta s plus ra. We said ra is the aerodynamic forces and we just replace this with cae into v2 and we said that v2 being there that will have an effect on integration and you can integrate this straight forward. But there is nothing very difficult about it. Cg to cae ln of fb plus frw cos theta s plus or minus w sin theta s plus cae into v1 square divided by fb plus frw cos theta s sin theta s plus cae v2 square. Just quickly I am not going to spend time. You can do that. Then this is the distance traveled when the velocities vary by v1 to v2 or v1 goes to v2. Now, putting v2 is equal to 0 which is actually the stopping distance when the final velocity is equal to 0. This equation can you know you can write that equation to be. So, that gives you the stopping distance that you would get. Remember why we wrote that gamma that is for rotational inertia and other things and we also defined what is called as the efficiency of braking. Remember that we said the efficiency of braking is simply that all the f that is to be spent goes to braking the vehicle or in other words we said ma or m into the force that is the deceleration should be equal to w by or w by g. Let me write that as w by g into d should be equal to if you remember w that is the normal load that is acting that is the weight multiplied by mu. If you remember mu is equal to d by g and we said that that is the maximum efficiency. So, that is the reason that is one of the things that we wrote. You can also say that the maximum efficiency is when the braking force is such that it has already compensated for all the rotating inertia. In other words, gamma B is equal to 1. It has compensated for it and the rest of it is available for in order to break the linear case. So, in which case we can also write down the minimum braking distance. I will call this as minimum braking distance where we have fB replaced by mu into w that is the braking force. So, this can minimum braking force let me write it here. Let me s is equal to gamma B w divided by 2 g CAE natural log 1 plus CAE into mu and squared mu into w because that is the minimum all the forces go to plus fR w break the vehicle plus or minus w sorry sin theta s. So, that gives the minimum braking distance. I am just copying it. This is nothing very difficult. If there is any questions on this, all of them are just forces. I just did not want to leave any of the things and that is the reason why I am carefully writing it, but concept wise they are all very simple. So, that is the minimum braking distance. One of the things that is important well now a lot of research is going on is on the time lag between a driver applies the brake and the forces realized the braking forces realized. This becomes very important because there will always be a time lag. This time lag is due to right from application, we are talking about right from application to the point when the forces felt at the brakes and the brakes start acting. Delays are due to so many reasons here right from the pedal motion to the hydraulics that is involved or the fluid that is involved and so on. So, I just want to point out that that time lag need to be understood and taken into account if you are looking at braking distance. So, what we are talking here is the braking distance when the force is felt, but it is not from the time the vehicle or the driver applies the brake and then starts braking. So, usually that is a factor should not go into the details, I just want to point out that. So, we have covered quite a bit. We now know about acceleration braking in a passenger car. We will slightly deviate now, not that we are going to go away from the launch to the dynamics, but we have to learn lot more in launch to the dynamics. We will talk about tires, we will talk about aerodynamic forces and so on, but before we go there, let us complete the topic by looking at a tractor trailer. This is a very important vehicle today, especially in a country like India becoming very popular. Of course, the number of axles increase, when the number of axles increase, I would not be able to do the kind of calculation that I do here because the problem is not statically determinate. So, we are looking at it only from point of view of deriving a simple equation on forces acting. Hence, we would restrict ourselves to a tractor trailer where you will see that the trailer has one axle and that the tractor has two axles and that the tractor and the trailer are connected by means of what is called as the hitch point. There are a number of terminologies for this. Sometimes people call this as 50 or 50 or 50 or 50 or 50 or 50 or 50 or 50 or 50 or 50 or 50 or 50 or 50 and so on. So, we are, that is the hitch point. Let us understand what are the forces that can act on this. And as I said in the last class, the reference for this is the theory of ground vehicles by Wong. So, we will move away from this after this class to look at, let us say forces that are opposing, exactly the same thing. There is no difference in the concept, absolutely the same, but only thing is that when I take moments, there are not more forces that come into picture, okay. So let us just recapitulate what are the forces that are acting for the axle rating vehicle. One of our great interest is to find out what are the reactions that take place in these axles. What we would call as front axle, what I would call as rear axle, I have to be consistent. If there is any confusion on the terminology or on the notation, please let me know, I will be as consistent as possible W s. Now what are the forces that are acting? Of course, the aerodynamic forces, there are two aerodynamic forces that we take into account. One that is acting on the tractor. There is a tendency in the industry