largely subpar when it comes to this compared to really dedicated humans doing this. However,


[ML News] Cedille French Language Model | YOU Search Engine | AI Finds Profitable MEME TOKENS

they're probably going to be better than just the developers doing it on the side as they're coding


the website under time pressure. And they're certainly going to be better than nothing at all.


Like I get it, the web sucks for visually impaired people interacting with a medium that is this


visual when your visuals don't work is bad. It's it's a bad experience. And it large ins the divide


between people who have good vision and people who have poor vision. I get this. And they also


get that we want to make an effort as a society to include visually impaired people more to make


websites more accessible, and so on. But I don't see when the standard has become that unless a


solution works 100% of the time, a lawsuit should be filed, like surely having a crappy AI annotated


website for visually impaired people is better than not having an annotated website at all.


On the other hand that you can absolutely see that if we as a society decide, well,


just use the AI tool for this, then companies are going to opt for that and actually avoid putting


in the work of making websites really accessible. So it is a hard problem. And I don't have the


clear answer for this. But I would certainly say that AI technology can help it's better than


nothing. It gives you sort of a lower bound on accessibility on a website, even if there are


some mistakes, because humans make mistakes too. But here is what I find funny. There is apparently


a document a sort of petition where researchers and companies and so on can put their name to ask


other people to ask other companies not to use these AI tools. It says signers include contributor


to w3c guidelines and employees at Microsoft, Apple and Google automated detection and repair


of accessibility problems is not reliable enough to bring a site into compliance the document says


accusing some vendors of deceptive marketing. And here it comes. The site was started by


Carl Groves, founder of the accessibility consultancy tenant.io who provided a withering 35


page analysis of accessibility software to Murphy's lawsuit against IBOBS. So IBOBS company being


sued, they used accessibility software. And now this tenant.ai Carl Groves has written a 35 page


analysis of this software growth set he surveyed a total of about 1000 pages from 50 websites using


the startup that's accessibility technology and found a median of 2300 violations of w3c guidelines


for each site. Here it comes. Groves says that this is a significant undercount because most


of the guidelines can only be checked by an expert manual analysis. So wait, did I understand


this correctly? Did you analyze 1000 websites and you either automatically or by a non expert humans


figured out a lower bound on the number of violations to the standards and that's not


actually the standards, but it's a lower bound and therefore it's better than nothing at all.


Really, you did that and you provide that as evidence into a lawsuit hypocrite hypocrite


hypocrite hypocrite hypocrite hypocrite. In his report to accessibility growth site and an image


of a model wearing a white dress for sale on an e commerce site. The alternative text provided


apparently generated by accessibility technology was grass nature and summer. Oh no, an anecdote.


Wow. And there you have it. The true story here is that complaining is easier than doing and will


always be able to write articles about AI systems that don't work 100% yet. As I said, I don't have


the definite solution to this problem. It is a hard problem. It's a balance between pushing


technology and making it accessible to all the people there are but how funny that's all I'm


gonna say. Pan Daily reports, Alibaba Domo Academy creates world's largest AI pre training model


with parameters far exceeding Google and Microsoft, right. So this is about a model called m six by


Alibaba Domo Academy. And the parameter count in these models is 1 trillion to 10 trillion far


exceeding the trillion level model previously released by Google and Microsoft, becoming the


world's largest AI pre training model. I found another article by info queue right here, which


I had to translate from Chinese. So m six stands for multimodality to multimodality multitask


mega transformer m six, that's why it's called m six. And the whole article is like an homage to


Chinese research. The real thing that's hailed here as a breakthrough is the efficiency by which


people can train these models. But the parameter count is a little bit tricky because this model


uses a mixture of experts architecture, which we can assume maybe to be sparse. And therefore,


a sparse model with a trillion parameters is not necessarily better than a dense model with 900


billion parameters given that the network is only activated sparsely. At this point, we don't


exactly know what we know is that the model is multimodal, which means it processes images,


it processes text and so on. One of the invention highlighted by the article is what they call a


grouped mixture of experts or what they call expert prototyping. They say it's so that different


groups of mixtures of experts can increase the expression space of the model without changing


the parameter scale. No idea what that means. So they tout that it can create more high resolution


pictures like Dali can create fashion as you see here can create textual descriptions find similar


images and so on. Alibaba achieved efficient training of the Trillion M6 model with only 480


v 100 cards reducing energy consumption by more than 80% and the efficiency is increased by nearly


11 times. Alright, so this seems to be the real achievement right here the investigation into


efficient model training. As I said, we don't exactly have better data right now, at least I


wasn't able to find it. What is a bit deceptive is that the title says that the model has 10 times


the number of neurons as humans. So apparently it has what a trillion parameters and the human brain


has 86 billion neurons yet, of course, the number of neurons is not equal to the number of parameters


for that you need the synapses in the brain, which are more than 125 trillion. So no, your parameter


count is not larger than human parameter count quite yet. And even if we get there, it's probably


not going to perform as well as humans just because you have that many parameters. If you


people figure out any more about this model, link it down below in the comments. Let me know the


scale and design of this models are amazing. This looks like a manifesto to the gradual growth of


many Chinese AI research organizations. Yeah, they kick your butt if you don't write this info queue,


this is like there's a guy in the corner being like this is great, isn't it? Isn't it? Excellent


journalism, everyone. On on tech writes AMD announces the instinct mi 200 accelerator family.


So this is AMD's newest incursion into the GPU space. They say they can connect whatever they


learn from building CPUs and GPUs together. And I honestly don't understand many of the things that


are said right here are what's supposed to be special. So as far as I can understand it,


one thing that's special is that their machines have like one memory for the CPUs and the GPUs,


which eliminates the need of shipping data back and forth, which is one of the main bottlenecks


in applications when using GPUs. Maybe I'm wrong. Another thing is that the individual parts that


you can put together into bigger parts into bigger servers, they are connected using super duper fast


whatever connections instead of PCI connections, which makes things yet even faster. So for their


biggest servers, they have 95.7 teraflops of floating point 32 matrix operations. And if you


go to FP 16, they have 383 teraflops. I'm being told that's a really good thing. I have no idea.


But if you're interested in this, if you maybe want to buy one, get in touch with AMD, please


sponsor me. The state of the eye report 2021 is out. This is produced by AI investors Nathan Benach


and Ian Hogarth. So actually, it's October 12. So this thing has been out for a while. But forgive


me for only reporting on this right now. So as it says, these two people are investors. So they


naturally have a distinct look onto the field, which is interesting, right. So it's divided into


various sections like research trends. It does quite a good job of summarizing sort of what's


going on currently in research where talent is in which countries at which universities and so on,


notably China seems to be rising quite a bit in pumping out AI graduates, as you can see right


here. Now, it's a quite a lengthy presentation. But what's really interesting is their predictions


for the next 12 months. For example, transformers replace recurrent networks to learn world models


with which RL agents surpass human performance in large and rich game environments. That's quite a


specific prediction, but could actually be true, right? Small transformers and CNN hybrid models


match current state of the art on ImageNet top one accuracy with 10 times fewer parameters,


a new AGI focused research company is formed with significant backing and a roadmap that's focused


on a sector vertical e.g. developer tools or life science. Well, I guess them being investors,


they can just make that happen and then claim their prediction was correct. But it's pretty


cool. I'm excited to follow which ones will actually work out and where they are completely


wrong. Probably they're under betting most of these things quite a bit. But you know, that's


just my opinion. If you're interested in the more general report, as I said, it's quite interesting


carries together a lot of data into a neat little package. TechCrunch writes landing AI brings in


57 million US dollars for its machine learning operations tools. So landing AI is a company


started by Andrew Ng and has just raised 57 million dollars to build essentially an M l ops


platform. They're doing what they're calling data centric AI. And whole idea is that things like


convolutional neural networks or in general machine learning models, they're as easy to


build as downloading a bit of code from GitHub and running it on your data set. So the real


challenge nowadays is really to get the data set to a quality where you can actually train some


good model on it. So their product is essentially this data manager and data labeler tool where it


helps professionals really label the data. This is all geared towards manufacturing. So here you'd


label cracks or dents or whatnot in newly manufactured phones, and then you train your


model on very little data. And that's then supposed to give you a nice detector for classifying


further manufacturing defects. So their idea isn't necessarily to build one big model that's going to


solve all the problems, but to provide the different industry players in manufacturing


with the tools to build their own models from very little but very high quality data so they


can essentially get their expertise into these models. I guess that's not a dumb idea. If you're


a manufacturer, maybe you want to try a landing lens. Another startup that has raised a lot of


money is Cerberus raising 250 million US dollars for an over 4 billion US dollar valuation. So


