Big data bodies: Machines and algorithms in the world
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Ganesh, Maya Indira
This short talk presents stories of machine learning, automation and algorithms in the social and cultural context of borders, representations of race, queer asylum, and online harassment. In doing so, it emphasises ways of thinking about big data’s operations as social and cultural practices.
 So, we go on. Now with Maya Indira Ganesh from Tactical Tech. If you have been to the Republic, you have seen her a couple of times, I think, or one time at least. And she's now talking about big data bodies, machines and algorithms in the world. A huge applause, please. Thank you for your invitation. And that's the only thing I can say in German. I'm really sorry, but my German is not good enough to give this whole presentation. And so I'm going to have to stick to English. So I'm going to just start with in the next 20 minutes telling you about a single issue publication that I've been working on at Tactical Tech. It's called seen through machines, data discrimination and design. This publication will come out in a few weeks, I hope, and has a number of essays in them. And for the purpose of this talk, I'm going to talk about a couple of these essays, some contributions in the publication. I want to start with something that happened to me recently. I got into a little bit of miscommunication with my landlord. He sent me an email that said this, and it ended up in my spam folder. And as a result, he was a little bit annoyed that I was sending the rent to the wrong account for many months. Now spam filters are the most common and familiar kinds of machine learning that we come across, but they still get it wrong. This email looked a lot like a phishing attempt, so it got sent to spam. So which is great. I mean, it made me feel very secure, but also annoying. It was also annoying because I missed this email. Machine learning at its most base level in a very simplified way is a process of computer software reading a lot of data and identifying patterns and associations in that data, like how different pieces of information in that data set are related to each other. We can move on to something a little bit more complex that relates to the topic of my talk. Now let's see if this works, because I have a link embedded in this. And I hope there's sound. No. I'm just going to stop it for a moment and see if I can. Okay, there's sound in this machine. Nothing is happening. Okay, maybe we won't do the video, which is a shame. But anyway, this is a video by, it's on YouTube, as you can see. So Dr. Mihal Kuzinski is a computational psychologist at Sanford University who develops deep neural networks, a kind of machine learning process that is said to closely mimic how animal brains are thought to work. They are used to identify patterns in faces based on images loaded online. Let's see if we can hear them. Shall I try again? It was just working when we tried before. Aha, okay, of course. Okay. It doesn't matter. It's okay, we'll just go with this. So what Dr. Kuzinski does is that he uses deep neural nets to identify extroverts and introverts. In the clip, he was talking about how you can look at people's faces and identify their gender, how you can look at people's faces and identify their political views. And he was saying that we get outraged when we think that machines can do these things, but if you think about it for a moment, we do these things all the time. We're constantly making these decisions about people based on when we see their faces. So why can't we train machines to do the same thing? So in that clip, he was showing extroverted people and introverted people their faces and asking us in the audience, his audience in that talk, to identify which one was the extrovert and which one was the introvert. People got it right. Machines also started getting it right because they could identify patterns in the way that extroverts and introverts were taking photographs of themselves. These were all selfies. So that's what the talk was about. And how machine learning works is that the software called algorithms are exposed to a training data set. In this case, faces of people and the data can be really anything, but it looks at patterns within those sets. So what Kuzinski was looking at with the extrovert introvert was tilt of the head, separation between the eyes, shape of the nose, breath of the forehead. And then the algorithm was picking up those patterns in the faces and being able to identify them in new faces. So in a new data set that it had never seen. This process of kind of like looking at faces and identifying things about people is nothing new. I mean, we know that this is like over 150 years old in trying to identify criminals, for example. It was a big thing in Europe about 100 years ago. Now what's interesting about Kuzinski is that a few months ago he was in Berlin and he gave a talk where he described this research. He also talked about new research where he was applying facial recognition technology to gay and straight faces. He was working with the data set, he said, of self-identified gay and straight people who had posted their pictures online, as we all do, and using algorithms to read these faces and to be able to identify patterns in them, whatever patterns they saw. And then he said that with 90% accuracy, his algorithms then based on what they had learned about the patterns in those faces, and we're talking about like 20,000, 35,000 images, that the same algorithm could look at a new data set and identify what was a gay face and what was a straight