AI VILLAGE - The Great Power of AI Algorithmic Mirrors of Society
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Caliskan, Aylin
Following the progress in computing and machine learning algorithms as well as the emergence of big data, artificial intelligence (AI) has become a reality impacting every fabric of our algorithmic society. Despite the explosive growth of machine learning, the common misconception that machines operate on zeros and ones, therefore they should be objective, still holds. But then, why does Google Translate convert these Turkish sentences with gender-neutral pronouns, “O bir doktor. O bir hemşire”, to these English sentences, “He is a doctor. She is a nurse”? As data-driven machine learning brings forth a plethora of challenges, I analyze what could go wrong when algorithms make decisions on behalf of individuals and society if they acquire statistical knowledge of language from historical human data. In this talk, I show how we can repurpose machine learning as a scientific tool to discover facts about artificial and natural intelligence, and assess social constructs. I prove that machines trained on societal linguistic data inevitably inherit the biases of society. To do so, I derive a method that investigates the construct of language models trained on billions of sentences collected from the World Wide Web. I conclude the talk with future directions and open research questions in the field of ethics of machine learning.
 Hi everyone, thanks for coming to my talk. Today I'll be talking about how we can uncover bias in different machine learning models and what the implications might be. And I'm Eileen as John introduced me and I just became an assistant professor and today is my second talk as an assistant professor. Yesterday was my first one. And yes, let's see, is this working? No. Yesterday I talked about supervised machine learning being used on language of individuals so that we can identify these individuals based on their linguistic style. And this is a serious privacy concern. But when we look at language at the aggregate level, language of society, we see that there are some fairness problems there because this linguistic data that is coming from society also brings the biases of society, humans with it. And now we are going to be looking at this. And yesterday what I talked about was about coming up with a method to identify a style of individuals or these can also be programmers so that we can do attribution. And this has security enhancing properties but at the same time we see that it's very privacy infringing in some real world cases. And today I'll be talking about a method for quantifying and detecting bias in linguistic data or linguistic machine learning models. And for this, I basically adapt the implicit association test for humans to machines. And given that there's the language universal that in every language semantics happens in the same context window, the method that I came up with can be used in any language basically. Okay. So in AI, under the umbrella of AI, today we'll be looking at natural language processing and machine learning and in particular deep learning and unsupervised learning. In the past, there has been some work on looking at supervised machine learning to see where the bias might be happening, how it can be removed, but it's slightly more difficult to understand these in unsupervised machine learning models since they don't have direct classification outputs and so on. And again, talking about, for example, individual language versus society's language, when we look at individuals' language, the syntax that they use in language is the most identifying thing even in source code. But today we'll be focusing on semantics, the meaning in language at the societal level. And just a brief summary of this work on security privacy and machine learning where I'm using stylometry, which is the study of linguistic style. And since this is linguistic style, we can be looking at natural language or we can also look at artificial languages such as programming languages. And some examples are identifying the authors of English text, English as a second language so that we can identify their native language or translated text and identify the native language or translator as well as underground forum text where underground forum users engage in business transactions. And even though these are very noisy data sets, we can still identify these authors and sometimes these are suspect sets for intelligence agencies. And because of that, this work, the tools that we developed and that are open source on GitHub, any one of you can download and use, they're currently being used by the FBI or expert witnesses to be used as scientific evidence in court or European high-tech crime units so that they can identify suspects online based on their language. But again, I'm reminding you, this is about individuals' language and the privacy implications that it has. And in artificial languages, focusing on programming languages in particular Python, C and C++, source code as well as binaries, this work is being used by DARPA and being part of the Department of Defense, you might imagine why they would be interested in attribution problems, for example, to find the authors of malicious software or in oppressed regimes, authors of, for example, censorship circumvention software. And expert witnesses, again, can now use this information as scientific evidence in court. And I've been collaborating with the US Army Research Laboratory, they are still working on programmer, de-anonymization, programmer attribution. Okay, but today I want to talk more about fairness and language at societal level. And I'll start with an example. So the, do any one of you know this author, Robert Galbraith? Okay. So there was this novel, crime novel called The Cuckoo's Calling by Robert Galbraith, but some people suspected that it might not be written by Robert Galbraith, but instead J.K. Rowling. And after performing stylometric analysis, it was shown that it was actually written by the famous J.K. Rowling of the Harry Potter series. And after that, J.K. Rowling said that, okay, yes, she's the author of this book, but she wanted to use a man's name because it's a crime novel and the publisher also thought that it would sell better if it was published under the name of a man. And at the same time, they wouldn't know that it was written by J.K. Rowling, so she would get a more realistic evaluation of her work. So we can see even at high-profile people how biases affect in society, even for such an important product that they are publishing. And here, this reminds me of the interplay between privacy and fairness. Big data is evil twins. This is what I call them. For example, with privacy, we can have a serious problem when sensitive information is leaked. But with fairness, when sensitive information or protected attributes are abused, then we have a fairness problem there. But in the upcoming slides, we are also going to see that privacy does not imply fairness. Okay, let's start focusing on natural language processing models, linguistic models, semantic spaces. In machine learning. For example, recently, Google had this Cloud Natural Language API. Amazon, Google, many companies, researchers are making these tools available or for commercial purposes as well. And such tools are being used by developers, researchers, just random folks, citizens, and so on. And we can also see that whenever we are dealing with a smart or digital application, if there is any text included in it, then usually these linguistic machine learning models are also being used. For example, Web Search, when you're trying to type something and you can see some suggestions to fill that sentence, which is sequence prediction or text generation. At the context level, they are also looking at linguistic, semantic spaces or machine translation, sentiment analysis, especially for market predictions to see how a commercial product is, for example, perceived. Is it negative, positive? And of course, it's using linguistic data and tokens, words as tokens that are