Cerberus builds these really big chips that are geared specifically towards AI computation. Now,


as I said before, I have no clue what's going on in these chip manufacturing processes and what's


important and whatnot. But these are apparently really, really big chips, and everything's


connected to everything in memory super fast and memories with the compute and yada yada yada,


what you need to know is that there are indeed other players than video or AMD in the space of


providing compute solutions for AI. And that's a good thing. And maybe at some point Cerberus will


come away from their giant chips and actually also make consumer products. Who knows if that happens,


it's going to be good for all of us. And if they stay in the big chip server world, I think it's


still good for us because all of the cloud compute might get cheaper because there's just more


competition. Speaking of cheap synced right Microsoft India proposes Veruna, a scalable and


low cost training of massive deep learning model system. So this is essentially an engineering


paper that details how you can train big models on cheap and unreliable hardware. So the system


uses both data parallelism as well as model pipelining. So you split up your data batches


across different machines, you also split up your models across different machines. And if you do


that in a smart way, you can achieve actual big throughput. So usually big models have to be


trained on what they call hyper clusters, which means clusters that are very fast interconnect,


because in order to do something like an order reduce, if you have to do layer normalization,


or batch normalization, I don't remember which one it is, sometimes you need to send data around,


sometimes you need to send gradients around, and that costs a lot of compute and bandwidth and so


on. It's very interesting to see that these researchers are able to compete with these big


hyper cluster training procedures and essentially bring that down to a heterogeneous clusters of


spot instances that can die at any time. It's cool to see that AI training of these big models becomes


something like a Kubernetes cluster where you can just add machines and the system will reconfigure


itself to make optimal use of the machines, however fast they may be connected and however


long they might be up. So if you're looking for a cheap way to train a 200 billion parameter model,


then this might be the way to go. Okay, here is a shout out to a few places. So the first shout out


is to Laura Ruiz's website, where she replicates a bunch of things in young lacrosse and others


papers called learning in high dimension always amounts to extrapolation. Now, it's a very technical


paper. And Laura does a great job here, not only replicating the experiments in here, but providing


really nice background and reasons and also the code that she uses to do everything. So I just


thought this was really neat interleaving plots, code, math, and so on, and really going through


all of this. And in the end, actually being able to reproduce the plots of the papers, yippee,


there it is so beautiful, very reproduced, much similar. If you want to follow Laura, definitely


check out her website or GitHub. This is absolutely beautiful photo Laura. Good job.


Right, another cool project is real life punch out by Ian Charnas. This is a really well made video


about using body tracking models and pairing them up with punch out the N64 game. So you can actually


play this in the browser, it tracks your arms, and you can punch using various boxing moves and play


punch out. Not only that, but Ian actually went ahead and bought many cartridges of the game,


as you can see in the background right here. And if you play it in the browser, it will actually


use one of those cartridges because using just a ROM downloaded from the internet would violate


the licensing agreements. So every game you play is essentially corresponding to a real life


cartridge. As I said, the video is done extremely well. It's a fun video to watch. Or if you simply


want to try it out, you can go to Ian's website and just play it by yourself. Nothing to install


runs in the browser. Excellent. Alright, so this is the section where I provide some helpful things.


First helpful thing market tech post writes Google AI introduces go emotions, an NLP data set for


fine grained emotion classification. I've actually shown this in last week's weights and biases ad


if you have followed the weights and biases ads, but this is a data set where Reddit comments are


annotated with one of I believe 28 different emotions contained in the comments. It's not


only one emotion per comment, but technically any emotion could or could not appear in any comment.


In total, there are 58,000 Reddit comments classified into our no it's 27 emotion categories,


12 positive 11 negative four ambiguous and one neutral with that adds up to 28 hours right. So


the data set creation process detailed here is detailing how they went about it, how they went


about balancing the data paying attention to the fact that Reddit isn't exactly a good replica of


the entire world and so on. If you're interested, you can give this article a read. You can also


look at the paper that goes along with the data set and you can use the data set if you want to


try out your hand at emotion detection. I have to say it's gotten a bit tired to see NLP tutorials


always doing sort of semantic classification where it's just positive or negative. And this might


just provide a little bit of a more challenging task here has this language interpretability tool,


it's open source, and it's for visualizing and understanding NLP models. This provides various


things you can look at embedding spaces of NLP tasks, it can analyze things like classification,


regression, looking at attention heads, analyzing parts of the input, which parts are important for


which things and so on. All in all, it's quite a rich tool. And I encourage you to check it out


if you're into language interpretability. Or if you want to just check out how your models do the


things they're doing code is available tool is available. Okay, last week, we've reported on the


rudely the Russian Dalai model. And now apparently the large model is available for download as one


Reddit comment says, or much rather the edit of the comment says that the availability is on


December 1. So expect that soon. Yee Machine on Twitter says after a year in Dev, I'm happy to


release the core of my vTuber apps. Now vTubers are special sort of things that I have never really


touched on. But this seems to be a large community that transforms their body movements onto digital


anime avatars, as you can see right here. So this also uses body pose tracking, and apparently also


face tracking in order to make your avatar do as you're doing code is available. And it's not only


sort of for face and upper body, but you can also track your entire body movements and map them onto


characters. As you can see right here, it can do facial point tracking such that it really replicates


your facial expressions. So there's never been a better time to become a vTuber. Check out Khalido


kit on GitHub if you're interested. There's an article by Newsfile Corporation on Yahoo Finance


that writes that artificial intelligence now makes it possible for investors to find promising new


hidden gem meme tokens automatically. This isn't necessarily what you think you think while there's


a company that tells me which meme tokens are good so I can buy it. No, no, no, no, no, no, no,


see, this is an actual token itself. So you put money into the token, and then the token selects


projects in which the money is to be invested. These projects, it says are automatically selected


using a special AI based sniper bot. So the AI will look at all the meme tokens, the dodge and


the Shiba Inu and the squid game tokens, and it will predict which ones will go up and then it


will take all the money that is invested into the FI new token, put it into those tokens and then


pay out the winnings to the holders of the FI new token. I mean, look at this for an enhanced version


of this graphic, please. Yes, I want an enhanced version. Oh, wow, that's enhanced. That that is


that is so hands. Absolutely. Currently, there is a website for this and it says vote for FI new


help the price pump and hit the back there is a doge. Okay, people who want to make a quick buck


using meme tokens that have absolutely no value whatsoever, are encouraged to buy a meme token.


Excellent. Now I'm not saying this can't be done. meme tokens are essentially like fashion. There's


no reason why this particular that particular fashion should be in or out next year. And yet,


it still happens and there might be ways to predict it. But still, whether or not this is


the way to go. Can't tell. So I've mentioned this shoe does not exist last week. But there's also


this sneaker does not exist. Look at that. And this is pretty cool. So this is a grid of AI


generated sneakers, you can click on one, right, and then you can apparently edit that sneaker.


So you can go normal to futuristic, you can go high creativity. That's very creative. You can


change up the colors a little bit. Very cool. Very functional. Look at that one. Yeah, futuristic,


creative, light color. I mean, it's not super futuristic. But yeah, so shout out to this sneaker


does not exist.com. Check it out. And that was already it for this week's ML news. I hope you had


fun hit subscribe if you liked it. We're only 105 million 900,000 subscribers behind PewDiePie.


We can totally catch them if we really do our jobs. Tell three people they're going to tell


2M All-In into $5 Pot! WWYD? Daniel Negreanu's No-Limit Hold'em Challenge! (Poker Hand Analysis)

Hi there, today I want to bring to you a little bit of a different video. The video right now is


supposed to be sort of a motivational lead up to the next video I want to release. And the next


video is going to be about Facebook's new rebel algorithm, which is an algorithm that solves two


player zero sum imperfect information games. So it is very similar in to the Alpha Zero algorithm


or the AlphaGo algorithm, just that line of algorithms that combine search and learning.


But whereas the alpha line is in perfect information games, so games where you can see


everything like chess or go, the rebel algorithm is an imperfect information games. And one example


of this is poker. So heads up, heads up poker, like heads up, Texas Hold'em, no limit, let's say in


this case, is a two player zero sum. Let's assume the house doesn't take a rake. Two player zero sum


imperfect information game, which this algorithm rebel can solve better than apparently anything


before it. And Daniel Ngranu, who is a, you know, a longtime poker pro has released these polls on


Twitter, which I found just to be very interesting. So the timing was very fitting. And I thought I


sort of make a lead up video to the next paper video, just to sort of get you into the thinking


if you've never if you've never played poker at sort of beyond an amateur level, I sort of want to


motivate you what makes this game so interesting, because it seems pretty simple at the start. Okay,


so here we go. The Daniel Ngranu poses the following question, poker question for you all.


And maybe I should briefly explain how the game works for anyone who doesn't know there. And if


you have one minute, if you know, just jump ahead one minute or so. So at the beginning, you get two


cards, your opponent gets two cards, you don't know the opponent's cards, the opponent doesn't know


your cards, then success successively on the board, they're going to be revealed first three


cards at a time, which is called the flop. Then there's one other card, which is called the turn.