face. And so he talked about this research. It's not published yet. He was just sort of talking about it so I can't actually give you a citation. And then the next slide he showed us was about, was a map of all the places in the world where homosexuality is criminalized and in some places punished by death. And he said, wouldn't it be really unfortunate if this kind of technology was available to kind of governments in these places? And I think he was concerned, but that did not make him sort of think about the technology he was developing anyway. It's also useful to note that Dr. Kaczynski's research on psychographics has been in the news for the last few months because it's allegedly Cambridge Analytica used his research to do micro-targeting for election campaigns. So there's kind of interesting connections there. So let's move on to another example. There's an anecdote about border crossings between Canada and the US. This is from some time ago. The porosity of the border has made it possible for citizens of one country to travel, live in and work in the other. So it was common for border control police to randomly ask people crossing to say the last four letters of the English language alphabet because it isn't always easy in that part of the world to identify people based on just how they look or how they speak. Canadians following the British English pronunciation would say WXYZ, whereas Americans would say WXYZ. This quaint technique was of course very quickly learned and hacked. And of course after 9-11 passport checks became mandatory. A version of this continues over in Europe. In a recent news story, Deutsche Welle reported that the German government was considering bringing in voice recognition technology to distinguish Syrian Arabic speakers from Arabic speakers from other countries to ensure that only citizens fleeing Syria were receiving asylum in Germany. Deutsche Welle reports that such software is based on voice authentication technology used by banks and insurance companies. However, the news report says that a court's linguistics experts who say that such analyses are fraught. Identifying the region of origin for anyone based on their speech is an extremely complex task, one that requires a linguist rather than automated software. In 2012, the artist Lawrence Abu Hamdan held a meeting in Utrecht in the Netherlands to talk about the use of speech recognition technologies in asylum cases of Somali refugees. Having ascertained that they were coming from relatively safe pockets of Somalia, the Dutch authorities wanted to deny them asylum. Even with a group of cultural practitioners and artists and activists and Somali asylum seekers, Abu Hamdan was able to show that accent is not a passport and was in fact a non-geographic map. Abu Hamdan writes that the maps explore the hybrid nature of accent, complicating its relation to one's place of birth by also considering the social conditions and cultural exchange of those living such itinerant lives. It reads the way people speak about the volatile history and geography of Somalia over the last 40 years as a product of continual migration and crisis. Voice eventually is an inappropriate way to fix people in space. So as Nicole Shepard writes, we must look at the practices of quantification and what they mean but as a sort of continuation of history. Big data are the latest trend in a long tradition of quantification with roots in modernity's fetishization of taxonomy in the service of institutional order. And what's interesting about sort of looking, or at least interesting for me in looking at big data technologies is in a way that sort of challenges the assumption of that all of these algorithms and machine learning and big data just kind of plug very seamlessly into human systems and we can speed up and automate things. And as Shepard says, we need to look at the sort of historical continuities especially where gender, race and sexuality are concerned. So I'm interested in data and discrimination. In the things that have come to make us uniquely who we are, how we look, where we are from, our personal and demographic identities, what languages we speak, these things are effectively incomprehensible to machines. What is generally celebrated as human diversity and experienced is transformed by machine reading into something observed, something that marks us as different. Big data technologies are not only being used to classify but also misclassify us by what we say and do online. There's a very interesting recent Pulitzer Prize winning investigation by ProPublica, maybe many of you have seen this, that showed that racial bias was being perpetuated by the use of algorithms that predicted recidivism rates or the likelihood of offending again, in that it was predicting that people of color were more likely to commit crimes in the future than white people. What I found interesting about this story was that it exposed the ways in which state and private institutions, social prejudices and multiple databases collude in the creation of algorithmic mechanisms with bias. Such networked effects have become the stuff of everyday news now, each story presenting a fresh outrage to establish notions of human rights and dignity. For example, image recognition software that runs on machine learning identifies black people as gorillas or Asian people as blinking. Latanya Sweeney finds that people with black sounding names were more likely to be served ads for services related to arrest and law enforcement. Interestingly, I was in Canada recently just at the beginning of August for a few weeks for a summer school and I found that as soon