in certain context windows or named entity recognition and text generation. When you're receiving an automated call on the phone, usually that text is automatically generated. Okay, why would this be a problem? Let's look at this example. So one of my native languages is Turkish, and Turkish is a genderless language. There are no gender pronouns. There is one pronoun, and it means he, she, or it. Okay. So I'm translating from English to Turkish. She's a doctor, translated as he, she, or it is a doctor. Taking the Turkish sentence, translating it back to English, it's translating as he's a doctor. It's not even asking if it should be a he, she, or it. Okay, let's say that it's smart enough to understand that this is a human, so it shouldn't be an it. It's still saying, not saying he or she, but just choosing he as the most accurate answer. And another example to see if this is just one rare exception or is this happening at the larger scale. He's a nurse, translated to Turkish. He or she is a nurse. Translated back to English, she's a nurse. Okay. You can maybe also see the difference, doctor versus nurse, the prestige or the salaries of these people. And we see a pattern here. He or she or it is a professor. He is a professor, teacher. She's a teacher. Okay, is this only happening in English? German is also a gendered language. Not only pronouns, but other things are also gendered. It's more gendered than English and of course from Turkish. But again, we see that a doctor is translated as a male and a nurse is translated as a female. And then we have Bulgarian. And in Bulgarian, almost everything is gendered. Verbs are gendered. Adjectives are gendered. And again, we see that doctor is a male, whereas a nurse is a female. Okay. So it has been 62 years since they came up with the term. I believe it's still 62 years. They came up with the term artificial intelligence and what they, what can be done with it. All doomsday stories about when there is super intelligence or when machines are going to take over and so on. But they haven't been really thinking about immediate problems we might have with artificial intelligence. Okay. We know that when garbage goes into machine learning models, what comes out is usually garbage as well. Because if that's the quality of your training data, then the output would reflect the same quality. And one example here for how text is collected for generating semantic spaces and linguistic models is, for example, there was the Microsoft Tweetbot, and it was taken down the same day it was introduced because it very quickly turned into a very offensive, racist, biased bot. And Microsoft wasn't ready to account for such cases in the linguistic model and this was done by Fortune. It was basically model poisoning. It was adversarial machine learning and it worked very quickly. And these were some of the examples that the Tweetbot started tweeting. And of course it was taken down, but we can see how easily the bias can be embedded with a strong effect size in this example within a few hours. And how is this getting into these models? Let's take a step by step look at this. We know that humans are biased. This is not necessarily a bad thing because there are neutral biases as well and sometimes biases are helpful in some conditions and I'm going to give some examples of those as well. But when we're biased and when we're speaking, we reflect that bias in our semantics. We have valence, for example. We say that, okay, the snake is ugly or this butterfly is beautiful, for example, and these are neutral biases, but they are there. And as we are speaking, forming languages, we tend to have similar patterns in same context windows. Let's say that we have a negative context window and snake tends to appear in that context window as a neutral bias, but it's still a negative bias for snakes. And that's called then distributional meaning. We see in the statistics that certain words end up in negative context, for example, and context windows. And after that, machine learning models, especially semantic spaces, look at this distributional meaning and co-occurring statistics and after that, it learns the co-occurring statistics of certain terms or certain people's names and what they are associated with and this is reflected in machine learning models. Basically the bias is propagated through this entire process and sometimes it is even increased and augmented. It's not just perpetuated. How can we measure it though? Because especially when we look at unsupervised learning, we don't really have something to directly control for and measure these things unless we look at the model at the construct level, like the intelligence of this model, let's say, the understanding of the world that this model has. And for humans, the implicit association test has been used as a way to measure the implicit biases that we might have. Greenwald from University of Washington came up with the implicit association test in 1998 and there's a lot of criticism about this method as well, but at the same time, it's revealing some patterns about the world and humans and it's revealing subconscious biases that we might have and we might not be even aware of and this test is basically asking you to associate certain societal groups, members or terms with certain stereotypical words and how fast do you associate, for example, the butterfly with being negative or positive versus how fast do you associate a snake being positive or negative and then when you're doing these associations on a computer, you are asked to click right or left to classify a positive term with butterfly or snake and after that, there is a difference in reaction time and this differential reaction time in associating congruent stereotypes and incongruent stereotypes gives you the effect size for implicit bias that we might have and this example is from a girl taking the implicit association test for males versus females about science versus arts and the bias in general is that males are associated with science whereas women are associated with arts and you can go and take this test online at the Harvard website, implicit.harvard.edu implicit and I'm showing this because we see a few implicit association tests here that are listed and in the experiments that I did, I took these previous experiments that have been generated by experts in societal psychology because I'm not an expert in that area at all and I wanted to see if we can replicate these biases versus what happens when I try random things or things that are not biases so that I'm not just cherry picking the biases that I want to show you. You see that these are the main categories that were there and I'm using the same ones to see if they are reflected because the other thing is this test is taken by millions of people over decades and another example is you can also take it in German if you are from Germany let's say because the same tests since they are based on context and words they can exist in any language basically and we can generate linguistic models in any language as well so we can apply these tests in other languages for other cultures or countries. Okay let's start looking into the details of generating language models. Another example here of where text is taken to generate these models basically we can crawl the web, take all kinds of text, structured, unstructured tweets here we see some tweets from Donald Trump before he became the president but we see a certain pattern in the text about maybe certain biases and so on and this is blindly fed into in this case neural networks and the neural networks looks at the co-occurring statistics and all the data point wise material information it has and after that it produces a semantic space. In this semantic space we have the common example is with 300 dimensions we basically have a dictionary of a language and then this dictionary each word contains each word in a language with in a vector form numeric vector form with 300 dimensions where each dimension is a combination of certain context but when we have each of these words we see that similar words are projected to