to call this also as a horse. They call this as a horse because it is something like a horse drawn carriage. So when they say horse driving a trailer, okay, do not be carried away by it because it is a loose term or a colloquial term that they use, okay, that is called a horse. So that is the RA1 is the aerodynamic forces that are acting, okay, on the tractor. The similar aerodynamic force acts here and that is RA2. The other forces that are acting, you know it, the famous rolling resistance force. So that acts everywhere on all the three, okay, the rolling resistance force. D'Alembert's force for acceleration, for deceleration it will just be opposite, same thing. The other force which is important are the hitch point forces, okay. Simple mechanics, the hitch point forces, directions you have to be careful, okay. You can look at this from two perspectives, one from the tractor perspective and the other from the trailer perspective. So these are all very simple mechanics. You can understand that. So you be careful in putting down the hitch point forces as they are called. Remember that we are considering the weights and then D'Alembert's forces, the tractor and the trailer separately. So that becomes the weight of the tractor and this becomes the weight of the trailer and the CG locations are written as H1, H2, right. We will make some simplifying assumptions. Not that it is necessary, so that I need not write the equation in a full equation on the board, I will make some assumption.  So any questions, ask me. So I am going to just write down the final equations, okay, telling you how I got it. You can derive it yourself straight forward, okay. So the first task for us is to find out what are the reaction forces that are acting onto the spake. Once we finish this, we will also go to the breaking which is very interesting, okay and that is the next step we are going to do. You can easily write down, why not you start writing down what would be the reaction forces that would result or that would be the result of this kind of arrangement. Note that I have put a point A, it is customary to take the moment about that point many times in order to find out the forces, okay. So let us do that, let us say that Ws into L2, I would take Ws into L2. Please note what I am going to do is to separate out, separate out, in your mind you can build this up, separate out the tractor and the trailer and then, you know, put the free body diagram. This is, this is just combined, I just put it like this. As I told you, this is important. Separate out the tractor and the trailer, when it is accelerating, of course, Fhi for the trailer will be in the other direction, okay, Whi won't change, okay and vice versa. So or in other words, there will be a support here for the trailer, okay. So note that and accordingly change it, okay. In other words, when I am considering the trailer part of it, this will be in the other direction, this will be in the opposite direction and vice versa. So well set, very simple. I am going to write down only the final equation for Ws, okay. This will be a good exercise for you. Verify it, okay. I am going to just make a small assumption so that I don't need to write down a lot of things. So I will write down H3 H2 L2 into W2 plus H2 W2. Note I am taking all the moment about the point A which I have defined, the hitch point forces, okay, that is the force which actually pulls the trailer. So that will be opposed by of course all the other forces so that the hitch point force can be written as, that is the, be very careful with this plus and minus. I don't want to every time give an explanation and confuse you but you would know how to write that, okay. So I am not going to do that but be very careful when you put a plus and when you put a minus. One of the easiest ways of doing it is that when it is going uphill, okay, accelerating, it is acting in the same direction as that of the RA toward the accelerating forces. When it is coming downhill, okay, the forces will be in the opposite direction. So the direction of forces is determined whether it is a plus or a minus, okay. Now using these two equations and substituting for whatever is in the brackets, here I can write down Ws to be W2 into D2 divided by L2 plus FR into H2. That is the reaction that happens in the trailer, the rear wheel of the trailer. Note that, note that these vehicles are rear wheel driven or in other words that is the wheel which has the drive and braking happens in all three wheels. So braking forces are applied to all three wheels. In other words, the driven wheel is this but the braking wheel is all three and so there is a distribution of the braking forces between these three wheels, okay. So the load at the hitch point, of course, all of you know how to calculate that. That is the W2 minus Ws because as I said the trailer is supported now by the rear as well as at the hitch point and so it can be written as W2 minus Ws and that is equal to 1 minus D2 divided by L2 plus FR into H2 into W2. So maybe you can call that, that equation is 1 and that equation is 2 and that equation is 3, that equation is 4 and this equation is 5 and so on. We can write down this as a coefficient and call that as CHI, we follow Wong theory of ground vehicles and just write that down as CHI. So now we shift to the tractor. We consider tractor as a free body and draw the free body diagram for the tractor, do exactly the same thing and find out the reaction forces in the front and the rear, okay, again by taking moments and so on. So WR into, here it is, you need not take it about A. So WR into L1 is what I am going to take. So let me write the final result, W1 L1 plus RA1 into HA1 plus H1A W1 by G plus or minus W1 H1 sin theta S F HI