{"id":5558,"date":"2021-02-19T15:31:12","date_gmt":"2021-02-19T20:31:12","guid":{"rendered":"https:\/\/afutureworththinkingabout.com\/?p=5558"},"modified":"2024-06-11T16:13:23","modified_gmt":"2024-06-11T20:13:23","slug":"video-and-transcript-why-ai-research-needs-disabled-and-marginalized-perspectives","status":"publish","type":"post","link":"https:\/\/afutureworththinkingabout.com\/?p=5558","title":{"rendered":"Video and Transcript: &#8220;Why AI Research Needs Disabled and Marginalized Perspectives&#8221;"},"content":{"rendered":"<p>Hello Everyone.<\/p>\n<p>Here is my prerecorded talk for the NC State R.L. Rabb Symposium on Embedding AI in Society.<\/p>\n<p style=\"text-align: center;\"><iframe loading=\"lazy\" src=\"https:\/\/drive.google.com\/file\/d\/11Ty877OSGns9h70V9pkdunsEH6qjNHjj\/preview\" width=\"640\" height=\"359\"><span style=\"display: inline-block; width: 0px; overflow: hidden; line-height: 0;\" data-mce-type=\"bookmark\" class=\"mce_SELRES_start\">\ufeff<\/span><\/iframe><\/p>\n<p>There are captions in the video already, but I&#8217;ve also gone ahead and C\/P&#8217;d the SRT text here, as well.<br \/>\n[<strong>2024 Note:<\/strong> Something in GDrive video hosting has broken the captions, but I&#8217;ve contacted them and hopefully they&#8217;ll be fixed soon.]<\/p>\n<p>There were also two things I meant to mention, but failed to in the video:<\/p>\n<p>1) The history of facial recognition and carceral surveillance being used against Black and Brown communities ties into work from Lundy Braun, Melissa N Stein, Seiberth et al., and myself on the medicalization and datafication of Black bodies without their consent, down through history. (Cf. Me, here: <a href=\"https:\/\/www.tandfonline.com\/doi\/full\/10.1080\/23299460.2020.1831365.)\">Fitting the description: historical and sociotechnical elements of facial recognition and anti-black surveillance&#8221;<\/a>.)<\/p>\n<p>2) Not only does GPT-3 fail to write about humanities-oriented topics with respect, it still <a href=\"https:\/\/onezero.medium.com\/for-some-reason-im-covered-in-blood-gpt-3-contains-disturbing-bias-against-muslims-693d275552bf\"><span data-offset-key=\"atto6-1-0\">can&#8217;t write about <b><em>ISLAM AT ALL<\/em><\/b> without writing in connotations of violence and hatred<\/span><\/a>.<\/p>\n<p>Also I somehow forgot to describe the slide with my email address and this website? What the hell Damien.<\/p>\n<p>Anyway.<\/p>\n<p>I&#8217;ve embedded the content of the resource slides in the transcript, but those are by no means all of the <a href=\"https:\/\/drive.google.com\/file\/d\/1-TbSYMiFhJbuswzfWJ3q8ET2H3ohhuIK\/view\">resources<\/a> on this, just the most pertinent.<\/p>\n<p>All of that begins below the cut.<\/p>\n<p><div style=\"width: 471px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/pbs.twimg.com\/media\/EukKCX4WQAI6CWW?format=jpg&amp;name=small\" alt=\" Black man with a mohawk and glasses, wearing a black button up shirt, a red paisley tie, a light grey check suit jacket, and black jeans, stands in front of two tall bookshelves full of books, one thin &amp; red, one of wide untreated pine, and a large monitor with a printer and papers on the stand beneath it.\" width=\"461\" height=\"680\" \/><p class=\"wp-caption-text\">[First conference of the year; figured i might as well get gussied up.]<\/p><\/div><!--more--><\/p>\n<div data-offset-key=\"6vrun-0-0\">\n<p>1<br \/>\n00:00:01,170 &#8211;&gt; 00:00:02,730<br \/>\nHello, my name is Damien Williams.