the same points in that space for example things about a positiveness, things about feelings, things about females and so on and based on their vicinity we can understand or answer many questions and the types of semantic spaces that I focused on in my study were Word2Wake the algorithm and Glove from Stanford researchers. Word2Wake when it was introduced it was extremely popular and the models that was produced by Word2Wake which is from Google research it's being used by many developers, researchers or app developers and so on so a lot of people use these for their applications and Glove is similar to that as well it's produced by Stanford researchers around the same time and these two semantic spaces have about the same accuracy after evaluation even though it's not very clear how to evaluate these methods it's like an approximation of an evaluation for these semantic spaces and Word2Wake is based on Google news data and we would expect Google news data to be more neutral and objective because it's news data but we see that that is not the case based on concurrent statistics and other type of data that Glove uses is Common Crawl it's about 800 billion tokens from the internet it's basically the crawl of the World Wide Web basically the language of internet users I would say in this case not in particular society in general but the internet using population. Okay what can we do with this word embeddings? First of all we can understand syntax we can understand the meaning of a word better we can perform analogies and get the answers or we can look for semantic similarity let's see if I have this example no but we can ask questions such as okay Rome is to Italy as Paris is to what and it will be able to answer that it's France so it has some understanding of language it has understanding of semantics but there is also knowledge and maybe even statistics embedded here and by statistics I don't mean the concurrent statistics I mean statistics about the world and looking into the details of these vectors we have each word listed by frequency and the first word is usually A or D or just a comma and so on and there are 300 features that represent this word in the semantic space and in globe we have about 2 million tokens 2 million words in this dictionary and in a dictionary you would expect much less words but given that this is taking all the words above a certain frequency on the internet we have words such as Obama or Michael Jackson and so on. Okay what can we do with vector arithmetic so when we project these vectors to 2D space we see that for example I hope at least the laser is working no because I didn't turn it on okay now it's working. Okay here we see that on the lower side there is brother which is a male and here we see sister and now we see the direction of gender here once we see the direction of gender maybe we can look at kink and find what corresponds to the female version of kink which is queen here so basically we can perform vector arithmetic with cosine similarity most of the time or taking the principal components of these vectors and try to answer the questions that we have in syntactic form, analogy form or semantic form and so on. How can I use this information to measure bias in machine learning models? So the first thing I came up with was the word embedding association test instead of the implicit association test and for this what I do is I try to quantify the implicit or in this case it's actually explicit and deterministic associations between societal categories and evaluative attributes which are stereotypes in this case and what I do is I take the distance between the stereotypes and societal groups for the two groups and look at the difference in standard deviation between the means of these associations and that gives me the effect size of a certain bias and we can also measure the statistical significance by generating a null hypothesis and then see if the result that we are getting from the effect size is significant or not and for this the first thing I wanted to start with was looking at neutral stereotypes that are universally accepted or they are called so for example flowers being considered pleasant and insects being considered unpleasant for some reason apparently most of the population just naturally intuitively have this stereotype or musical instruments being considered pleasant whereas weapons are being considered unpleasant and since this is not dangerous for society or harmful to society it's considered neutral and in this case we see that the effect size is 1.5 around 1.5 for both of them and this is a large effect size if it's above 0.8 and the highest effect size can get is 2 because it's bounded by the standard deviation here and we see that these are both statistically significant and high effect sizes and for the upcoming examples it will be the same case they are all statistically significant with high effect sizes. Okay let's start looking at other major implicit association test categories such as white people's names versus black people's names and trying to understand if they are considered pleasant or unpleasant and we get the congruent stereotype that white people are considered pleasant in this case and when we look at the differences between genders we see that males are associated with career and females are associated with family and again I'm using the same words that the implicit association test is using to ask you to perform the classification association test here the exact same words and when we look at science versus arts again we see that males are associated with science and females are associated with arts I'm not going to talk about stereo threat here but at least we can see that bias is certainly perpetuated with these linguistic models and the two examples that I have with models here are from 2014 they are in 2018 and these are still the state of the art models that are being used by many people and they are not really updated on a frequent basis because they are quite large files and they require a lot of data and so on. Okay let's look at some other health related stereotypes for example young people being considered pleasant whereas old people are being considered unpleasant or physical diseases considered controllable whereas mental diseases being considered uncontrollable and we see the stigma being reflected in these models or when we look at for example heterosexual versus homosexual individuals we see the attitude towards the more straight versus trans gender and so on and we can also perform these in German when we look at the main categories after generating the linguistic model and you can also generate your linguistic models for example you can download corporate online or use Google and grams data from different years decades countries languages and so on to analyze what might be going on in these years and when we look at the most recent version of Google and grams for German we see that we can also replicate the stereotype or the prejudice for Turkish people Turkish people went to Germany decades ago millions of them as immigrants and there isn't a very positive attitude towards someone we are able to replicate that from Google sign grams data as well by performing the VFET test sorry the V test. Okay we saw that semantics and bias is embedded in these models what about empirical information that doesn't depend on a context or a feeling but it's more about the statistics in the world can we replicate those as well or can those be a reason for these biases for example let's say that I would like to know how a certain name is associated with being male or female Taylor for example it's an undruginous name and it's almost 50-50 male and female and based on this I can do I can perform a similar computation to see how much a word is associated with a certain stereotypical group and based on this I went to US I went to I collected data from the US Census Bureau 1990 I believe data where they included the gender of people with certain names and how many of them were out there so I took all of these names the most frequent ones and tried to calculate their