H3 plus L1 minus D1 W HI1 HI divided by, okay. That is WR. Again you can write down, you can apply equation 1, let me call that as 6 and you can simplify this equation, okay. Now the technique is the same. So what is the next step? We are going to simplify this by looking at the longitudinal forces and hence the longitudinal force F, the balance of the longitudinal force. What is the longitudinal force here? Traction force, okay. So the longitudinal force which is a traction force has to now equilibrate all other forces that are acting which is again very straightforward. You can say that that is RA1, I am not going to write down that equation, it is very simple and then it is, that is the next force, okay. Then W1, let me write it like this, so that W1 that is the next force, okay plus of course the rolling resistance force, okay. So we can, yeah, plus, remember that we had something called a drawbar pull and we removed it but now it becomes very important and this is very similar to the drawbar pull which we had used in the last class, in one of the earlier classes. So that is the force that you have. You can substitute it and you can determine what would be the force of the, we can calculate what should be the maximum force, okay. Ranging into this expression, the previous expression, I can simplify this equation, okay, rearrange them and so on. I am going to skip that, rearranging, writing down and so on and I am going to only write this. The maximum force that can be supported by the tract, by the attractive effort or by the rear wheel is obviously equal to like what we had written, we are lumping all the effects of the tire into one number called mu into WR, so the maximum is given by f max is going to be, so mu into WR, substituting WR, simplifying all these equations, I can write f max to be, I am writing the final form, okay, will be equal to mu into L1W1 minus H1 into FR into W1 plus CHI, so that gives you, that gives you the maximum force that can be supported by a tractor, semi-trailer, technique is exactly the same, any questions? Yes, yeah, we are assuming all H is to be the same which is not correct, okay, I agree. This is just to write down laziness of writing down the equation that is all, okay, you can write down that more elaborately putting down HA2, obviously, obviously HA2 is not going to be equal to HA1, HA3 is going to be very different from H, sorry, H3 is going to be very different from H2, it is going to be different from H1 and so on, I agree. Just to write it down, this is not correct, okay, you can, you need not make that assumption, there is nothing in that assumption, you can substitute it and write down the final form. The equation will become long, concepts are simple, nothing will change, clear? You are absolutely right, that kind of assumption is not, is only for a classroom deriving the equations and in practice that would not be correct, okay. So that is for traction, we quickly finish breaking in this class so that we will move forward because it is longitudinal dynamics, we have to move, we have to accelerate. So we will look at breaking here. Any other questions, anything? Okay, again very simple dynamics is what we are going to use for breaking, okay. What we are going to do now is to divide this into three categories, we are going to look at tractor, we are going to look at the trailer and we are going to look at the tractor trailer together. So that is the first thing that we are going to do. We are going to get, in other words, we are going to get three sets of equations by considering these three systems or these three scenarios. That is the number one. Number two is that we are going to look at sigma fx, sigma fy in the moment about an axis which is perpendicular to the board. So in other words, for each of these scenarios we are going to get three sets of equations, okay and so we are going to get nine equations. Then we will see how we can simplify it so that we can find out what is the loads that are acting or the normal reactions at the three wheels. So this is the strategy. Why are we interested ultimately or what is our goal? Our goal is to find out what would be the reactions in the wheels. Our goal is to find out these three, right. Why are we finding this out? Because we want to make sure there is a locking sequence. So the most important thing in breaking is what happens when these wheels lock. So I have to find out when these wheels lock. So I have to find out what these are. So that is my first step. That is why I did all these things or I am going to do all those things. Please note that when I look at all these three Wf, sorry, three Ws, Wf, Wr and Ws, I will multiply this with Mu in order to look at the maximum breaking force that is possible before the wheel locks. So in other words, derivation is pretty simple. In other words, what we are ultimately interested in is whether the front wheel locks, the rear wheel locks or the trailer wheel locks. These are the three things, right. So in other words, what can be the sequence at which we can have locking would determine how we are going to distribute the breaking forces, clear? Now what happens when the front wheel locks? The situation is exactly similar to what we had before. We are going to lose directional stability or directional control. So we are going to lose directional control and the vehicle is going to go forward. What happens when, remember that scenario, it is exactly similar when the rear wheel locks. When the rear wheel locks, remember that the whole force goes to the front and use yaw and so directional stability is lost. Now what happens here in this case when we have this wheel, it locks. Here in this case, what is going to happen is very interesting. The