And then there's another card, which is called the river. And there are four betting rounds. So


there's one betting round pre flop, which is when no cards are on the table, there's one betting


round at the flop, one at the turn and one at the river. And then if the players are still in and


haven't folded, the cards are revealed and scored according to the normal rules of poker. So your two


cards and the table five cards, you get to choose any five of those seven to make up the poker hand,


whoever has the better poker hand wins. Okay. So in this situation here, you have aces, so your whole


cards are two aces, which is you know, the best pre flop hand, but the board is ace, aka eight,


four, four, so ace king, eight, four, and four. So that's the board, which gives you a full house


aces with fours, okay, which is the second best hand that's possible on this board. So you have


the second best hand, usually you would be happy to put all your money in into this board. Because


the only hand that's better than you is if your opponent has two fours. So that is a possibility,


right? But it's a very, very, very slim possibility. So you might think I want to put all my money into


here. But now, you know, now comes the tricky part is you put all your money in here, because you say,


well, there's only really one hand that beats me, okay, but you have to think ahead and say, how


often does my opponent have that hand? And crucially, crucially, how often are they going to


give me their money while not having this hand? So let's say your opponent has an eight and a nine,


okay. And, and so they have a pair of eights, which you know, they might think, you know,


I have a pair pairs, okay. But you put in a lot of money, they're probably going to fold that hand,


right? So if you put in a lot of money here, they're not giving you any money. So if now,


let's say they have like, two kings, which is a very strong hand on this board. But if you put in


like, exorbitant amounts of money, still, they're going to conclude, well, it's, it's not worth it,


like, there are still better hands I'm going to fold. So all of this, it's not just a question of


which cards do you have? It's not even a question which cards your opponent has, it's, it's a it's


a question also of how much money do you put in? Because that regulates very much how the strategies


are, I hope I hope you can sort of see that. So you always have to think about what possible cards


could my opponents hold? And which of these cards are they willing to put in how much money into the


pot? And then from that, you can determine, is that profitable for me or not? In this particular


situation, there are $5 already in the pot. So all the previous betting rounds, they get collected


into what's called the pot. So the pot here, in this case is $5. And your opponent, your opponent


bets $2 million, okay, so $2 million on the pot into a pot of five, it's obviously a constructed


scenario, but your opponent now puts up 2 million, okay, so you have to put in 2 million into a pot


that's now $2 million and $5. So if you let's say if you fold, you lose whatever you put in of these


$5. So you shouldn't think that sunk cost anyway, you should simply think I put in 2 million in


order to win five plus the 2 million the opponent puts in, okay. So obviously, this is exactly the


reverse of what we looked at now your opponent is putting in a ginormous amount of money, okay,


and you, you have the second best hand. So this this get now gets interesting. Now there is an


additional complication here, would you call or fold against the guy who always goes in on the


river every hand, okay, this is an additional information, somehow, you know, that this person


always goes in on the river. So on the river, they always shove all their money all in. That's what


you know. Now, a lot of people would lean to an easy call here, a lot of people would say, of


course, they're going to all in with any like any, anytime they're on the river. So of course,


I'm going to call it the second best hand, there are many, many hands and if they're going to do


this with all hands, but that's not the case. They're just because they always go all in on


the river every hand. I think this is slightly under specified. It's every hand where they get


to the river, right? So here, a smart opponent, let's say this is a smart opponent. But for some


reason, someone kidnapped their dog and threatens to kill the dog if they don't always go all in on


the river. But other than that, they're very smart player. So they, they now also know that they


always go all in on the river, because you know, they always go in all in on the river. So what


they will do is, once they're on the flop and the turn, they will only ever continue with hands


where they would go all in all in on the river, right? And they they not only they not don't


always have 2 million in the end on the table, they might have no smaller values. So when they


are on the flop, and when they are on the turn, they are very much aware that they have this


giant amount of money, and that they must go all in if they reach the river. So conceivably, they


would fold every hand that they weren't willing to go all in on the river. So they they won't have


just any cards they that that seriously skews their distribution of cards that they could hold


because they make that inference, right? So now you can sit here and say, Okay, it's conceivable


that they actually hold off on, you know, most of their cards, they would fold most of their cards


on the on the flop or turn, given that they must always go all in all in on the river. So let's


actually look at the turn. So let's imagine we do not know that this is a four, right? So we the


last decisions are made here, right here, when it's the when it's the turn. Here, your opponent


will only go to the river with cards where they feel that they can then fully go all in all the


way, right? That's because they also know they go all in every time they reach the river. So the


question is, what possible range could they do this with? And one possibility is like they they do


it. If they know they have 2 million it's a very risky move to go all in on the river, right? So


conceivably, I'd say they would not do it with two fours because they can't possibly know that


another four is coming the chances so incredibly slim. However, of course, that strategy now also


changes the range of hands that you continue to the river with so you can be you knowing that the


opponent will only go to the river with cards where they could go all in on the river also will


change your distribution. But just in this particular situation, I would say the following.


If this is the case, the opponent can't possibly know that there's another four coming. Therefore,


their range here, if it includes two fours, if it includes those, it will also include something


like two kings, it will also include something like ace four, or king four, like conceivably


because those maybe not but two eights maybe. But at least two kings, so their range is conceivably


yeah, if it includes two fours, it must include two eights and two kings, right? Because these are


strictly better at the turn. It could even be any ace because that blocks you from having an ace.


So if they can have fours at the end, they can also have kings and eights. And just because they


can have those hands, it probably makes for a for a good call here on the river because you are


beating kings and eights on on the river. Specifically, the fours are much more unlikely


because the four is actually in the deck since we we already know it's coming right here. So in this


case, I would call because of those whole reasoning not because I have the second best hand, right, I


hope you can sort of see how this back and forth goes. So you assume that your opponent is smart,


your opponent assumes that you are smart. And then you sort of reason 123 levels in depth. And of


course, if you reason to infinity, that becomes a Nash equilibrium. And that's exactly what this


rebel algorithm approximates. I would have guessed that this situation is much more interesting if


you reverse the board. So if the board was something like 448444, four, four, ace, King


eight or something like this, where your opponent clearly already has the best possible hand before


they enter the river, that would make would would make it quite a bit more interesting, I believe.


And I, I don't know what the analysis would be. But let's go on to the next 10. So that would be


my guess would be called. I haven't, as you can see, I haven't answered yet I will after the video.


But it's irrelevant because the most comments I read are just like inferring very simple things,


which are, as I say irrelevant. So the follow up question here is their same situation $5 in the


pot to me, the opponent bets 2 million all in on the river board is the same you have aces, would


you call a fold against the player you know nothing about? Okay, so here's a player you know nothing


about. Now, the you know nothing about is so now you like, now you have to estimate probabilities


that the person is brain dead and things like this, right? But what you can do, what you can do


is always just estimate sort of the Nash equilibrium strategy of the situation and maybe go with that


because at least then you cannot lose an expectation. So if you fact if you like factor in the fact that


the person might be dumb or brain dead or something like this, then if you mess up these probabilities,


you are in fact exploitable. Though, you know, the exploitability only matters if that situation


happens over and over and over and over again, whereas I think this is going to happen to you


at maximum once. However, same situation, but your opponent does not go all in on the river every


hand, you know nothing about them, right? The board happens as it is. And all of a sudden, this person


pushes 2 million. Now let's analyze this. So you might think, hey, this person pushes 2 million


in the pot of $5. They must hold the nuts very, very, very, very often for this to be profitable,


right? So they probably hold the two fours right here. But then again, if you infer that you might


want to go ahead and fold those aces, okay, you fold the aces. So your opponent thinks about this,


and they realize, wait a minute, if I can get them to fold aces, which is the second best hand on


this board, right? I should probably push this much money a lot more often, because I can, you


know, like I can get them off aces, I can probably get them off most hands that they are in this


situation with right on this board, a ace, King eight, we don't know the colors, but there are a


lot of hands that get to the river in this situation. So I can bluff them off a lot of them


by simply pushing 2 million in the pot, right? But then it's this old game, you push 2 million to win


$5. This has to work very often. In fact, this has to work now it has to work like 4,399,000


out of 400,000 times to break even right? If it if it doesn't work even one time. Yeah, so


if you're up, if you fold anything, but the but the absolute nuts, your opponent might actually


just hold a single four, because then they know you don't have two fours. And then they know


you can't possibly have the best hand, then it can push you off of it. But then, right, they,


if they bluff a certain amount of time, if they don't need to bluff often for you to actually


make it profitable. And if they do, in fact bluff. So let's let's assume they just bluff if they have


a four, because they know you can't have both fours because they have one. So you can never


have the best hand. And they think if they bet 2 million, they can push you off any hand. Now you


go ahead and you say, wait a minute, if they bluff whenever they have a single four, they're much more


often going to have a single four, like maybe they have a four, four, nine or something like this,


they're much more often going to have a hand like this, then two fours just combinatorically, right?


So maybe they're actually on a bluff pretty often here if they do this every single time they have a


four. So I can actually call, it doesn't even matter that I have aces, right, I can call with


any any hand that hits anything on this board is probably going to beat though, if they have a four,


they have trips. So let's say if they bluff with any hand, I can call with any hand. And they will


think about this and say, oh, maybe I shouldn't bluff with any hand, right? I should probably


moderate that because the other person will adjust. If they bluff with a four, they have trip fours.


So I even if they bluff with a four, I might only and it is a bluff. Like if you have a four and you


bet 2 million here, that's a bluff. Like you're clearly trying to get someone off of like aces.