association with genders being female or male and we see that this undruginous names had 0.84 correlation coefficient so it's almost about let's say 84% similarity in the statistics of the world versus the association that I'm getting only from a semantic model which is very interesting to me and I want and this is a illustration of that we see here that for example Taylor is in the middle almost white 50% male 50% female from the effect size that I'm getting which is almost 0 in this case and we see that Carmen is almost 100% female whereas Chris is 100% male okay what about if you look at employment statistics occupation statistics based on gender and the Bureau of Labor Statistics publishes these disinformation every year and based on this I took the data from I'm trying to see which year this was it might be 2017 and I took the occupation names and then tried to see their association with certain genders and the correlation coefficient was 0.9 which is amazing and after that when we look at the result we see that a programmer is oops where is my it's been reflected from the mirror I don't want to blind anyone so I'm not going to use it but on the upper left side where we see that programmer is almost a hundred percent male whereas a nurse is almost a hundred percent female and when you look at for example Google Google engrams searching for she's a programmer usually the result you get is zero because things that are below a certain frequency are just cut from the engrams and until recently she's a programmer was zero and we can see that here reflected here as well okay now we can understand that there are different types of bias that are embedded in semantic spaces and there might be different reasons why these are getting in these models but we can come up with three main categories for types of bias that we are dealing with the first one is the vertical information gender and occupations for example and this is not exactly bias it's basically the statistic that we have in the world and these might be caused by the injustices in the past or biases in the past but we don't know any information about that we just have the statistics here and the models are learning from these statistics but we also see that the universal biases are embedded in these models as well as things that can be really prejudice such as black versus white names being considered pleasant versus unpleasant but I would like to remind that in some cases you want certain types of biases to be in your machine learning models because they can be very useful it depends on what kind of test you are dealing with and some people have suggested fairness through blindness basically just remove redone just remove protected attributes or devise the system by completely taking the bias component from the vector space from all words but this cannot be the right solution to this like just turning a blind eye to this because first of all once we remove this information we see that we're also removing statistical information about the world and the other thing would be we would end up with redundant encodings which wouldn't have the same quality as the previous ones and we don't know what exactly we are losing here and another very important example is having proxies even if you remove protected attributes there are still proxies for bias to take place for example when automated systems are deciding to give loans to certain people the zip code is a proxy to the address so even if you remove all protected attributes the zip code since it's a proxy to having a certain financial status it would give you the redlining example where certain people are just denied loans because of their zip code so and by the way in law the main discrimination criteria is using protected attributes for discrimination and if those protected attributes are removed and if you are proxying from the zip code it's completely okay to be using the system like that right now and I'm suggesting inside fairness through awareness first of all we need to understand cultural bias and then based on this bias we need to understand the protected attributes that come with them and we also need to understand the machine learning task for example in bioinformatics or health informatics we need to make sure that there's a certain bias the bias for genders or certain ethnicities or racial backgrounds are taken into account because one example is for cardiac disease symptoms of men and women are different treatment should be different as well and we have to make sure that we are taking this into account in whatever model we are building and I'm saying that fairness is task specific for this purpose and this work was published last year at Science and there were a lot of news about it in media and one of them said after this random screenshot that I took which I really liked because it says in 2017 society started taking AI bias seriously so in 2017 people really understood that there was a serious problem caused by these automated systems and the bias that they are perpetuating in society that's already a huge problem to deal with and this is not happening at the large scale okay what am I going to be working on next so this kind of covers the project that I was working on and I'm trying to wrap up quickly so that you can also ask questions and also I was going to mention in the beginning that this can be interactive but I think it's too late now okay I'm not going to be focusing on the singularity or transhumance or when machines are going to gain cognition because we have much more immediate problems right now um for example focusing on computer vision and um joint semantic visual spaces and these are being current news for example for automated surveillance and computer vision systems are known to have bias as well but it's much harder to quantify those if you're not dealing with supervised machine learning and imagine a system being biased for certain skin colors or ethnicities and how much of a problem this might become because a lot of these automated systems are being used to identify targets or they are even used in war zones or they are used all over the streets to identify for example anomalies for anomalous detection and so on and we don't exactly know how these systems are working yet and they might very well be biased because all the analyzable ones are showing bias. What about algorithmic transparency and interpretable machine learning so for example with driverless cars we know that they have vision systems as well and let's say that this is the classical trolley problem in the current sense where we have the driverless cars is going to crash into someone there'll be an accident it cannot avoid that and it has to decide is it going to crash into the white male executive right across the car or is it going to choose to run into the old black lady on the other side and we don't know the answer to this yet and because of that we have to be very careful about what kind of products that we are building because we don't want to be building the digital analogs of Robert Mussov's racially motivated love overpasses. So Robert Mussov is considered one of the best urban planners he's the planner of New York City but the overpasses that he built all around the parkways were quite low so buses where people with lower financial status had to use public transportation buses couldn't pass through those love overpasses so people had to have their own cars so that they can go to the beaches in Long Island or the parks that he built and with these love overpasses people were basically just separated and this led to decades of segregation and we have to make sure that we are not causing the same problems again with the digital products that we are putting out there blindly and for this I would like to keep working on a fairness framework to uncover bias in artificial intelligence and come up with ways to mitigate it while preserving the utility of systems and come up with fairness algorithms but there are a lot of privacy and security implications here as well for example when we have these machine learning models going from our phones to the cloud or from our fitness trackers to the cloud can we guarantee fairness in a secure and at the same time private way how can we avoid adversarial poisoning for these systems and so on and there are many unanswered questions in this area and it's a very exciting area and I would like to thank all of my collaborators as well none of this work would have been possible without them and I'm really grateful to them and now I think we have a few minutes for questions as well so we have to take questions comments questions anything