as I went to Canada, I was getting these ads for arrest and take care of your criminal record and bail and it was kind of shocking to me and I thought that maybe in Canada, people with Indian sounding names have seemed criminal. The line of questioning that interests me in looking at data and discrimination is a continuation of tactical text work from an exhibition we did last year called Nervous Systems, Quantified Life and the Social Question which we did at the House Diculture and the Velt in between March and May last year. What Nervous Systems did was to try and tell a more layered and nuanced story beyond just how algorithms work but to look at the complex infrastructures of quantification and software but also culture, symbols, values and practices. One of the things that Nervous Systems did was to sort of unpack the historical precedent of big data lest we think that these are things that are new. I like this quote by Shannon Matta and that this quantifying spirit is something that's kind of old in Europe but also in many other parts of the world. Explorers were returning from distant lands with new bites of information, logs, maps, specimens, while back home Europeans turned natural history into a leisure pursuit. Hobbyists combed the fields for flowers to press and butterflies to pin. Scientists and philosophers saw rational modes of description, classification analysis, in other words, systematicity. So I want to sort of go from this ordering and systematicity to the idea of discrimination itself. So I start by sort of unpacking what does the word discrimination mean. And actually you can move away from discrimination in a legal sense to sort of think that what the examples I've been talking about show that discrimination is about being very clearly identified, being very clearly distinguished and seen. It's not necessarily just about disadvantage per se but about visibility and what is visibility through a machine mean for different kinds of people. In work that I did last year at Tactical Tech with Jeff Deutch and Jennifer Schulte, we wrote about what we call the tension between anonymity and visibility. We examined the technology practices and environments of LGBT activists in Kenya and of housing and land rights activists in South Africa. We found that they wanted the kinds of visibility that technology brought but in order to do their activism and make their claims, that visibility brought risks because of their marginal position in society, being gay and from a working class background or a very Christian environment or from a small town or some combination of these was much more risky perhaps for someone who was maybe English speaking, upper class, urban, both ran the risk of exposure through technology and had to maintain their presence online very carefully but the latter's visibility was less of a liability. So for the past four months, I've been working on the Zine or magazine about data and discrimination which I call seen through machines to sort of examine what it means to be seen through machines but also what it can we look through the machine in the process of doing so. These are some of the contributions that are there and I'm going to take you quickly through them. The first one is actually really interesting because Louisa Prado is a designer who's looking at this artifact called the Humboldt Cup. It's actually on display at the Mie Kollektors room in Augustraza in Mitta. The Humboldt Cup is a 17th century Dutch artifact and what she does is she traces this cup which has engravings of Brazilian natives and through that explores how racial classification and the census in Brazil developed and since I'm running out of time, I'm going to leave that and talk about the last D actually which is design. So there's data discrimination in design and I've been really interested in sort of struggling with the idea of design and what we expect of design in this context. If we design something better, we think maybe the problem will go away or that if we have more visibility and transparency in the design process, it will be improved, systems will be improved and held to account. One contribution in the scene that talks directly to this is Kate Sims' contribution on anti-rape technologies. She says that anti-rape technologies are about good user experience, UX, as well as about gathering more data about the context in which rapes happen in order to design more better systems. She finds that the design process is dislocated and anti-rape technology is actually developed in many different cities and because this process is dislocated, it makes accountability difficult. Amy Elliott and Kate Deem directly take on UX design and the weaponization of design and malware saying that just making something easier to use even if it's malware isn't necessarily a good thing. There's a lot of emphasis on marketing good design to make systems more usable. Caroline Cinder's writes to designers and technical experts to consider that perhaps machines and machine learning are as messy as the people who make them in showing how complex it is for machines to learn how language works, she discusses developments and automating online harassment and abuse identification in Wikipedia. At some level, talking about machines and bodies and data and discrimination is really also a study in failure and error in machine systems but also in our own human imagination and empathy for and about each other. So I will end on that note and ask that you look out for our new publication. It should be out by the end of September. Stay in touch and I'm happy to take more questions as we go forward. Thanks.