<\/p>\n<p>2<br \/>\n00:00:02,820 &#8211;&gt; 00:00:12,390<br \/>\nI&#8217;m a PhD candidate at Virginia Tech&#8217;s department of Science, Technology, and Society, and my talk today is called &#8220;Why AI Research Needs Disabled and Marginalized Perspectives.&#8221;<\/p>\n<p>3<br \/>\n00:00:15,510 &#8211;&gt; 00:00:22,320<br \/>\nOne of the things that I want to make clear at first is that when I talk about AI today, I&#8217;m talking about things like algorithmic systems, machine learning,<\/p>\n<p>4<br \/>\n00:00:23,100 &#8211;&gt; 00:00:34,080<br \/>\nsystemic institutionalized solutions, support systems, not so much talking about things that we think of as, &#8220;strong AI,&#8221; or &#8220;artificial general intelligence&#8221;\u2014<\/p>\n<p>5<br \/>\n00:00:34,110 &#8211;&gt; 00:00:38,670<br \/>\nwhat I like to think of as &#8220;autonomous generative intelligences.&#8221;<\/p>\n<p>6<br \/>\n00:00:39,740 &#8211;&gt; 00:00:47,240<br \/>\nThat being said, everything that I&#8217;m going to say is exponentially more important in the considerations of strong AI,<\/p>\n<p>7<br \/>\n00:00:47,930 &#8211;&gt; 00:00:53,750<br \/>\neven over and above what its importance is for the considerations within algorithmic systems.<\/p>\n<p>8<br \/>\n00:00:55,850 &#8211;&gt; 00:01:05,630<br \/>\nAll that being said, before we talk about why it is that we need disabled and marginalized perspectives in AI research,<\/p>\n<p>9<br \/>\n00:01:05,660 &#8211;&gt; 00:01:09,890<br \/>\nwe have to talk about what perspectives are currently embedded in AI research.<\/p>\n<p>10<br \/>\n00:01:10,200 &#8211;&gt; 00:01:22,350<br \/>\nAnd when we take a look at the raft of AI research today, we find that there are a whole host of things that get included and assumed to be true.<\/p>\n<p>11<br \/>\n00:01:22,410 &#8211;&gt; 00:01:29,460<br \/>\nAnd those assumptions, those values embodied by those assumptions, get embedded within the research that gets done,<\/p>\n<p>12<br \/>\n00:01:29,670 &#8211;&gt; 00:01:34,050<br \/>\nand within the AI products that get put out into the world and with which we all must live.<\/p>\n<p>13<br \/>\n00:01:35,050 &#8211;&gt; 00:01:42,640<br \/>\nThose perspectives can be capitalist, thinking about profit motive, thinking about the bottom line,<\/p>\n<p>14<br \/>\n00:01:42,640 &#8211;&gt; 00:01:49,960<br \/>\nas in the case of certain AI healthcare systems, or insurance systems, which will put\u2014 in many cases *have* put\u2014<\/p>\n<p>15<br \/>\n00:01:50,230 &#8211;&gt; 00:01:57,880<br \/>\nthe bottom line of the insurance company as more important than the life or health of a patient, because that&#8217;s what has been trained to do;<\/p>\n<p>16<br \/>\n00:01:57,661 &#8211;&gt; 00:02:04,651<br \/>\nit&#8217;s been trained to make sure that the premiums and payouts of the insurance company are as low as possible, regardless of what that takes.<\/p>\n<p>17<br \/>\n00:02:05,440 &#8211;&gt; 00:02:10,660<br \/>\nBut you can also see that in the cases of things like the Temporary Assistance for Needy Families benefits,<\/p>\n<p>18<br \/>\n00:02:10,690 &#8211;&gt; 00:02:16,840<br \/>\nthe algorithms that run those systems, as showcased in the works of Virginia Eubanks, in her Automating Inequality,<\/p>\n<p>19<br \/>\n00:02:17,680 &#8211;&gt; 00:02:29,920<br \/>\nin which cases\u2014 people who are already at the lower socioeconomic status are made more subject to systems that will keep them in poverty,<\/p>\n<p>20<br \/>\n00:02:29,890 &#8211;&gt; 00:02:34,570<br \/>\nrather than being able to be elevated out of poverty because of the kinds of assumptions about their life<\/p>\n<p>21<br \/>\n00:02:34,540 &#8211;&gt; 00:02:41,770<br \/>\nand what kinds of needs they have and the payouts of the systems that they depend on, get embedded in the systems.