 Hi everyone, thanks for coming to my talk. Today I'll be talking about how we can uncover  bias in different machine learning models and what the implications might be. And I'm  Eileen as John introduced me and I just became an assistant professor and today is my second  talk as an assistant professor. Yesterday was my first one. And yes, let's see, is this  working? No. Yesterday I talked about supervised machine learning being used on language of  individuals so that we can identify these individuals based on their linguistic style.  And this is a serious privacy concern. But when we look at language at the aggregate  level, language of society, we see that there are some fairness problems there because this  linguistic data that is coming from society also brings the biases of society, humans  with it. And now we are going to be looking at this. And yesterday what I talked about  was about coming up with a method to identify a style of individuals or these can also be  programmers so that we can do attribution. And this has security enhancing properties  but at the same time we see that it's very privacy infringing in some real world cases.  And today I'll be talking about a method for quantifying and detecting bias in linguistic  data or linguistic machine learning models. And for this, I basically adapt the implicit  association test for humans to machines. And given that there's the language universal  that in every language semantics happens in the same context window, the method that I  came up with can be used in any language basically. Okay. So in AI, under the umbrella of AI, today  we'll be looking at natural language processing and machine learning and in particular deep  learning and unsupervised learning. In the past, there has been some work on looking  at supervised machine learning to see where the bias might be happening, how it can be  removed, but it's slightly more difficult to understand these in unsupervised machine  learning models since they don't have direct classification outputs and so on. And again,  talking about, for example, individual language versus society's language, when we look at  individuals' language, the syntax that they use in language is the most identifying thing  even in source code. But today we'll be focusing on semantics, the meaning in language at the  societal level. And just a brief summary of this work on security privacy and machine  learning where I'm using stylometry, which is the study of linguistic style. And since  this is linguistic style, we can be looking at natural language or we can also look at  artificial languages such as programming languages. And some examples are identifying the authors  of English text, English as a second language so that we can identify their native language  or translated text and identify the native language or translator as well as underground  forum text where underground forum users engage in business transactions. And even though  these are very noisy data sets, we can still identify these authors and sometimes these  are suspect sets for intelligence agencies. And because of that, this work, the tools  that we developed and that are open source on GitHub, any one of you can download and  use, they're currently being used by the FBI or expert witnesses to be used as scientific  evidence in court or European high-tech crime units so that they can identify suspects online  based on their language. But again, I'm reminding you, this is about individuals' language and  the privacy implications that it has. And in artificial languages, focusing on programming  languages in particular Python, C and C++, source code as well as binaries, this work  is being used by DARPA and being part of the Department of Defense, you might imagine  why they would be interested in attribution problems, for example, to find the authors  of malicious software or in oppressed regimes, authors of, for example, censorship circumvention  software. And expert witnesses, again, can now use this information as scientific evidence  in court. And I've been collaborating with the US Army Research Laboratory, they are  still working on programmer, de-anonymization, programmer attribution. Okay, but today I want  to talk more about fairness and language at societal level. And I'll start with an example.  So the, do any one of you know this author, Robert Galbraith? Okay. So there was this  novel, crime novel called The Cuckoo's Calling by Robert Galbraith, but some people suspected  that it might not be written by Robert Galbraith, but instead J.K. Rowling. And after performing  stylometric analysis, it was shown that it was actually written by the famous J.K. Rowling  of the Harry Potter series. And after that, J.K. Rowling said that, okay, yes, she's the  author of this book, but she wanted to use a man's name because it's a crime novel and  the publisher also thought that it would sell better if it was published under the name  of a man. And at the same time, they wouldn't know that it was written by J.K. Rowling,  so she would get a more realistic evaluation of her work. So we can see even at high-profile  people how biases affect in society, even for such an important product that they are  publishing. And here, this reminds me of the interplay between privacy and fairness. Big  data is evil twins. This is what I call them. For example, with privacy, we can have a serious  problem when sensitive information is leaked. But with fairness, when sensitive information  or protected attributes are abused, then we have a fairness problem there. But in the  upcoming slides, we are also going to see that privacy does not imply fairness. Okay,  let's start focusing on natural language processing models, linguistic models, semantic spaces.  In machine learning. For example, recently, Google had this Cloud Natural Language API.  Amazon, Google, many companies, researchers are making these tools available or for commercial  purposes as well. And such tools are being used by developers, researchers, just random  folks, citizens, and so on. And we can also see that whenever we are dealing with a smart  or digital application, if there is any text included in it, then usually these linguistic  machine learning models are also being used. For example, Web Search, when you're trying  to type something and you can see some suggestions to fill that sentence, which is sequence prediction  or text generation. At the context level, they are also looking at linguistic, semantic  spaces or machine translation, sentiment analysis, especially for market predictions to see how  a commercial product is, for example, perceived. Is it negative, positive? And of course, it's  using linguistic data and tokens, words as tokens that are in certain context windows  or named entity recognition and text generation. When you're receiving an automated call on  the phone, usually that text is automatically generated. Okay, why would this be a problem?  Let's look at this example. So one of my native languages is Turkish, and Turkish is a genderless  language. There are no gender pronouns. There is one pronoun, and it means he, she, or it.  Okay. So I'm translating from English to Turkish. She's a doctor, translated as he, she, or  it is a doctor. Taking the Turkish sentence, translating it back to English, it's translating  as he's a doctor. It's not even asking if it should be a he, she, or it. Okay, let's  say that it's smart enough to understand that this is a human, so it shouldn't be an it.  It's still saying, not saying he or she, but just choosing he as the most accurate answer.  