 So, we go on.  Now with Maya Indira Ganesh from Tactical Tech.  If you have been to the Republic, you have seen her a couple of times, I think, or one  time at least.  And she's now talking about big data bodies, machines and algorithms in the world.  A huge applause, please.  Thank you for your invitation.  And that's the only thing I can say in German.  I'm really sorry, but my German is not good enough to give this whole presentation.  And so I'm going to have to stick to English.  So I'm going to just start with in the next 20 minutes telling you about a single issue  publication that I've been working on at Tactical Tech.  It's called seen through machines, data discrimination and design.  This publication will come out in a few weeks, I hope, and has a number of essays in them.  And for the purpose of this talk, I'm going to talk about a couple of these essays, some  contributions in the publication.  I want to start with something that happened to me recently.  I got into a little bit of miscommunication with my landlord.  He sent me an email that said this, and it ended up in my spam folder.  And as a result, he was a little bit annoyed that I was sending the rent to the wrong account  for many months.  Now spam filters are the most common and familiar kinds of machine learning that we come across,  but they still get it wrong.  This email looked a lot like a phishing attempt, so it got sent to spam.  So which is great.  I mean, it made me feel very secure, but also annoying.  It was also annoying because I missed this email.  Machine learning at its most base level in a very simplified way is a process of computer  software reading a lot of data and identifying patterns and associations in that data, like  how different pieces of information in that data set are related to each other.  We can move on to something a little bit more complex that relates to the topic of my talk.  Now let's see if this works, because I have a link embedded in this.  And I hope there's sound.  No.  I'm just going to stop it for a moment and see if I can.  Okay, there's sound in this machine.  Nothing is happening.  Okay, maybe we won't do the video, which is a shame.  But anyway, this is a video by, it's on YouTube, as you can see.  So Dr. Mihal Kuzinski is a computational psychologist at Sanford University who develops deep neural  networks, a kind of machine learning process that is said to closely mimic how animal brains  are thought to work.  They are used to identify patterns in faces based on images loaded online.  Let's see if we can hear them.  Shall I try again?  It was just working when we tried before.  Aha, okay, of course.  Okay.  It doesn't matter.  It's okay, we'll just go with this.  So what Dr. Kuzinski does is that he uses deep neural nets to identify extroverts and  introverts.  In the clip, he was talking about how you can look at people's faces and identify their  gender, how you can look at people's faces and identify their political views.  And he was saying that we get outraged when we think that machines can do these things,  but if you think about it for a moment, we do these things all the time.  We're constantly making these decisions about people based on when we see their faces.  So why can't we train machines to do the same thing?  So in that clip, he was showing extroverted people and introverted people their faces  and asking us in the audience, his audience in that talk, to identify which one was the  extrovert and which one was the introvert.  People got it right.  Machines also started getting it right because they could identify patterns in the way that  extroverts and introverts were taking photographs of themselves.  These were all selfies.  So that's what the talk was about.  And how machine learning works is that the software called algorithms are exposed to  a training data set.  In this case, faces of people and the data can be really anything, but it looks at patterns  within those sets.  So what Kuzinski was looking at with the extrovert introvert was tilt of the head, separation  between the eyes, shape of the nose, breath of the forehead.  And then the algorithm was picking up those patterns in the faces and being able to identify  them in new faces.  So in a new data set that it had never seen.  This process of kind of like looking at faces and identifying things about people is nothing  new.  I mean, we know that this is like over 150 years old in trying to identify criminals,  for example.  It was a big thing in Europe about 100 years ago.  Now what's interesting about Kuzinski is that a few months ago he was in Berlin and  he gave a talk where he described this research.  He also talked about new research where he was applying facial recognition technology  to gay and straight faces.  He was working with the data set, he said, of self-identified gay and straight people  who had posted their pictures online, as we all do, and using algorithms to read these  faces and to be able to identify patterns in them, whatever patterns they saw.  And then he said that with 90% accuracy, his algorithms then based on what they had learned  about the patterns in those faces, and we're talking about like 20,000, 35,000 images,  that the same algorithm could look at a new data set and identify what was a gay face  and what was a straight face.  And so he talked about this research.  It's not published yet.  He was just sort of talking about it so I can't actually give you a citation.  And then the next slide he showed us was about, was a map of all the places in the world where  homosexuality is criminalized and in some places punished by death.  