second one, so let me come back to this. When this locks, let us see what happens. Even we saw that as directional stability but here there is going to be small difference. Let us see what happens. I am going to break this and there is going to be, this guy is now moving forward. He is not, because if he has to be stopped, that has to be stopped and so on. So when this guy locks, more important effect is what is called as jack knifing because this kind of yawing which is interwheeler has to be looked at more closely because there are two forces that may be acting on either side. So may or may not happen, we do not know. But when this locks, there is going to be jack knifing, one going into the other, that would be the case and that would be a more important or dangerous situation. That would happen but we cannot talk about stability because that is for two-axle passenger car, it was very straightforward. So when this locks, there is going to be jack knifing. One, there will be a difference in the deceleration between the two and so one drives into the other. What happens when this locks? When this locks, there is going to be, please note that it is at the hitch point, this is something like supported here and this has a hinge there and so when this locks, this whole, this is going to sway and that is going to be dangerously swaying perpendicular to the boat. So the effects are such that each one of them has is important of course. One is more dangerous than the other two. Of course I said that locking in the front is the least of this danger. Why? Because the driver will be able to find out that he is going to lock or his wheel is going to lock. So he has a feel. So he will be able to correct it. So that is the least of the problems. Jack knifing is the worst and this is also very dangerous especially for vehicles which are coming or which are in the road, other vehicles because the whole trailer is going to sway. So this is, it is very important that these concepts are taking into account. So which locks first? So I would not allow this to lock. This would first lock, no issues. Why are we talking this? Because we want to distribute the total breaking forces. So this fine because I can find out what is happening and then this locks and then finally this locks would be a situation which would be useful in order to decide what should be the breaking forces. Of course this is only to understand the concepts. If you look at ABS which we will quickly run through, after understanding the behaviour of the tires you would appreciate the ABS in vehicles because this kind of situations, locking situations, how the ABS handles this kind of locking situation becomes important and the concepts will be very well understood at that point. So now what is my goal? I just want to write down the three sets of equations. I will do that right now and then write down what would be the W's that would happen in these three axes. So I am considering first the tractor. So let me consider the tractor first. The tractor WF plus WR, this is the, I said the sigma of forces equal to zero. This is the vertical forces that are acting. So WF plus WR, there are two vertical forces, the weight plus the hitch point. Then we will consider the breaking forces, the breaking force which acts in the, this would, we will consider that as the x as usual and the other one as the z direction and that as the y. So this is the x direction forces, the breaking forces. So CR into WF plus CR into WR, that is the coefficient of breaking is equal to as I said D by G into W1. Please note that in breaking F is in the opposite direction. So it should be plus into FHI right. So that is the second equation and the third equation that I am going to write is the moment equation in the y direction. So that is the set of equations for 1, 2 and 3 for the tractor. We have similar set of equations for the trailer, sometimes called as semi trailer because there is one wheel there. So anyway I will call this as trailer and again the same set or the same directions is what I am looking at, nothing difficult, the two directions that is the 4, 5. Then I said that I am going to take the tractor trailer combination and write down another three sets of equations. That is in the z direction, WF plus WR plus WS is equal to W1 plus W2 and then the total breaking force that is acting, those are what I would call as the breaking coefficients is equal to the, that is my F is equal to MA equation. Of course you know that D is the deceleration. So the signs are taken into account. Once I say that, that is the deceleration. So that is 7, 8 and 9. So these equations are now solved in order to get WF and WR. As I told you I am going to write down only the final expression. So the tractor, so I have to find out WF, WR and WS and I am going to just write down the final equation for this. You can derive that, the final equation for WF is a long equation multiplying, substituting and all that doing all this. I promise that there is going to be a long equation and it happens to be. Now we will write down WR, so obtained from these equations, WS, that means to be W2, that becomes 12. So we need to of course know CSE and CF and CR and so on in order to solve the equation. The key equation is this. Once you know this, you can go back to those 9 equations in order to find out WF and WR as well. So you need not go through this. You can as well from this equation, once you know WS, you can go to one of those 9 equations and you can determine WF and WR. So we will stop here just for you know assimilate this. We will quickly maybe spend 5, 10 minutes on the distribution of the braking force in the next class and then we will move to tire dynamics. So we will stop here for today.