Because it's not like you don't bet for value 2 million into $5 with this. So I will only call


with aces, kings, eights, ace four, king four, eight four, stuff like this because they all beat


a single four, right? And now the question becomes, again, how so there is there is the


number of hands I will call with like aces, kings, and so on. Ace four, how these are a subset of


hands, or maybe not like this as subset of hands, probably a large subset of all the hands that I


would hold on the river like that I would get to the river with right here. And they are going to


push me off of those hands with with any large bet. But this this bet is really meant to get me


off of those strong hands. So the question is, how often do they do this with a four in order to


still be profitable? So we get back to this sort of inference of how often can this be a bluff for


me to legitimately call here? And that factors in how often I am on the river and how often on the


river I hold one of these hands that I could conceivably catch a bluff with. So you can see


that a lot of a lot of stuff is going in here. Me personally, I would say that I know nothing about


this person, I would probably fold in this in this case. Because if I assume they're smart, they must


know that they can only pull this 2 million into $5 thing very, very few times if they don't have


the absolute nuts in this case. And if they don't have the nuts, it almost it almost doesn't matter


what they have, they probably have a single four and then yeah, the number of hands that I can have


on the river that are going to catch a bluff with a single four is just too large for them to often


bluff right here. Of course, if we both play if if the person plays Nash optimal, then I have like


some assignment to call or fold right probability of call probability of fold that I would do in


this particular situation. And and it's going to be break even. Okay, last question, though that


might not be true, I might have actually a fixed binary decision here. No, because that influences


their strategy to Yeah. Last question, same thing. But now, which hand would be better to have if


you choose to call, so you, you choose to call, but now, which hand would you rather have in that


situation? Would you have king four or aces? So some people might say, well, aces, clearly because


aces here is the better hand than King four, right? aces is full house aces full of fours and King


four is fours full of kings. So let's say you imagine you have King four, why would you want to


have King four, you would want to have King four, because now your opponent can't have two fours


anymore. Okay, so the possibility of your opponent holding two force is off the table because there


are only four fours in the deck. And the so you're blocking that possibility that your opponent has


two force. So they cannot have the nuts possibly. They, it's much more probable now that in fact,


they have a single four, right? And they are trying to push you off of something like aces.


You see, so it's a bit the same situation as before. And we can we can remark that King four


is also in this hands that we would call with. But so are the aces. Now, it all again boils down


to what's the frequency of them folding here. And that boils down to what's the proportion of hands


that you have here plus what's the frequency of them that you call with? So the question is, would


you rather have aces or King four? And why would you why would you rather have aces? What would


be reasons that you would rather have aces? Well, if your opponent is smart, they might think that


and I haven't thought this through before, but let's just try to figure this out together.


Your opponent. So if you'd rather have aces than King four, that must mean that your opponent would


do this conceivably with hands that you beat with aces, but not with King four, like you, you decide


to call that's a given you decide to call. So now everyone reveals their cards. And so if you say


you'd rather have aces, that means you think that your opponent would do this kind of stuff with


something like that Kings or eights or something like this, something that would beat King four,


but not beat aces. So your opponent, your opponent might be smart and think, Wait a minute. If this


person has an a four, right, then they will think that I cannot possibly have two fours. And


therefore they will call with a single four, even if I bet 2 million date, they will think,


who I have the four and therefore they can't have the four. So this must be one of those rare times


where they bluff, right. And, and then they might say, Well, but I have two eights, right, I have two


eights, I beat a single four. And therefore, I can actually get money out of anyone that's trying to


catch my bluff, because they have a single four. So now the question is, how often does anyone on


the river here have a single four? And again, this is where I go and say that board would be probably


more interesting if it was this way around, because it's much more conceivable that anyone has a


single four lying around. If the flop was this already, though, King four conceivably is you hit


the king on the flop, and then you somehow get through to the river while scoring two fours,


but it's just not as likely that you still have the four around. And so, again, this is where I


still have the four around. But still, you can sort of see the thinking right. So the opponent might


think, wait, they're going to call me with any old four, especially with like, also with like King


four, I have eights, I beat things like Ace four, King four, I beat a single four, my opponent's


gonna think I only do the 2 million things with two fours, my opponent's gonna have a four, they


will infer that I can't have a four, they will call me because they think I'm bluffing and


ta da da da. Okay, so you can see that it goes pretty, pretty deep. And then in that case,


they will push with the eights. And in that case, you much rather have the aces right here,


because they don't know whether you have the four or not, right. But if you have the aces, again,


you do not have the four, and it is very possible that your opponent has two fours. And after all,


it's 2 million into a pot of $5, they would only, they have to have a very good hand very often


for this to be profitable. Okay, so this, this kind of thinking is, is what computation of an


Ash equilibrium, in effect boils down to. So we're going to see, I don't know what the correct


answers to this is, by the way, I the even the rebel source code isn't open source for poker,


the code is open source, but the implementation for poker isn't and I think the checkpoints for


poker aren't. So maybe we won't we won't find out, I would love to hear your opinions on this,


maybe I am completely wrong right here. But this is about what an algorithm like that has has to


do. And I hope I've sort of given you an overview of why this sort of games are interesting, what


these algorithms need to think about, and why it is so much harder than something like chess or go


not that the game itself is harder, but you have to constantly reason about things that you do not


know. And you constantly have to assign probabilities and combinatorial fractioning.


How often does this happen? How often does this happen? And then you have to adjust each time


when you adjust your strategy, you have to think that your opponent can make the same conclusions,


given the observed state, and they can also adjust their strategy. So that's the difficulty. Those


are the questions I would say you go vote, see what other people have to say. And maybe Daniel


will let us know once the polls are over. Alright, so that was it for me. Thanks a lot for watching.


Discriminating Systems - Gender, Race, and Power in AI

Hi there. Today we're looking at discriminating systems, gender, race and power in AI by Sarah


Myers West, Meredith Whitaker and Kate Crawford of the AI Now Institute, which is a part of


New York University or associated with it. This is not as much a paper as it is a report


kind of summarizing current literature and also kind of an opinion piece, slash recommendation


giving document. Yes, so we'll dive into it. As you can see from the index, it's quite


a long report and we were we don't have time to go into all of it. Actually, we don't have


time to go into most of it. I just hope to kind of point out what the main arguments


and themes are in the report kind of what it's trying to say, pick out some interesting


things and summarize it to the best of my ability. Also give a little critique. So let


me actually go ahead and try to state the the kind of core the kind of core arguments


that the report is trying to make because it's not really clear from reading it and


you have to kind of read the whole thing and then kind of becomes clear what the argument


is, I feel, though they they somehow stated in the introduction numerous times in various


ways. So I might just be not as as attentive a reader at first time, but all right. So


here's the argument and I really hope I'm representing this correctly. We have a problem


currently that sometimes AI systems can exhibit what we usually call bias. And we don't mean


mathematical bias, like bias, variance, trade off, we mean bias in a societal sense, let's


say, bias against certain types of people where they shouldn't exist. So, for example,


let me draw an AI system and just draw a little computer screen with a light bulb. All right,


this is because it's smart. This is an AI system and the AI system and they give numerous


examples. One example they give us for is like face recognition algorithm that is much


more accurate on faces of white males, as opposed to darker skinned females. So let


me draw like two curves to represent these distributions are unequal. And so the AI system


exhibits some bias with respect to some kinds of people with and especially protected attributes.


And in this report, they focus mainly on gender and race. So that's what we're going to talk


about. The second thing they observe, so this observation one, the second thing they observe


is, I'm going to draw some generic people here that represent the workforce of AI. So


the AI workforce is classified as all the people that work on AI, be that university


researchers or within companies building AI products or deploying them. So this is the


workforce. And they observe that there is a unequal distribution among the AI workforce.


So this distribution, I'm also going to do this for unequal distribution. There's an


unequal distribution in the workforce. Most notably, it's predominantly males who work


on AI. And and also white people are overrepresented compared to the world population at large.


So that's kind of the two observations they make. And now what they claim is that the


inequality or the unequal representation in the workforce is causing the bias in the AI


systems. So they're basically saying the these AI systems are biased, because that the workforce


is unequally distributed. And also they claim in a less powerful sense, I feel, but they


claim there is a loop that this then leads back that because there is bias in the AI


system, that again, leads to an unequal, more unequal distribution of the workforce. So


the the core argument really is, as they set out to do, like in the introduction, and also


claim that they have done in the conclusion is to demonstrate these two directions here


in a in a causal way. So the the systems are biased because there is an unequal representation


in the workforce, and that feeds back. So the the argument is that if you want to fix


the bias here, if you want to fix that, then you will have to fix it via making the workforce


more what they call diverse. So less less unilaterally distributed towards white males.