And he said, wouldn't it be really unfortunate if this kind of technology was available to  kind of governments in these places?  And I think he was concerned, but that did not make him sort of think about the technology  he was developing anyway.  It's also useful to note that Dr. Kaczynski's research on psychographics has been in the  news for the last few months because it's allegedly Cambridge Analytica used his research  to do micro-targeting for election campaigns.  So there's kind of interesting connections there.  So let's move on to another example.  There's an anecdote about border crossings between Canada and the US.  This is from some time ago.  The porosity of the border has made it possible for citizens of one country to travel, live  in and work in the other.  So it was common for border control police to randomly ask people crossing to say the  last four letters of the English language alphabet because it isn't always easy in that  part of the world to identify people based on just how they look or how they speak.  Canadians following the British English pronunciation would say WXYZ, whereas Americans would say  WXYZ.  This quaint technique was of course very quickly learned and hacked.  And of course after 9-11 passport checks became mandatory.  A version of this continues over in Europe.  In a recent news story, Deutsche Welle reported that the German government was considering  bringing in voice recognition technology to distinguish Syrian Arabic speakers from Arabic  speakers from other countries to ensure that only citizens fleeing Syria were receiving  asylum in Germany.  Deutsche Welle reports that such software is based on voice authentication technology  used by banks and insurance companies.  However, the news report says that a court's linguistics experts who say that such analyses  are fraught.  Identifying the region of origin for anyone based on their speech is an extremely complex  task, one that requires a linguist rather than automated software.  In 2012, the artist Lawrence Abu Hamdan held a meeting in Utrecht in the Netherlands to  talk about the use of speech recognition technologies in asylum cases of Somali refugees.  Having ascertained that they were coming from relatively safe pockets of Somalia, the Dutch  authorities wanted to deny them asylum.  Even with a group of cultural practitioners and artists and activists and Somali asylum  seekers, Abu Hamdan was able to show that accent is not a passport and was in fact a  non-geographic map.  Abu Hamdan writes that the maps explore the hybrid nature of accent, complicating its  relation to one's place of birth by also considering the social conditions and cultural  exchange of those living such itinerant lives.  It reads the way people speak about the volatile history and geography of Somalia over the  last 40 years as a product of continual migration and crisis.  Voice eventually is an inappropriate way to fix people in space.  So as Nicole Shepard writes, we must look at the practices of quantification and what  they mean but as a sort of continuation of history.  Big data are the latest trend in a long tradition of quantification with roots in modernity's  fetishization of taxonomy in the service of institutional order.  And what's interesting about sort of looking, or at least interesting for me in looking  at big data technologies is in a way that sort of challenges the assumption of that  all of these algorithms and machine learning and big data just kind of plug very seamlessly  into human systems and we can speed up and automate things.  And as Shepard says, we need to look at the sort of historical continuities especially  where gender, race and sexuality are concerned.  So I'm interested in data and discrimination.  In the things that have come to make us uniquely who we are, how we look, where we are from,  our personal and demographic identities, what languages we speak, these things are effectively  incomprehensible to machines.  What is generally celebrated as human diversity and experienced is transformed by machine reading  into something observed, something that marks us as different.  Big data technologies are not only being used to classify but also misclassify us by what  we say and do online.  There's a very interesting recent Pulitzer Prize winning investigation by ProPublica,  maybe many of you have seen this, that showed that racial bias was being perpetuated by  the use of algorithms that predicted recidivism rates or the likelihood of offending again,  in that it was predicting that people of color were more likely to commit crimes in the future  than white people.  What I found interesting about this story was that it exposed the ways in which state  and private institutions, social prejudices and multiple databases collude in the creation  of algorithmic mechanisms with bias.  Such networked effects have become the stuff of everyday news now, each story presenting  a fresh outrage to establish notions of human rights and dignity.  For example, image recognition software that runs on machine learning identifies black  people as gorillas or Asian people as blinking.  Latanya Sweeney finds that people with black sounding names were more likely to be served  ads for services related to arrest and law enforcement.  Interestingly, I was in Canada recently just at the beginning of August for a few weeks  for a summer school and I found that as soon as I went to Canada, I was getting these ads  for arrest and take care of your criminal record and bail and it was kind of shocking  to me and I thought that maybe in Canada, people with Indian sounding names have seemed  criminal.  