<\/p>\n<p>22<br \/>\n00:02:43,250 &#8211;&gt; 00:02:46,370<br \/>\nWe find disableist perspectives embedded in these systems.<\/p>\n<p>23<br \/>\n00:02:46,930 &#8211;&gt; 00:02:57,730<br \/>\nSystems about disability payouts, or even systems about machine vision that tries to monitor how people cross the street\u2014<\/p>\n<p>24<br \/>\n00:02:59,250 &#8211;&gt; 00:03:07,980<br \/>\nautomated vehicles that don&#8217;t see people in wheelchairs, or people using crutches, *as* pedestrians, and so doesn&#8217;t categorize those people for safety<\/p>\n<p>25<br \/>\n00:03:07,980 &#8211;&gt; 00:03:12,810<br \/>\nin the same way as it would someone walking on upright on two legs. Right?<\/p>\n<p>26<br \/>\n00:03:13,260 &#8211;&gt; 00:03:21,060<br \/>\nBut then there&#8217;s disability benefit systems which make decisions, determinations about the kind of help and health care that people need to live.<\/p>\n<p>27<br \/>\n00:03:21,570 &#8211;&gt; 00:03:27,300<br \/>\nThese systems are often opaque and they&#8217;re trained on datasets which are, in many cases,<\/p>\n<p>28<br \/>\n00:03:27,900 &#8211;&gt; 00:03:33,090<br \/>\nfilled with assumptions about what the right kind of way to live is about what the right kind of healthcare is.<\/p>\n<p>29<br \/>\n00:03:33,570 &#8211;&gt; 00:03:41,550<br \/>\nAssumptions that, in many cases, hark back to the 1800s; y&#8217;know, assumptions about institutionalization of disabled people.<\/p>\n<p>30<br \/>\n00:03:41,810 &#8211;&gt; 00:03:45,620<br \/>\nThese things persist today and are in many cases blackboxed,<\/p>\n<p>31<br \/>\n00:03:45,860 &#8211;&gt; 00:03:54,200<br \/>\nbecause disabled people have not been consulted in the administration of these things, let alone in their construction.<\/p>\n<p>32<br \/>\n00:03:55,190 &#8211;&gt; 00:04:00,650<br \/>\nYou can see this the work of people like Karen Hao, asking &#8220;Can we ever make an AI that isn&#8217;t ableist?&#8221;<\/p>\n<p>33<br \/>\n00:04:00,630 &#8211;&gt; 00:04:08,220<br \/>\nYou can see this in the work of Alexandra Reeve-Givens with the Future Tense article whose headline you can see here, &#8220;How Algorithmic Bias Hurts People With Disabilities.&#8221;<\/p>\n<p>34<br \/>\n00:04:08,270 &#8211;&gt; 00:04:13,730<br \/>\nAnd in Lydia X. Z. Brown and their work at the Center for Democracy and Technology,<\/p>\n<p>35<br \/>\n00:04:14,180 &#8211;&gt; 00:04:21,050<br \/>\nwhich looks at benefits determinations, algorithmic systems&#8217; benefits determinations for disabled individuals.<\/p>\n<p>36<br \/>\n00:04:21,270 &#8211;&gt; 00:04:26,370<br \/>\nThis report just came out last year, it&#8217;s really quite in depth and fantastic.<\/p>\n<p>37<br \/>\n00:04:28,020 &#8211;&gt; 00:04:34,830<br \/>\nWe find that racist bias, racial perspectives are embedded in AI and algorithmic systems, all the time.<\/p>\n<p>38<br \/>\n00:04:35,110 &#8211;&gt; 00:04:41,320<br \/>\nFacial recognition systems famously don&#8217;t see Black people anywhere near as well as white people.<\/p>\n<p>39<br \/>\n00:04:41,540 &#8211;&gt; 00:04:50,360<br \/>\nPeople with darker skin tones, in many cases, simple facial recognition systems like blink detection systems on Nikon cameras<\/p>\n<p>40<br \/>\n00:04:50,360 &#8211;&gt; 00:04:55,100<br \/>\nwill ask whether people of Asian descent have blinked when they&#8217;re merely smiling.