That's that's kind of the the final conclusion. If you read their report and the recommendations,


that's that's mainly what they're going for. Yeah, so my opinion, or in my opinion, having


read the report a couple of times is that as as I see it, they really don't demonstrate


these links. So they get they give examples of this, and they give examples of this, right,


they show that the workforce is unequally distributed. They show that AI systems can


exhibit such bias, but they never actually show these links. In my opinion, they they


don't show this. So if if you make the claim that in order to fix the bias in AI systems,


you must fix the unequal representation in the workforce, I would need a argument that


says because there is unequal representation, therefore, a therefore be therefore, see therefore


bias like a an actual argument to follow that says because of this that because of that,


and so on, like, it's just not there. Like they, they simply show parallels, they simply


show that these two things exist, and they just list example after example of that. And


the the the I don't think they make this argument. What I think I think to also the other direction,


they don't really make this argument. Except in very in, in like one, in one case, where,


you know, if you give them benefit of the doubt. What I also think is that it appears


like, like the article, if you read it, and I encourage you to read it, if you have some


time, it makes a lot of sense, if you have already accepted this conclusion, like if


you've already accepted this, then it's like, Oh, yeah, because I feel this is just a text


where the confirmation bias is so high, just the way it's written, that it must make a


lot of sense to someone who's already kind of in on this conclusion. But to someone who


isn't sold yet, like myself, I am just not finding this convincing at all. The second


thing is that it very much feels like this isn't like a discovery or something. But someone


actually set out with the goal to address this year with the goal of I want companies


to hire more of these people or certain kinds of people or to become more diverse or to


promote more of a certain type of people. And now I'm going to find reasons for this.


And the reason is like, Oh, look at look at this bias here. This is cost. This is caused


by this other thing. And therefore, we must fix this other thing. It very much feels like


someone setting out with the already the conclusion in mind rather than this being an honest investigation.


But yeah, I mean, read it for yourself. I can't prove the absence of an argument by


not reading every single line. And I can't read every single line because it will just


get very long and boring. But read it yourself. And I'm I think I'm pretty, I'm pretty. I've


read it numerous times with really an open mind to be convinced that there is an argument


in there. But I don't think there is, or I don't think there is a very strong argument


for this. Alright, let this first part here is more or less a summary. So research findings


is more or less a summary. And we'll get to these things as they are important. Then they


state recommendations right at the beginning. So actually, you'd have to read the article


first, this is kind of more of an abstract section. But since it's right here, we'll


kind of jump right into it. So these are recommendations. And I've claimed they don't really show a


connection. But they actually just show examples, examples of this and examples of this and


parallel them. And this is reflected in like every single section, including here in the


recommendations. They have recommendations for improving workplace diversity. And they


have recommendations for addressing bias and discrimination in AI systems. Right. So all


right, in my case, if you make this argument, I would, I would feel you also make recommendations


for breaking these links, but or argue why they can't be broken. But all right, let's


jump into some of them. And it's really a mixed bag here, really. So some recommendations,


I'm really in favor of just from from the go not even you don't even need the article


for those here, published harassment and discrimination transparency reports, including number of


claims over time, the types of claims submitted and actions taken. So it's known that especially


in these larger companies, sexual harassment claims often go down in either bureaucracy


or are kind of hushed under the table, or something like this, what you have to recognize


is that a human resource department of a large company isn't there to serve the human resources,


it's there to serve the company, providing human resources, that that's why a sexual


harassment claim to an HR department is just a potential lawsuit. And that's why they don't


want to take it seriously, except for it must go away really quickly. So I think, to kind


of force companies or to ask companies to be more transparent to take more seriously


these the accusations of sexual harassment and assault, and also discrimination is a


very valuable goal. And I fully fully support this. Also, the here commit to transparency


around hiring practices, especially high regarding how candidates are leveled, compensated and


promoted that also is in the larger the company gets the less transparent this process usually


becomes, or the more bureaucratic the more people are able to game it and so on, and


distorted. So I feel it's always good to be transparent around around, okay, this person


provides this much value to the company, therefore, they should be compensated in according, like


according to that, or at least be be transparent about it. So these are kind of recommendations


I like then recommendations that really go into a different direction is something like


this year change hiring practices to maximize diversity. And this is kind of reflect, I'm


not going to go on the this reflected in other in other points, increase the number of people


of color, women and other underrepresented groups at senior leadership levels of AI companies


across all departments. So these things, they are usually within like company diversity


goals and so on, doesn't really say how to do it. But then the I mean, as such, they're


not really recommendations yet. They're more like goals. But here recommendation seven,


I think is the the crucial one. Ensure executive incentive structures are tied to increases


in hiring and retention of underrepresented groups. So this is it's a bit of coded language,


but but here they talk about executive incentive structure tied to hiring and retention of


under represented groups. This basically means if you are a manager or someone in charge


of hiring or promoting, and you hire or promote a underrepresented person, and since they're


talking about gender and race here, if you that means if you hire or promote a person


of color or a woman, in this case, you will be compensated more. So at the end of the


year, you'll somehow have more money, like more bonuses or more base comp or more equity


or something like you'll get more money. So this, this recommendation is a direct call


to hire based on race and gender. So this, this is a direct call to racist and sexist


hiring basically to discriminate people according to their skin color and according to their


gender, which I I mean, how, how is this okay with anyone? Like, how can anyone? How are


people even able to state this and in like a high profile report like this and get away


with it and not have people criticize them? This directly calls for people to be treated


according to their gender and race. And probably as directly as you can go without getting


into actual legal trouble. But yeah, I'm really, really against such such practices. I mean,


yeah, that's I just I just don't know how this how this can ever how this can ever be


thought of as a good thing by anyone. All right. So, well, yeah, in my mind, this recommendation


and this recommendation kind of are counter to each other. Because if if I commit to transparency,


how people are okay now I can come I can transparently commit to to be racist, I guess. But if I say,


okay, I'm going to come and promote people based on how much value they provide to the


company, then yeah, I'd much rather have that than saying I'm going to come and promote


people based on their skin color. All right. So let's actually jump into the report. I'm


not gonna these recommendations for addressing bias and discrimination in systems. This these


are fairly general and common. So as well, as I said, we'll jump most of the things in


the report. So introduction. So they start out with there is a diversity crisis in the


AI industry. This they give like some numbers like 15% of AI research staff and 10% at Google,


so 15% of Facebook are women. So these are some kind of fairly known statistics about


how the AI field is kind of gender and race skewed. Currently. So they say the claim in


bold diversity problem is not just about women. It's about gender, race, and most fundamentally


about power. It affects how AI companies work or products get built, who they're designed


to serve, and who benefits from their development. So this, I find this, this this word power


and this notion of power a lot in this report, it's it appears again and again and again,


in like power dynamics and power dynamics among groups. It's like a worldview. It paints


like a worldview where these different gender and race groups kind of struggle against each


other to gain power over another, and whoever's in power will try to remain in power in alliance


with their gender and race group and try to keep the other groups down. It's I don't,


I'm not sure that's the correct view of the world. In my mind. The world is comprised


of individual people that want to achieve something for themselves and they would like


to prop themselves up. Whereas in this worldview, it's like, I'm going to use the power of my


group to keep the other groups down. I don't know which worldview you subscribe to, but


I find the the world is comprised of individuals. Yeah, and this is not discrediting that some


people have it harder because of their gender or race. But to see the entire world as a


power struggle between these groups. To me, it's it's a Yeah, and I'm not going to point


out everywhere it appears this power wording, but it appears a lot. And it's really shapes


how the report reads. You have to you have to kind of remember, if you're a white male,


and currently, the field is comprised of 90% white males, you, if you have like 10, like


10 hours, let's say you have to do 10 hours to do something, right, you can either choose


to put down some other groups like put down groups that you're not part of, or you can


choose to invest these 10 hours in putting up yourself, you, right. So if if I, like


I profit, if I'm a white male, I profit minimally from keeping the other groups down, because


guess what, I still have to compete with the like 1 billion other white males there are.


That's not going to help me to keep down anyone else. And especially, like it's it's moronic,


like who does that? Who like has Alliance, except most French people, like to their race


or gender rather than to the people they admire and respect and like to work with. So I'm


gonna have like 10 hours today, I'm gonna rather spend this and propping up myself compared


to everyone else. And I don't care what gender or race they are. And so that to me, that's


a much more accurate or, I don't know, plausible worldview. But just be aware that this report


really takes on the language of kind of groups and power between groups and groups trying


to, you know, kind of gain power and keep in keep power and keep others from having


power. All right, so say, to date, the diversity problems of the industry and the issues of


bias in the systems it builds have tended to be considered separately. We suggest that


these are two versions of the same problem. Issues of discrimination in the workforce


and in system buildings are deeply intertwined. challenge and moreover, tackling the challenges


of bias within technical systems requires addressing workforce diversity and vice versa.


So the the, I think this this here actually is like how I described the argument and they


kind of restated multiple times in a bit different way. But I think this is the core. And I really


think I'm not misrepresenting the article here in that this is what they are setting


out to do. They're setting out to say, okay, the diversity, the kind of unequal representation


in the workforce, and the bias in some AI systems are causally linked to each other.