The line of questioning that interests me in looking at data and discrimination is a continuation  of tactical text work from an exhibition we did last year called Nervous Systems, Quantified  Life and the Social Question which we did at the House Diculture and the Velt in between  March and May last year.  What Nervous Systems did was to try and tell a more layered and nuanced story beyond just  how algorithms work but to look at the complex infrastructures of quantification and software  but also culture, symbols, values and practices.  One of the things that Nervous Systems did was to sort of unpack the historical precedent  of big data lest we think that these are things that are new.  I like this quote by Shannon Matta and that this quantifying spirit is something that's  kind of old in Europe but also in many other parts of the world.  Explorers were returning from distant lands with new bites of information, logs, maps,  specimens, while back home Europeans turned natural history into a leisure pursuit.  Hobbyists combed the fields for flowers to press and butterflies to pin.  Scientists and philosophers saw rational modes of description, classification analysis,  in other words, systematicity.  So I want to sort of go from this ordering and systematicity to the idea of discrimination  itself.  So I start by sort of unpacking what does the word discrimination mean.  And actually you can move away from discrimination in a legal sense to sort of think that what  the examples I've been talking about show that discrimination is about being very clearly  identified, being very clearly distinguished and seen.  It's not necessarily just about disadvantage per se but about visibility and what is visibility  through a machine mean for different kinds of people.  In work that I did last year at Tactical Tech with Jeff Deutch and Jennifer Schulte, we  wrote about what we call the tension between anonymity and visibility.  We examined the technology practices and environments of LGBT activists in Kenya and  of housing and land rights activists in South Africa.  We found that they wanted the kinds of visibility that technology brought but in order to do  their activism and make their claims, that visibility brought risks because of their  marginal position in society, being gay and from a working class background or a very  Christian environment or from a small town or some combination of these was much more  risky perhaps for someone who was maybe English speaking, upper class, urban, both ran the  risk of exposure through technology and had to maintain their presence online very carefully  but the latter's visibility was less of a liability.  So for the past four months, I've been working on the Zine or magazine about data and discrimination  which I call seen through machines to sort of examine what it means to be seen through  machines but also what it can we look through the machine in the process of doing so.  These are some of the contributions that are there and I'm going to take you quickly through  them.  The first one is actually really interesting because Louisa Prado is a designer who's looking  at this artifact called the Humboldt Cup.  It's actually on display at the Mie Kollektors room in Augustraza in Mitta.  The Humboldt Cup is a 17th century Dutch artifact and what she does is she traces this cup which  has engravings of Brazilian natives and through that explores how racial classification and  the census in Brazil developed and since I'm running out of time, I'm going to leave that  and talk about the last D actually which is design.  So there's data discrimination in design and I've been really interested in sort of struggling  with the idea of design and what we expect of design in this context.  If we design something better, we think maybe the problem will go away or that if we have  more visibility and transparency in the design process, it will be improved, systems will  be improved and held to account.  One contribution in the scene that talks directly to this is Kate Sims' contribution  on anti-rape technologies.  She says that anti-rape technologies are about good user experience, UX, as well as about  gathering more data about the context in which rapes happen in order to design more better  systems.  She finds that the design process is dislocated and anti-rape technology is actually developed  in many different cities and because this process is dislocated, it makes accountability  difficult.  Amy Elliott and Kate Deem directly take on UX design and the weaponization of design  and malware saying that just making something easier to use even if it's malware isn't  necessarily a good thing.  There's a lot of emphasis on marketing good design to make systems more usable.  Caroline Cinder's writes to designers and technical experts to consider that perhaps  machines and machine learning are as messy as the people who make them in showing how  complex it is for machines to learn how language works, she discusses developments and automating  online harassment and abuse identification in Wikipedia.  At some level, talking about machines and bodies and data and discrimination is really  also a study in failure and error in machine systems but also in our own human imagination  and empathy for and about each other.  So I will end on that note and ask that you look out for our new publication.  It should be out by the end of September.  Stay in touch and I'm happy to take more questions as we go forward.  Thanks.
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