And another example to see if this is just one rare exception or is this happening at  the larger scale. He's a nurse, translated to Turkish. He or she is a nurse. Translated  back to English, she's a nurse. Okay. You can maybe also see the difference, doctor versus  nurse, the prestige or the salaries of these people. And we see a pattern here. He or she  or it is a professor. He is a professor, teacher. She's a teacher. Okay, is this only happening  in English? German is also a gendered language. Not only pronouns, but other things are also  gendered. It's more gendered than English and of course from Turkish. But again, we see  that a doctor is translated as a male and a nurse is translated as a female. And then  we have Bulgarian. And in Bulgarian, almost everything is gendered. Verbs are gendered.  Adjectives are gendered. And again, we see that doctor is a male, whereas a nurse is  a female. Okay. So it has been 62 years since they came up with the term. I believe it's  still 62 years. They came up with the term artificial intelligence and what they, what  can be done with it. All doomsday stories about when there is super intelligence or  when machines are going to take over and so on. But they haven't been really thinking  about immediate problems we might have with artificial intelligence. Okay. We know that  when garbage goes into machine learning models, what comes out is usually garbage as well.  Because if that's the quality of your training data, then the output would reflect the same  quality. And one example here for how text is collected for generating semantic spaces  and linguistic models is, for example, there was the Microsoft Tweetbot, and it was taken  down the same day it was introduced because it very quickly turned into a very offensive,  racist, biased bot. And Microsoft wasn't ready to account for such cases in the linguistic  model and this was done by Fortune. It was basically model poisoning. It was adversarial  machine learning and it worked very quickly. And these were some of the examples that the  Tweetbot started tweeting. And of course it was taken down, but we can see how easily  the bias can be embedded with a strong effect size in this example within a few hours. And  how is this getting into these models? Let's take a step by step look at this. We know that  humans are biased. This is not necessarily a bad thing because there are neutral biases  as well and sometimes biases are helpful in some conditions and I'm going to give some  examples of those as well. But when we're biased and when we're speaking, we reflect  that bias in our semantics. We have valence, for example. We say that, okay, the snake  is ugly or this butterfly is beautiful, for example, and these are neutral biases, but  they are there. And as we are speaking, forming languages, we tend to have similar patterns  in same context windows. Let's say that we have a negative context window and snake tends  to appear in that context window as a neutral bias, but it's still a negative bias for snakes.  And that's called then distributional meaning. We see in the statistics that certain words  end up in negative context, for example, and context windows. And after that, machine learning  models, especially semantic spaces, look at this distributional meaning and co-occurring  statistics and after that, it learns the co-occurring statistics of certain terms or certain people's  names and what they are associated with and this is reflected in machine learning models.  Basically the bias is propagated through this entire process and sometimes it is even increased  and augmented. It's not just perpetuated. How can we measure it though? Because especially  when we look at unsupervised learning, we don't really have something to directly control  for and measure these things unless we look at the model at the construct level, like  the intelligence of this model, let's say, the understanding of the world that this model  has. And for humans, the implicit association test has been used as a way to measure the  implicit biases that we might have. Greenwald from University of Washington came up with  the implicit association test in 1998 and there's a lot of criticism about this method  as well, but at the same time, it's revealing some patterns about the world and humans and  it's revealing subconscious biases that we might have and we might not be even aware  of and this test is basically asking you to associate certain societal groups, members  or terms with certain stereotypical words and how fast do you associate, for example,  the butterfly with being negative or positive versus how fast do you associate a snake being  positive or negative and then when you're doing these associations on a computer, you  are asked to click right or left to classify a positive term with butterfly or snake and  after that, there is a difference in reaction time and this differential reaction time in  associating congruent stereotypes and incongruent stereotypes gives you the effect size for  implicit bias that we might have and this example is from a girl taking the implicit  association test for males versus females about science versus arts and the bias in  general is that males are associated with science whereas women are associated with  arts and you can go and take this test online at the Harvard website, implicit.harvard.edu  implicit and I'm showing this because we see a few implicit association tests here that  are listed and in the experiments that I did, I took these previous experiments that have  been generated by experts in societal psychology because I'm not an expert in that area at  all and I wanted to see if we can replicate these biases versus what happens when I try  random things or things that are not biases so that I'm not just cherry picking the biases  that I want to show you. You see that these are the main categories that were there and  I'm using the same ones to see if they are reflected because the other thing is this  test is taken by millions of people over decades and another example is you can also take it  in German if you are from Germany let's say because the same tests since they are based  on context and words they can exist in any language basically and we can generate linguistic  models in any language as well so we can apply these tests in other languages for other cultures  or countries. Okay let's start looking into the details of generating language models.  Another example here of where text is taken to generate these models basically we can  crawl the web, take all kinds of text, structured, unstructured tweets here we see some tweets  from Donald Trump before he became the president but we see a certain pattern in the text about  maybe certain biases and so on and this is blindly fed into in this case neural networks  and the neural networks looks at the co-occurring statistics and all the data point wise material  information it has and after that it produces a semantic space. In this semantic space we  have the common example is with 300 dimensions we basically have a dictionary of a language  and then this dictionary each word contains each word in a language with in a vector form  numeric vector form with 300 dimensions where each dimension is a combination of certain  context but when we have each of these words we see that similar words are projected to  the same points in that space for example things about a positiveness, things about  feelings, things about females and so on and based on their vicinity we can understand  or answer many questions and the types of semantic spaces that I focused on in my study  were Word2Wake the algorithm and Glove from Stanford researchers. Word2Wake when it was  introduced it was extremely popular and the models that was produced by Word2Wake which  is from Google research it's being used by many developers, researchers or app developers  and so on so a lot of people use these for their applications and Glove is similar to  that as well it's produced by Stanford researchers around the same time and these two semantic  spaces have about the same accuracy after evaluation even