And tackling one requires tackling the other. So, yeah, if I'm misrepresenting them, let


me know. But I really think I'm accurately representing their argument. So what they


what they do, as I said, is they give examples of one and of the other. And also, they really,


they're really on kind of discrediting the the kind of issues to solve problems of bias


in a different way. So they, they point a little bit to this here in the introduction,


they say in the face of growing evidence, the AI research community and the industry


producing our products have begun addressing the problem of bias by building on a body


of work of fairness, accountability and transparency. So fairness, accountability and transparency


research concerns these these issues, for one, is research showing that some products


are unfair or untransparent. And so on. And on the other hand, it's trying to devise algorithms


that are more fair, according to some notions, or more accountable and transparent, which


means that the algorithm can kind of say why it made a certain decision rather than it


being a deep learning system that you don't really have an insight. These fields are active


fields of research, definitely very interesting to look into. So but they they kind of it


is not already here. But they say, yeah, we have adjusting our systems that then produce


a result deem fair by one of various mathematical definitions. You can already see in the language


here, they don't really like this research. And they are trying in this report to kind


of discredit it, or at least claim that it doesn't solve the whole problem. Because their


point is, of course, you have to address this diversity issue in the workforce in order


to fix the problems. Yeah. So to this, I just want to say, no, like, if you can, I mean,


you can criticize the the fairness and accountability and transparency research field in that they


haven't solved the problem fully yet. But in principle, if I have an algorithm, if I'm


being delivered an algorithm, right, and the fairness of literature has been applied to


that algorithm, and someone tells me, I guarantee you here is a proof, the algorithm is fair,


right, then I really don't care who made that algorithm, as long as it's fair, the problem


is fixed. If the bias is gone, the problem is fixed. And I don't care who fixed it. I


don't care if the person who fixed it is black or white or purple, then the problem is fixed.


And they, they really have to really try to just make the counter argument here is that


no, that's, it's not enough. But I claim yes, it if you can actually solve the fairness


problem, technically, then you have solved the fairness problem. Yeah, the only thing


you can do is claim that it is not good enough yet, but not that it's fun to they kind of


have to make the argument that it's fundamentally flawed approach. And I don't think they succeed.


And doing that here. Yeah, so they go on to say, we should expand to consider not only


how it i tools can be biased technically, but how they're shaped by the environments


in which are built in and the people that built them. Again, this this focus, like who


builds the AI system, I don't care, I care what it does, right? As much as if I hear


an argument for or against something, I don't care who makes the argument, right? I care


what the argument says. This is it's like an ad hominem attack for an entire community.


That's kind of how this this article, this report shows, or is it appears to me. So they


say, currently, large scale AI systems are developed almost exclusively in a handful


of technology companies and a small set of elite university laboratories spaces that


in the West tend to be extremely white, affluent, technically oriented and male. So yeah, they're


their problem. That's their fundamental problem here that these these spaces are skewed in


one direction. Interestingly enough, their problem is not so much that it's that they're


all in the same place, right? That they all live like 20 miles from each other in around


San Francisco. That's that seems to be not a problem at all, as long as we get to like,


enough people of color and women into these 20 miles. But yeah, so that that's pointing


out the the problem here or the Yeah, kind of issue they have. Right. So they go on.


I just kind of want to highlight again, they say, both within the spaces where AI is being


created and the logic of how AI systems are being designed. So paralleling the two things,


the cost of bias, harassment and discrimination are borne by the same people, gender minorities,


people of color, other underrepresented groups. And they also say similarly, the benefits


of such systems from profit to efficiency, accrue primarily to those are already in positions


of power tend to be white, educated and male. So they again, they say, the this points to


a systematic relationship between patterns of exclusion within the field of AI and the


industry driving its production on the one hand and the biases that manifest in the logics


and applications of the technologies on the other. And they try to make this connection


because they say the cost and the benefit of these two things are overlap in the people


that were a cost and a benefits and I really, again, it's just a parallel, but I really


even don't think that's true, because they, they kind of, they kind of argue against themselves


later. So they always say, we have to look at, again, they shoot against the take much


more than the technically driven problem solving. They point to this. So a research requires


looking at gender and race as categories within which humans think, in short, sorry, studies


of discriminatory systems, we need to ask who is harmed, who benefits, who gets to decide.


So it's kind of who bears the cost, who bears the benefits, and who has the power. So that's


the and again, it's, we seek to understand how AI disadvantages some, we also consider


how it works to the advantage of others. So keep that in mind. That's kind of the lens


through how they analyze the this thing, again, one that acknowledges power relationships


and centers, equity and justice. That's the they they want to see this bigger picture.


So that's Yeah, keep that in mind. So they go into a section called, which humans are


in the loop, how workforces and AI systems interact. So this kind of, from the title


of this section, you think, okay, here's where we get in, here's where we make the argument.


And they start by listing examples of how AI systems can be discriminatory. And first,


they go into an example of Amazon had developed an experimental hiring tool to help rank job


candidates. By learning from its past reference preferences, Amazon hoped that the resume


scanning tool will be able to efficiently identify qualified applicants, comparing their


applications to previous hires, the system quickly began to downgrade resumes from candidates


who attended all women's colleges, along with any resumes that included the word women's.


After uncovering this bias, Amazon engineers tried to fix the problem by directing the


system to treat these terms in a neutral manner. The company eventually abandoned the tool


when they were unable to ensure that the algorithm would not be biased against women. Gender


based discrimination was built too deeply within the system, and in Amazon's past hiring


practices to be uprooted using a purely technical approach. So this just the way is written,


I find to be, I find to be quite dishonest. But let's, let's analyze what, what happened


here. So their final claim is that gender based discrimination was built too deeply


within the system to be uprooted using a purely technical approach. So this is one of their


arguments they say, technical approaches, they don't help because the Amazon engineers


tried to fix the problem, right? But we're when they were unable to ensure that the algorithm


would not be biased against women. So if you if you read this, you really I mean, I really


get the impression that's not what happened here. What happened here, most probably is


Amazon built this tool, okay, and it fed in its past hires. And we know of issues of like


data set bias bias inherent in data sets. So if your data set is skewed, the the AI tends


to pick up on the skewed data set and becomes skewed itself. Okay, so I actually would argue


that most or all of the examples they stay stayed in here are examples of such biased


data sets and not. So the the cause of the bias is the data set that they are strained


on and not the person that ran the code or built the algorithm to train it on or built


the deployment. And so but it doesn't matter your your your Amazon, you built this tool


and you realize, oh, it discriminates against people having women's on their CV. So this


is a pretty bad PR wise. So you tell your engineers, engineers fix the problem. So the


engineers go fix the problem, they come back and say, Okay, we fix the problem. And then


what you do is you say, Okay, engineers, can you ensure me that the algorithm would not


be biased against women? Because if only the slightest bias exists, if only it doesn't


even have to be if one journalist finds one example, where there is a down rank, because


I add the word women's, then we are screwed, right? And the engineers will say, No, we


can't guarantee that it's a deep learning system or something, right? We, we can't,


like give you a proof that it's not biased. If you're a smart executive at that point,


you'll scrap the tool, because the potential PR downside are just huge. And probably they've


also realized it's not that handy to have this, this tool compared to their recruiters


doing their job, because their recruiters might actually be, you know, good and have


been doing this for a while. So to the, to the fact that this tool was scrapped is probably


much more a result of a PR disaster. But also independent of that, to say gender based discrimination,


sorry, gender based discrimination was built too deeply within the system to be uprooted


using a purely technical approach. It's just, I mean, what is what is this? This is just


trying to discredit this kind of technical, technical going about solving this problem.


But I'm pretty sure if someone comes to me and says, here, I have this tool, and I can


mathematically prove to you that it's not biased, then it's not, then the problem is


solved. And also, I really don't see how the person training the algorithm, or the person


researching such an algorithm has any influence over how the algorithm works, because they're


not the ones making the data set. Or if they are, yeah, then they can make a better data


set. Also, if a person comes and makes a better data set, that will fix the problem. And it


doesn't matter what skin color the person has that makes the better data set. So all


of this, this link is just not demonstrated here, or anywhere here at all. But this, this


here is the closest Amazon that this report actually comes to making this point. So before


I wrote, I drew this thing workforce AI bias, right? So this this link, since it here, the


AI system is used for hiring the workforce. So at least one could make a claim that this


link is somewhat demonstrated. But I this, it's a weak case, I would agree, but this


is the closest they come. So that and but then to go this direction, you have to somehow


argue, well, the workforce somehow makes the AI system bias, no, the workforce influences


the data set, I, if the AI is trained, so if a hiring AI, how do you train a hiring


AI, you optimally train it on the performance. So this this employee here is going to have


a performance over time, right? And the AI system will look at that performance over


time. So if the AI system, even if it's initially biased, because it learns from the risk recruiters,


it will learn that, okay, actually, if I always forego these women, then I don't get as much


performance of a workforce. So I should correct for that. So if you train the AI system on


a good metric, then then then this problem will even out itself. So but again, this Yeah,


this this is this could be considered like, one point in the argument, but I think it's


a very weak point. And only because the AI system is actually used for hiring, where


I think the point they're making is a much larger one is the general bias in the AI systems


contributes to the workforce imbalances. And there you somehow have to say that, okay,


the AI system somehow influences society at large and society at large, then go leads


to the workforce being skewed. I don't Yeah, that it's just not strong enough, in my opinion.