Hello and welcome to the John Stiltrager show presented by Buffalo Wildlings. I'm Greg Royce, joined with Stevenson Head Swimming Coach John Stiltrager. And Coach, it's the one year since the start of the year and probably since last year this time. We've been working towards and that's conference championships. So start tomorrow, Thursday evening, one session and then Friday, Saturday, Sunday. So how do you feel going into it? You're always a little bit nervous. Swimming is a little different than a lot of other sports in that we work towards really one meter of the year unlike other sports that might work hard a couple of days and then relax a couple of days to win a game or a meet. We work from September all the way through for this one meet only. Yeah, absolutely. That's what we've kind of talked about all year, kind of built up this moment. So obviously you've had a couple of weeks since your last dual meet. Done your shave, done your taper. Are kids ready to go? Are kids ready to go? They're a bit nervous. You can see the changing them from mid-January where they're tired and beat down to now where they're kind of excited in the pool and their attitudes are much better and you can see that the rest and the taper really start to take hold. Yeah, we've talked about a little bit like last year, kind of surprised some people finishing fourth on the women's side. Probably not going to sneak up on anybody this year but at the same time I think there's still maybe a little bit of an element of surprise that maybe you can shock some people with your performance this weekend. Yeah, we're going to have to show them I think this year that we weren't just a one-year wonder in that what we did last year wasn't a fluke and that's our goal is to go up there again this year and show them that we're for real and that we're going to put up some fast times. Yeah, absolutely. So what are you kind of looking for going into the meet? Obviously you've kind of looked at where everybody swam over the course of the year so what are your, not predictions, but kind of thoughts of where the team could finish? More on the women's side, obviously the men's side, more where individuals could play, it's more on the women's side, where do you think you could follow the team? Again, I think we'll be right in the same area that we were in this past year like fourth to fifth or sixth but you know I guess what we're looking to do is set the tone early with the first event of the day or the first event of the meet which is the medley relay. Last year we got second, you know we're hoping to repeat if not do better than that next year or this year and again really set the tone let the kids know that yes tapers here and that we're ready to swim fast. I think that's the big thing we did last year was, again we surprised people with our medley relay. This year I don't think we're going to surprise anyone with it but I think we want to surprise them with the time that we do. Yeah, absolutely. Obviously getting out to strong start in any multi-day events is huge. Yeah, I mean again setting the tone early is what's really important and again I think we had the chance to do that early on Friday night or Thursday night and then really start taking the individual events on Friday. Alright coach, we wish you the best of luck this weekend and we'll recap it all next week. Great, thank you very much. Alright, for John Soltrager, I'm Greg Royce. This has been the John Soltrager Show.


What's up everybody? Jason here with jacobyscasebrakes.com. The 12th team round of number block Phil's Spectre Pikachu number six This includes the Bulls, Warriors, Celtics, Nats, T-Wills, Hornet, Sons, Blazers, Magic, Raptors, Nuggets, Pacers, and Kings And again works like any other random number block break on our website But just for these teams in this specific break and 10 people will get a number from 0-3-9 Of course if the card is not numbered it'll be randomized at the end of the break in one lot But again, I think you guys know how this works Let's get a new dice roll. Here's a customer name from Matthew or from Sean all the way down to Jeremy and then 0-3-9 there So good luck. Let's roll it and we got a six and a two eight times one two three four five six seven and eight Brayden without a brownie Jason down to zero Brayden with a five Brannon with two one six So that'll be a number one any one-to-ones for these teams Brannon Jeremy with lucky number seven last one Hold your brownie one nine Sean with three Matthew with eight Adam with four and brownie of zero So any in our redemptions. So there you go guys coming up next is Spectre Pikachu number six


Welcome to JSA TV and JSA podcast, the newsroom for telecom and data center professionals. I'm Laura Nolan, coming to you from ITW 2020. Joining me today is Piyush Shrivastava, founder and managing director at AirX Technologies Limited. Piyush, welcome to JSA TV. Thank you so much, Laura, for having me here. I'm really excited to be part of the show. Thank you. We are glad to have you. Well, let's get started. For those who already don't know about AirX Technologies, could you tell our viewers about the company? Sure. Thank you. AirX is UK based organization. We started back in 2017. So, I founded the company with the view of solving the gaps or the problems of the telecommunication industry, where the last mile is the key problem. This is the challenge for all the key global telecommunication organizations. So, people who got really good