<\/p>\n<p>41<br \/>\n00:04:55,390 &#8211;&gt; 00:05:01,210<br \/>\nThese kinds of things are, you know, old biases that get embedded into new systems<\/p>\n<p>42<br \/>\n00:05:01,210 &#8211;&gt; 00:05:07,990<br \/>\nbecause the new systems are encoded on the old assumptions that animate the technologies on which the new systems are based.<\/p>\n<p>43<br \/>\n00:05:08,500 &#8211;&gt; 00:05:15,370<br \/>\nPhotographic technology was never really designed to see black people very well, and so when digital technology kind of updated it,<\/p>\n<p>44<br \/>\n00:05:15,400 &#8211;&gt; 00:05:19,810<br \/>\nit took the same principles and just mapped them onto a digital space.<\/p>\n<p>45<br \/>\n00:05:21,490 &#8211;&gt; 00:05:30,520<br \/>\nFacial recognition systems that are meant to categorize individuals who are breaking the law are often trained on mugshots;<\/p>\n<p>46<br \/>\n00:05:30,550 &#8211;&gt; 00:05:37,540<br \/>\nmugshot databases are notoriously overpopulated with Black and brown individuals, because black and brown individuals are *assumed* to be criminal.<\/p>\n<p>47<br \/>\n00:05:37,870 &#8211;&gt; 00:05:45,250<br \/>\nAnd so those individuals populate mugshot databases more often, and so those systems have more of those faces in them,<\/p>\n<p>48<br \/>\n00:05:45,450 &#8211;&gt; 00:05:50,550<br \/>\nand so you get cases where like, 28 members of Congress (top right, or sorry, top left picture),<\/p>\n<p>49<br \/>\n00:05:50,760 &#8211;&gt; 00:05:54,090<br \/>\n28 members of Congress were falsely matched to mugshot databases.<\/p>\n<p>50<br \/>\n00:05:54,930 &#8211;&gt; 00:06:01,110<br \/>\nYou can see this in multiple different works from the ACLU, from ProPublica, a number of different places.<\/p>\n<p>51<br \/>\n00:06:01,630 &#8211;&gt; 00:06:10,690<br \/>\nThe GIF on the top right is from the HP face tracking camera scandal from 2009,<\/p>\n<p>52<br \/>\n00:06:11,020 &#8211;&gt; 00:06:15,520<br \/>\nwhen it was shown that HBS face tracking camera did not track the faces of black people.<\/p>\n<p>53<br \/>\n00:06:15,730 &#8211;&gt; 00:06:22,510<br \/>\nIn that GIF, a Black man, a computer store employee, is saying, &#8220;I&#8217;m Black, I think my blackness is interfering with the computer&#8217;s ability to follow me.&#8221;<\/p>\n<p>54<br \/>\n00:06:22,960 &#8211;&gt; 00:06:30,610<br \/>\nAnd at the bottom [of this slide] you have a six, two by three grid, six grid of white women in various clothes, lighter skinned women in various clothes.<\/p>\n<p>55<br \/>\n00:06:30,880 &#8211;&gt; 00:06:34,720<br \/>\nThis is the model of what&#8217;s known as the Shirley Car, and this comes from Kodak, right?<\/p>\n<p>56<br \/>\n00:06:34,750 &#8211;&gt; 00:06:41,260<br \/>\nThis is, the Shirley Card was a white woman named Shirley, and if you could see Shirley&#8217;s face, regardless of what she was wearing,<\/p>\n<p>57<br \/>\n00:06:41,260 &#8211;&gt; 00:06:45,580<br \/>\nor what background she was standing in front of, the image was properly balanced.<\/p>\n<p>58<br \/>\n00:06:46,470 &#8211;&gt; 00:06:55,860<br \/>\nThis is the industry standard on which photography was based and continued to be modeled, even into the development of digital camera technologies.