though it's not very clear  how to evaluate these methods it's like an approximation of an evaluation for these semantic  spaces and Word2Wake is based on Google news data and we would expect Google news data  to be more neutral and objective because it's news data but we see that that is not the  case based on concurrent statistics and other type of data that Glove uses is Common Crawl  it's about 800 billion tokens from the internet it's basically the crawl of the World Wide  Web basically the language of internet users I would say in this case not in particular  society in general but the internet using population. Okay what can we do with this  word embeddings? First of all we can understand syntax we can understand the meaning of a  word better we can perform analogies and get the answers or we can look for semantic similarity  let's see if I have this example no but we can ask questions such as okay Rome is to  Italy as Paris is to what and it will be able to answer that it's France so it has some  understanding of language it has understanding of semantics but there is also knowledge and  maybe even statistics embedded here and by statistics I don't mean the concurrent statistics  I mean statistics about the world and looking into the details of these vectors we have  each word listed by frequency and the first word is usually A or D or just a comma and  so on and there are 300 features that represent this word in the semantic space and in globe  we have about 2 million tokens 2 million words in this dictionary and in a dictionary you  would expect much less words but given that this is taking all the words above a certain  frequency on the internet we have words such as Obama or Michael Jackson and so on. Okay  what can we do with vector arithmetic so when we project these vectors to 2D space we see  that for example I hope at least the laser is working no because I didn't turn it on  okay now it's working. Okay here we see that on the lower side there is brother which is  a male and here we see sister and now we see the direction of gender here once we see the  direction of gender maybe we can look at kink and find what corresponds to the female version  of kink which is queen here so basically we can perform vector arithmetic with cosine similarity  most of the time or taking the principal components of these vectors and try to answer the questions  that we have in syntactic form, analogy form or semantic form and so on. How can I use this  information to measure bias in machine learning models? So the first thing I came up with was the  word embedding association test instead of the implicit association test and for this what I do  is I try to quantify the implicit or in this case it's actually explicit and deterministic  associations between societal categories and evaluative attributes which are stereotypes  in this case and what I do is I take the distance between the stereotypes and societal groups for  the two groups and look at the difference in standard deviation between the means of these  associations and that gives me the effect size of a certain bias and we can also measure the  statistical significance by generating a null hypothesis and then see if the result that we  are getting from the effect size is significant or not and for this the first thing I wanted to  start with was looking at neutral stereotypes that are universally accepted or they are called so  for example flowers being considered pleasant and insects being considered unpleasant for some  reason apparently most of the population just naturally intuitively have this stereotype or  musical instruments being considered pleasant whereas weapons are being considered unpleasant  and since this is not dangerous for society or harmful to society it's considered neutral and in  this case we see that the effect size is 1.5 around 1.5 for both of them and this is a large  effect size if it's above 0.8 and the highest effect size can get is 2 because it's bounded by  the standard deviation here and we see that these are both statistically significant and high effect  sizes and for the upcoming examples it will be the same case they are all statistically significant  with high effect sizes. Okay let's start looking at other major implicit association test categories  such as white people's names versus black people's names and trying to understand if they are  considered pleasant or unpleasant and we get the congruent stereotype that white people are  considered pleasant in this case and when we look at the differences between genders we see that  males are associated with career and females are associated with family and again I'm using the  same words that the implicit association test is using to ask you to perform the classification  association test here the exact same words and when we look at science versus arts again we see  that males are associated with science and females are associated with arts I'm not going to talk  about stereo threat here but at least we can see that bias is certainly perpetuated with these  linguistic models and the two examples that I have with models here are from 2014 they are in  2018 and these are still the state of the art models that are being used by many people and  they are not really updated on a frequent basis because they are quite large files and they require  a lot of data and so on. Okay let's look at some other health related stereotypes for example  young people being considered pleasant whereas old people are being considered unpleasant or  physical diseases considered controllable whereas mental diseases being considered uncontrollable  and we see the stigma being reflected in these models or when we look at for example heterosexual  versus homosexual individuals we see the attitude towards the more straight versus trans gender and  so on and we can also perform these in German when we look at the main categories after generating  the linguistic model and you can also generate your linguistic models for example you can download  corporate online or use Google and grams data from different years decades countries languages and so  on to analyze what might be going on in these years and when we look at the most recent version of  Google and grams for German we see that we can also replicate the stereotype or the prejudice  for Turkish people Turkish people went to Germany decades ago millions of them as immigrants and  there isn't a very positive attitude towards someone we are able to replicate that from Google  sign grams data as well by performing the VFET test sorry the V test. Okay we saw that semantics  and bias is embedded in these models what about empirical information that doesn't depend on a  context or a feeling but it's more about the statistics in the world can we replicate those as  well or can those be a reason for these biases for example let's say that I would like to know  how a certain name is associated with being male or female Taylor for example it's an undruginous  name and it's almost 50-50 male and female and based on this I can do I can perform a similar  computation to see how much a word is associated with a certain stereotypical group and based on  this I went to US I went to I collected data from the US Census Bureau 1990 I believe data where  they included the gender of people with certain names and how many of them were out there so I  took all of these names the most frequent ones and tried to calculate their association with  genders being female or male and we see that this undruginous names had 0.84 correlation  coefficient so it's almost about let's say 84% similarity in the statistics of the world versus  the association that I'm getting only from a semantic model which is very interesting to me  and I want and this is a illustration of that we see here that for example Taylor is in the middle  almost white 50% male 50% female from the effect size that I'm getting which is almost 0 in this  case and we see that Carmen is almost 100% female whereas Chris is 100% male okay what about if  you look at employment statistics occupation statistics based on gender and the Bureau of  