network do struggle during the supply chain or accessing the local last mile network from the local ISPs. So, the company is really founded on the network. So, the DNA of the business is really the network services. Myself, you know, comes from the background of telecommunication. We've got a team in UK, in India, in Prague. Operations sits back in India and Prague, I would say, in Czech Republic. And management and the sales is in UK. So, AirX is really about providing the connectivity services globally. With more than 500 partners, we can access to over 180 countries around the world. We keep on growing our supply chain. Product and services are pretty simple and straight. We do not own and operate any infrastructure of our own. So, we are pretty virtual in this age of virtual era. We need to keep things simple. And our services are around layer three to layer two and layer one services on time to time. Connectivity to the data centers, to the enterprise sites, to any of the remote locations via fixed line or wireless services. So, product range are pretty much DIA broadband, 3G4G, near to Ethernet services. And that's pretty standard in the industry. The key important thing of the AirX is really to help business connect to their enterprises with any of the mediums where they are struggling. We, as an organization, when I started the business back in 2017, found an interesting area where we can also be seen as a enabler of the connectivity and IT services. With the help of NECO, when I started the business, we found that's a really good area where we can be of the IT connectivity. And David, Mr. David, who has started the business back in 2007, I would say it's been almost 10, 13 years. And that's really his key area is to develop IT services, which was focused around equipment services, SDVAN, getting engineers on site, fielding genius maintenance. And in 2018, we decided to join, you know, to marry each other, to join the forces together to help and support our customers, not just around the connectivity, but also to be served, you know, as a enabler of the SDVAN or a managed service provider to these telecommunication organizations. So that's really about AirX. And we're excited to part of the ITW and the GSA TV on today. Tremendous growth and reach demonstrated by AirX already. So could you tell us what sets AirX apart from others in the industry? So this is the question that has been asked by many of the companies that we work with. And that's a very initial question. We are very easy to work with. So I would say two simple things about AirX, how we see ourselves different than other corporations out there in the market. One is our focused customers, or let's say partners, are really telecommunication organizations. People who own the infrastructure, people who are the managed service provider, SDVAN companies, we are not the company for the enterprise. So we do not deal into enterprise segment. Our really focused market is towards the channels, the partners that we work with. And that's really differentiate ourselves to avoid that conflict of interest with many of the organizations which all the time has started up approaching enterprises, though, working with the same companies or the telecom partners who are the same customers. So we try to avoid that in this market where people do trust us. Second, I would say is the part of the NECO, which brings a lot of capability in-house for us where we can bring not just, as I said, the connectivity part, but also the ICT services. So that's really, I see a key differentiator within our business to expand any regional provider to go global, not just with connectivity, but also with the SDVAN solution. Wonderful, wonderful. Well, do you have any recent news or updates that you can share with our viewers at this year's ITW virtual event? Indeed, we just announced on the day one about a kind of a promotion, but I won't say it's a promotion, but it's a joint partnership to help businesses with the current coronavirus of COVID-19, in fact, where businesses are struggling to get a customer site up and running and customers are struggling with the losses because of the current situation as well. So we have launched a news to offer a three months 4G service in Europe and US for free without any charges to our customers who got services, fixed line services with us, and who are ready to launch their sites within seven or 10 days time. So we can really deploy these solutions across Europe and the US for free 90 days and they just can relax upon the fixed line services while this lockdown situation gets over. So that's really exciting news that we are bringing up. Very exciting. So where can our viewers go to learn more about that and all about EREX? So we are pretty much on LinkedIn. We are quite being seen on LinkedIn. People can go on our website as well. We have our website, EREXTechnologies.com. We are also time to time attending events like IDW capacity, Vans Summit. So we are really focused into attending these telecommunications industries conferences and we are there on the exhibit list as well. So people can find us very easily and myself and my colleagues are always being there. So yeah. Well, thank you Piyush for joining us. It was so great to get to know you and learning more about EREX. So thank you so much again for joining us. Thank you so much for having me here. It's been really nice talking to you. And thank you viewers for tuning in to JSA TV and JSA Podcast. Happy networking.