And the other direction also isn't isn't strong here. But again, the examples only get weaker


from here on. They go on, they say, this is just one of many examples that show how the


functional logics of a given technology echo the gender and racial dynamics of the industry


that produced it here. Yeah, this, that's the claim they're making to echo the gender


and racial dynamics. And they're actually making a stronger claim, namely a causal claim.


They give the other example of the Amazon's recognition facial analysis service previously


demonstrated gender and racial biases worse than those of comparable tools. So it failed


to see dark skinned women while being most proficient at detecting likes light skinned


men. And they later go into this example again, where they basically also state, yes, this


is an issue of the data set, the data set being much more comprised of white men. And


they say, but then they have to kind of make the turnaround argument and say, well, the


data set is a reflection of society. And society, you know, part of society is the workforce.


And it's just not, I mean, it's, again, this argument only works if you already believe


the conclusion. Otherwise, there's actually no argument there, or no solid one. But what


they do here is they say, Amazon's initial response to such criticism has been to try


and discredit the research behind it. This reaction, or let's let's first discuss this.


So the Amazon, yeah, Amazon, of course, being the accused here and a multi billion dollar


company. And the criticism is something that is PR wise very bad for them. They discredit


the research try to discredit the research behind it. It's understandable that this could


be dishonest from Amazon's side, right? I mean, they're getting attacked. It's like,


you know, the tobacco companies trying to discredit the smoking research. But still,


I mean, that doesn't mean it's wrong, it could actually be bad research, right? You have


to actually go and look at what's Amazon saying, what is the research really doing? Is Amazon


right or wrong? completely open that Amazon is wrong here, but you still have to go look.


And this citation here, I've tried this citation here, this one isn't to a to Amazon's response.


It's to like a medium article. And the medium article doesn't even include Amazon's response


F of look, maybe haven't seen it. It doesn't also doesn't link Amazon's response, maybe


link something that links something or that includes it in some way. But basically, this


media article only states Yeah, Amazon has been denying this or Amazon has been critical


of this. And if you state such a sentence, Amazon's initial response to such criticism


has been to try and discredit the research behind it, I at least expect the citation


to lead me to Amazon's response so that I can verify what they're saying. Right. So


this I mean, I don't know willing to chalk it up to incompetence, rather than malice.


Right, but then they go on and they say this reaction is evidence of the wider problem.


The research was conducted by two well regarded AI researchers who are women of color. By


attempting to publicly discredit their expertise and research methods. Amazon is reinforcing


the same kinds of prejudice and erasure that the research critiques. Yeah, here you go


straight to the identity of the researchers, you like, play the race card straight out.


I mean, this is this is maximum dishonesty, right? Except if Amazon said something like,


well, these women of color, clearly, because they're women of color, they have no idea


what they're doing or something like this. This is basically it's coded language for


saying either saying you're not allowed to criticize people of color because they're


a minority. Or you're basically saying, Amazon is racist, and that's why they criticize them.


They they just don't take them seriously because they're women of color. I mean, both are both


are a porn. This is just dishonesty. Really stated here to mean, again, I'm perfectly


willing to accept that Amazon's critique of this research is wrong and is not well intended


because they're the ones attacked, but you still have to examine it rather than say,


well, they did shoot against women of color. And therefore, somehow that makes their their


counter argument irrelevant or even racist or something that's I don't know. I find this


dishonest. Yeah, I don't know about you. Going on. So they they go on and state a number


of examples of kind of examples of bias and discrimination in the workforce. And they


a lot of times they make a mixture kind of of the kind of gender and race imbalance in


a workforce and things like sexual harassment not being taken seriously by the companies


and also the things like gender or race pay gaps, which I'm open to to accept that the


these things exist and are even intertwined. But just to tell you what's happening because


we're kind of skipping, but it's kind of a mixture of these things. So they say these


issues are systemic. There's a close relationship between these workplaces with discriminatory


practices and discriminatory tools, a feedback loop that is shaping the industry and its


tools. So again, here to state, I think I've stated it enough now that we're demonstrated


enough that I'm really representing their arguments as they intended it to namely that


there is this kind of causal links and loop between these two things. Again, they shoot


against the fairness literature by saying, from this perspective, sorry, from this perspective,


locating individual biases within given given technical systems and attempting to fix them


by tweaking the system becomes an exercise in futility. Only by examining discrimination


through the lens of social logics, who would benefits who it harms and how can we see the


workings of the systems in the context of existing power relationships. Again, they


say these issues aren't technically fixing these systems won't help. If that's the problem


and they I agree, if that causal link actually exists, then technically fixing the system


might not solve the problem. Not even sure. I mean, if you technically fix a system like


this, then you technically break the causal link and thereby fix the problem. I would


not sure. But again, this is based on the hypothesis that they've already reached, like


demonstrated their, their conclusion, which they haven't and which they are not in the


entire article. Yeah, so the next section goes into who makes it so I don't know about


you, but this section was titled how workforces and AI systems interact. And apart from one


the AI system being used for hiring the workforce, which is said this one instance where actually


there could be one causal direction from bias to different means representation the workforce


other than that, there isn't really anything in there that really shows how these two interact,


especially in a in a causal way. Right, the next section is called who makes AI as broadly


about the about the gender and race imbalances or not unequal representation in the workforce.


And we're going to skip this diversity statistics that kind of that discuss that diversity statistics


statistics of companies aren't really accurate, or can be no massage kind of by the companies,


which you know, is true. Definitely companies will always try to maximize their profits.


And even if they give out such a report, so that definitely critical thinking is in order.


Alright, so the next section is called the discrimination feedback loop. Right? If so,


if in the earlier section, you felt like here we go into the meat, then you must feel with


this title, like, okay, we're actually going to see how this loop works and how the two


things are really linked, like how one causes the other and vice versa. So let's jump in.


They say AI systems increasingly play a role in our social and political institutions,


including education, healthcare, hiring criminal justice. Yes, therefore, we need to consider


the relationship between the workplace diversity crisis and the problems with bias and discrimination


in AI systems. No, why? I don't see how therefore, but yeah, so I don't see how therefore we


need to consider the relationship. Okay, if there's a relationship, we need to consider


whether there's a relationship. Okay, granted. So they say fairness, accountability and transparency


research is playing an emerging role. And what they mean here is the aspect of fairness,


accountability and transparency research that shows that there is a problem. So I told you,


there's two sides, one side is showing there is a problem in current systems, and the other


side is trying to fix them. So they're very much fans of the side that shows that there


is a problem. And they use show some of these problems here, we've already seen some, but


they show some more like Facebook's ad delivery systems let users to be shown as for housing


and employment in a discriminatory manner. So giving 2019 study found significant racial


bias in a widely used commercial algorithm used to determine whether patients will be


enrolled in care management programs. So the these are these are just examples of these


AI systems being biased. And they so they go into this, say taking a contextualized


view may enable more extensive account and the contextualized view days when they say


this they mean anything more than just a technical approach at solving these problems. More extensive


account of bias to emerge future work could examine the politics of system design study


how AI systems in situated reality and study AI systems in situated realities, ask why


a system was designed in a particular way, how it was constructed, whose interest it


shaped shaped by the metrics in which its success or failure is assessed, rather than


solely focusing on improving existing data sets or individual algorithms. Yeah, I agree.


I mean, we always have to, we always have to pay attention to these things, especially


like looking at the metrics by which its success or failure is assessed. But a lot of times


this is this is rather straightforward in kind of, if you look at the metric, the metric


most often, especially in commercial applications is money, right? So the metric of like an


ad showing system, like if I have a system to recommend ads to people show people ads


and personalize them and so on, I simply want to maximize my revenue. So I want to sell


someone something. And everything I want to know is how likely is it that person is going


to buy that thing, right? I, that's basically, yeah. So, in essence, sometimes, it's really


valuable to consider what capitalism is. So in capitalism, in so capitalism, these kind


of this system we're working on is kind of a form of limited capitalism, but mostly mostly


capitalism. And capitalism is very greedy. So capitalism, all corporations want to do


basically is make money. And that is, and on the other side, you have discrimination.


So discrimination, meaning these unequal represent, like unequal distribution, actively. So, and


often, sometimes these go hand in hand, sometimes you can make more money by discriminating


against a certain type of people. And that's, that's a really bad scenario. Like that's


a very, like, this is really something where we need to take action. But a lot of times,


a lot of times, these two things stand in opposition to each other. So a little arrow


here, non compatible. That means if I want to sell someone something, then I maximize


my profit by not caring by accurately assessing how likely is it that person buys that thing.


If I want to discriminate here, if I want to discriminate, start discriminating, according


to skin color, saying like, No, I don't like that this person with the skin color is able


to buy this product, I want to kind of keep them down, and so on, then I forego profit,


right, then I actually, even though this person could buy this thing, I am forego that. So


often these things are in direct opposition to each other. Also, if I am in charge of


hiring, and I don't like people of a certain gender, but they would actually be really,


really good, whatever, good employees. So I forego that that means I'm getting I pay


more for less qualified people just because I'm, I'm biased, and I'm down ranking unjustifiably,


these people of the gender I don't like. So oftentimes, you have to ask yourself, are


people fundamentally greedy, or discriminatory? Which are they more if push comes to shove,


would they rather have more money? Or would they rather keep their own race and gender


group in power? And with just Yeah, so so so the and you have to ask this of corporations,


you have to ask this of people. And in my experience and view, like people are much,


much more greedy than they are willing to discriminate and give up money for discrimination.