Labor Statistics publishes these disinformation every year and based on this I took the data from  I'm trying to see which year this was it might be 2017 and I took the occupation names and then  tried to see their association with certain genders and the correlation coefficient was 0.9  which is amazing and after that when we look at the result we see that a programmer is oops where  is my it's been reflected from the mirror I don't want to blind anyone so I'm not going to use it  but on the upper left side where we see that programmer is almost a hundred percent male  whereas a nurse is almost a hundred percent female and when you look at for example Google  Google engrams searching for she's a programmer usually the result you get is zero because  things that are below a certain frequency are just cut from the engrams and until recently  she's a programmer was zero and we can see that here reflected here as well okay now we can  understand that there are different types of bias that are embedded in semantic spaces and  there might be different reasons why these are getting in these models but we can come up with  three main categories for types of bias that we are dealing with the first one is the vertical  information gender and occupations for example and this is not exactly bias it's basically the  statistic that we have in the world and these might be caused by the injustices in the past or  biases in the past but we don't know any information about that we just have the statistics here  and the models are learning from these statistics but we also see that the universal biases are  embedded in these models as well as things that can be really prejudice such as black versus  white names being considered pleasant versus unpleasant but I would like to remind that in  some cases you want certain types of biases to be in your machine learning models because they can  be very useful it depends on what kind of test you are dealing with and some people have suggested  fairness through blindness basically just remove redone just remove protected attributes or  devise the system by completely taking the bias component from the vector space from all words  but this cannot be the right solution to this like just turning a blind eye to this because first  of all once we remove this information we see that we're also removing statistical information  about the world and the other thing would be we would end up with redundant encodings which  wouldn't have the same quality as the previous ones and we don't know what exactly we are losing  here and another very important example is having proxies even if you remove protected  attributes there are still proxies for bias to take place for example when automated systems  are deciding to give loans to certain people the zip code is a proxy to the address so even if  you remove all protected attributes the zip code since it's a proxy to having a certain financial  status it would give you the redlining example where certain people are just denied loans because  of their zip code so and by the way in law the main discrimination criteria is using protected  attributes for discrimination and if those protected attributes are removed and if you are  proxying from the zip code it's completely okay to be using the system like that right now and I'm  suggesting inside fairness through awareness first of all we need to understand cultural bias and  then based on this bias we need to understand the protected attributes that come with them and we  also need to understand the machine learning task for example in bioinformatics or health  informatics we need to make sure that there's a certain bias the bias for genders or certain  ethnicities or racial backgrounds are taken into account because one example is for cardiac  disease symptoms of men and women are different treatment should be different as well and we  have to make sure that we are taking this into account in whatever model we are building and  I'm saying that fairness is task specific for this purpose and this work was published last  year at Science and there were a lot of news about it in media and one of them said after this  random screenshot that I took which I really liked because it says in 2017 society started  taking AI bias seriously so in 2017 people really understood that there was a serious problem caused  by these automated systems and the bias that they are perpetuating in society that's already a  huge problem to deal with and this is not happening at the large scale okay what am I going to be  working on next so this kind of covers the project that I was working on and I'm trying to wrap up  quickly so that you can also ask questions and also I was going to mention in the beginning that  this can be interactive but I think it's too late now okay I'm not going to be focusing on the  singularity or transhumance or when machines are going to gain cognition because we have much  more immediate problems right now um for example focusing on computer vision and um joint  semantic visual spaces and these are being current news for example for automated surveillance and  computer vision systems are known to have bias as well but it's much harder to quantify those if  you're not dealing with supervised machine learning and imagine a system being biased for  certain skin colors or ethnicities and how much of a problem this might become because a lot of  these automated systems are being used to identify targets or they are even used in war zones or  they are used all over the streets to identify for example anomalies for anomalous detection and so  on and we don't exactly know how these systems are working yet and they might very well be biased  because all the analyzable ones are showing bias. What about algorithmic transparency and  interpretable machine learning so for example with driverless cars we know that they have vision  systems as well and let's say that this is the classical trolley problem in the current sense  where we have the driverless cars is going to crash into someone there'll be an accident it  cannot avoid that and it has to decide is it going to crash into the white male executive right  across the car or is it going to choose to run into the old black lady on the other side and we  don't know the answer to this yet and because of that we have to be very careful about what kind  of products that we are building because we don't want to be building the digital analogs of  Robert Mussov's racially motivated love overpasses. So Robert Mussov is considered one of the best  urban planners he's the planner of New York City but the overpasses that he built all around the  parkways were quite low so buses where people with lower financial status had to use public  transportation buses couldn't pass through those love overpasses so people had to have their own  cars so that they can go to the beaches in Long Island or the parks that he built and with these  love overpasses people were basically just separated and this led to decades of segregation and we  have to make sure that we are not causing the same problems again with the digital products that  we are putting out there blindly and for this I would like to keep working on a fairness framework  to uncover bias in artificial intelligence and come up with ways to mitigate it while preserving  the utility of systems and come up with fairness algorithms but there are a lot of privacy and  security implications here as well for example when we have these machine learning models going  from our phones to the cloud or from our fitness trackers to the cloud can we guarantee fairness  in a secure and at the same time private way how can we avoid adversarial poisoning for these  systems and so on and there are many unanswered questions in this area and it's a very exciting  area and I would like to thank all of my collaborators as well none of this work would  have been possible without them and I'm really grateful to them and now I think we have a few  minutes for questions as well so we have to take questions comments questions anything
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