And so if if we look at metrics by which it success or failure of AI systems are designed,


I would I would argue, a lot of the times metrics are actually profit incentives. And


especially if we look at data set construction, if there is a skewed data set, that makes


my AI system be biased, that actually loses me money, and the company would profit a lot


from building a better data set. So looking at kind of metrics actually makes a lot of


sense to me, and very much in favor of that. And I think by by designing accurate metrics,


and then getting the best possible information, the best possible data sets to maximize these


metrics will oftentimes actually eliminate such forms of discrimination. Again, there


are situations where they don't, we have to be very cognizant of these, they go into this


and they say, also examine more thoroughly how societal discrimination surface in data


provenance examining the history and process of data set construction. And considering


how cultural norms and stereotypes were enumerated and represented at the time of data creation.


This is a big issue, yes. So the data set construction kind of at the time of data creation,


and so on, this is a big issue in these systems and a lot of bias. And I would argue, most


of the bias we've seen here arises from corrupt data sets and from data sets that were constructed


in an already biased way. And the AI system trained on these data sets simply replicates


this bias. So they, I think that's very correct here. They go into this example, they say


the labeled faces in the wild data set contains over 15,000 images, only 7% of images are


of black people. And this is because these, the media landscape of the early 2000s, these


images were gathered from the news media at the time predominantly featured white men


in positions of celebrity and power. This, exactly. So if you train a system on these,


then this data set, the system will inherit this bias. Yeah, so this is a classic example


of a corrupt data set. Also, I mean, this isn't only with with race and gender. This


is also if you like, take pictures from IMDB, yes, you know, a lot of this currently Celeb


A data set that is used in all the GAN research is collected from IMDB, you probably have


overly beautiful, like pretty face people on there like that. So the your AI system,


your generative model is only going to produce mostly pretty face people. Since movie stars


tend to be a lot prettier than the average humans. So the kind of data set construction


process, I think is currently the biggest source of bias in AI. But that also, it's


interesting that they go into this here, and they kind of want to make the point that this


is, you know, this is because society, and you know, power in society, the data set reflects


that. But I would argue, if someone makes a data set that doesn't have this bias, then


the problem is solved. And I don't care who makes the data set. So the link between the


workforce and the bias is really broken by an argument like this, because as soon as


we have a correct data set, an unbiased data set, we can mitigate the bias. And they even


go, they go into this here. They say, sorry. Yeah, they say, down here. They say these


people, this, these researchers have looked at these facial recognition systems, and they


assessed this, what we saw earlier, higher error rates for darker skinned women than


for any other group, lowest error rates for light skinned men. To measure this disparity,


these researchers developed a new data set that is more balanced both in terms of gender


and skin color. Good, like problem, like make a larger data set to actually train on. And


then problem solved. Like, and I don't, I don't care at all, what race and what gender


these people are. Well done. Like, good people make a good data set like this. And then we've


solved the problem. What's the problem here? Like, why would you ever care what these people


look like? If they if they do good work? That's it to me, this this just this, this actually


breaks their own argument. I don't know why they included here. But yeah, to me that the


to then suggest that there is a link to the workforces if if it's, if here is obvious


that if you fix the data set, you can fix the the recognition system. All right, so


we'll go on here, jump a couple more paragraphs. Except one they say they shoot again against


this kind of say to this point, a focus on fixing technical systems in isolation without


examining their broader context of use and power and dynamics that attends issues is


not limited in its intervention, it can actively cause harm. So if you fix the problem in a


technical manner, they argue here it can actively cause harm. And the example they give is that


facial and image recognition systems, they are often applied in service of police surveillance,


which disproportionately harms poor people and communities of color. So the this person


is a quote from this person that says it is not social progress to make black people equally


visible to software that will inevitably be further weaponized against us. We are considered


criminal and more surveillable by orders of magnitude. Whatever claim to a right of privacy


that we may have is diminished by a state that believes we must always be watched and


seen. So this is an example where by improving the facial recognition for black people, it


makes them makes the police better at surveilling them, which is true. And then it is an ethical


problem that the police is able to use these facial recognition systems to surveil people.


That's a massive privacy problem. It's a massive problem in how much the state is allowed to


overreach and so on. So I think it's a discussion itself. But here they, they argue, because


at the very beginning, I asked you to remember the this whole notion of we always have to


look at who benefits from the way the AI system is constructed, who who is harmed from that,


who is who benefits from how the metrics are shaped, and so on. In this case, we actually


have a perfect example, where if the if the face recognition system is very inaccurate


for black people's faces, that actually helps them in the societal context. So by logic


of this report here, that must mean that somehow the the bias like the the bias works for them,


and thereby the system is good or something like this. And by fixing it, you actually


make it worse. Yeah, they say it can actively cause harm. So I think this is a pretty much


arguing against themselves earlier where they say, Oh, we always have to look at who benefits


from the system. Yeah, here, if the face recognition system can't recognize you actually benefit.


So I don't think that argument works in any case, except in if you only look at it when


you want to look at it. All right, so this, we're gonna jump a couple of sections here.


But the the the core thing here was the feedback loop. And again, the feedback loop isn't demonstrated


at all here, just examples of systems that are biased, and of data sets that are biased,


or because of data sets that are biased. But there's no demonstration of how the workforce


how I mean, yeah, to take this previous argument. So the workforce is supposedly supremely white.


And it makes a face recognition system that makes that is performing poorly for darker


skinned people. And that actually, in this context of police surveillance helps the darker


skinned people compared to the lighter skinned people. So that kind of exists in exact counter


example to the argument that the but this misrepresentation in the workforce leads to


the biases in the system. If we interpret it through the lens, who who it costs and


who it benefits. All right. So the next section is corporate diversity beyond the pipeline


problem. And this is kind of an odd inclusion when I read it first to interpret to go against


the pipeline problem here. But it kind of makes sense if you if you know what these


people set out to do. So what these people set out to do is to argue, we must fix the


workforce, right? We must fix the we must hire more people of color, more women and


so on, promote them more. So they have have a very much have a problem with this pipeline


argument and what the pipeline argument is, is the following. So at the beginning, if


you if you consider like the educational career paths of people, again, you have like 100%


of people that's represented this at the beginning, and then most of these people go through school.


So most of these go on, this is kind of the area in here is the population. And then some


of them pursue higher education, like some dropout. So this gets a smaller amount. Go


on. So this is here, this is time, and this is kind of volume of people. And then very


few go into computer science, right? And then even fewer go into AI. So what you end up


is just a tiny sliver of people that actually go into AI. So this is called a pipeline.


And we have various junctions here, like where you would go into higher education, where


you would choose your major in university, where you would go into a subfield of computer


science, where the kind of volume of people drops significantly from one point to the


other. And now if you compare this, if you compare this, and you say, we're not consider


all of society, but here over here, we'll call consider all just men. And over here,


we'll consider all women again, they all go to high school and then university and then


maybe very few go to CS, even fewer go to AI, what you'll find is and I've drawn it


maybe wrong here is that this is smaller than this. So if you comparatively look at how


many male males end up in the AI field, you will find that fewer end up in more and will


end up in a field than women, if you comparatively look at it. So at and this is over time, like


at the beginning, you have 5050, men, women distribution in society, almost, I guess,


slightly more boys are born, but I could be wrong about this. And then as you go through


time here, this skews, I believe. So you go through high school, and let's just assume


like high school is still kind of equal depends on the country, then you go to university


where is actually more, more women at university slightly. And then you go into computer science


and in computer science, and this is just relative here, that's why I kind of norm it


at 100%. Otherwise, these things will go down all of them at the same time. But comparatively,


you have then much more men than women in computer science, right. And then, if you


see who chooses AI, I don't know if there's any statistics of specifically choosing AI


from computer science, I'm just gonna assume that remains the same. So if you look into


the AI field, kind of this, this will stay the same. So in the AI field, you have much


more men than women. And presumably, because you already have much more men than women


choosing computer science as their major or choosing any technical field as their major.


This, this is kind of the so called pipeline argument. So where do AI companies hiring


come in AI companies come in here, they hire at this point, after your university degree,


presumably, there's exceptions, but just say they hire after your university degree. And


therefore, they basically have to choose from this distribution. And if they just say, Okay,


we'll just take the, the top, I don't know, 10% people will hire the good people of this,


we don't care what gender they are, right. So the top 10% here, the top 10% here, then


this will end up being the same distribution, as you have graduates. Right. So this is kind


of the company, company hiring from an, let's say, an 8020 distribution, without looking


at gender will end up with an 8020 distribution. That's the pipeline argument of companies.


And they don't like the pipeline argument, because the pipeline argument basically says