<\/p>\n<p>59<br \/>\n00:06:56,970 &#8211;&gt; 00:07:03,360<br \/>\nThe same goes for carceral surveillance, carceral systems of justice, which use surveillance systems, facial recognition systems,<\/p>\n<p>60<br \/>\n00:07:03,540 &#8211;&gt; 00:07:08,310<br \/>\npredictive policing, which says, &#8220;certain groups of people are more likely to be criminal,<\/p>\n<p>61<br \/>\n00:07:08,580 &#8211;&gt; 00:07:14,400<br \/>\nyou place those cameras there, you do predictive modeling based on your criminal metrics, based on the data that it&#8217;s trained on,&#8221;<\/p>\n<p>62<br \/>\n00:07:14,810 &#8211;&gt; 00:07:21,410<br \/>\nwhen that data is notoriously filled with over populations of Black and brown individuals, minority communities,<\/p>\n<p>63<br \/>\n00:07:21,770 &#8211;&gt; 00:07:29,720<br \/>\nthose systems will be trained to think of those communities *as criminal*, first and foremost.<\/p>\n<p>64<br \/>\n00:07:30,710 &#8211;&gt; 00:07:38,750<br \/>\nIf you then deploy those systems, they will make the same kind of racialized judgments, as previously were made by human beings.<\/p>\n<p>65<br \/>\n00:07:39,320 &#8211;&gt; 00:07:47,060<br \/>\nYou can see this in the work of Clare Garvie and others at Georgetown, their work &#8220;The Perpetual Line-Up,&#8221; from 2016,<\/p>\n<p>66<br \/>\n00:07:46,721 &#8211;&gt; 00:08:13,181<br \/>\nand the deployment of facial recognition and surveillance systems in various communities of color throughout the United States and England.<\/p>\n<p>67<br \/>\n00:07:56,810 &#8211;&gt; 00:08:02,600<br \/>\nInstitutional bias is a perspective that gets encoded not just in the surveillance state,<\/p>\n<p>68<br \/>\n00:08:02,600 &#8211;&gt; 00:08:08,390<br \/>\nbut in the judgments made about people who are then made subject to justice systems in the West,<\/p>\n<p>69<br \/>\n00:08:08,660 &#8211;&gt; 00:08:21,110<br \/>\nwherein algorithmic bail setting and sentencing recommendation systems will make recommendations that say that a Black man with no priors and a lower likelihood of recidivism\u2014<\/p>\n<p>70<br \/>\n00:08:21,650 &#8211;&gt; 00:08:25,730<br \/>\nbased on the system itself&#8217;s own judgments, based on its own estimations\u2014<\/p>\n<p>71<br \/>\n00:08:26,750 &#8211;&gt; 00:08:34,280<br \/>\na Black man with no priors, lower likelihood of recidivism is recommended a lower likelihood of bail,<\/p>\n<p>72<br \/>\n00:08:34,670 &#8211;&gt; 00:08:42,560<br \/>\nand a higher, more harsh sentence than a white man with priors and a higher likelihood of recidivism.<\/p>\n<p>73<br \/>\n00:08:43,890 &#8211;&gt; 00:08:49,440<br \/>\nProPublica&#8217;s investigation of this in 2016 showed how this system was at play in Broward County\u2014<\/p>\n<p>74<br \/>\n00:08:49,440 &#8211;&gt; 00:08:54,240<br \/>\nthis is the Compas bail-setting and sentencing recommendation guidelines\u2014<\/p>\n<p>75<br \/>\n00:08:54,000 &#8211;&gt; 00:09:00,810<br \/>\nand this, again, is based on what it&#8217;s trained on: the behavior of human beings trains these algorithmic AI systems<\/p>\n<p>76<br \/>\n00:09:00,810 &#8211;&gt; 00:09:06,870<br \/>\nand the systems then replicate and iterate on that behavior exacerbating these outcomes.<\/p>\n<p>77<br \/>\n00:09:08,220 &#8211;&gt; 00:09:09,660<br \/>\nThen you have all of the above.<\/p>\n<p>78<br \/>\n00:09:10,260 &#8211;&gt; 00:09:13,770<br \/>\nOn this page, you have a host of different headlines:<\/p>\n<p>79<br \/>\n00:09:14,100 &#8211;&gt; 00:09:18,330<br \/>\n&#8220;It&#8217;s Our Fault That AI Thinks That White Names Are More &#8216;Pleasant&#8217; Than Black Names.&#8221;<\/p>\n<p>80<br \/>\n00:09:18,690 &#8211;&gt; 00:09:22,950<br \/>\nNext headline reads, &#8220;Health Care Algorithm Offered Less Care to Black Patients.&#8221;<\/p>\n<p>81<br \/>\n00:09:23,380 &#8211;&gt; 00:09:30,340<br \/>\nNext one reads, &#8220;AI scraps,&#8221; or, sorry, &#8220;Amazon Scraps Secret AI Recruiting Tool That Showed Bias Against Women.&#8221;<\/p>\n<p>82<br \/>\n00:09:31,270 &#8211;&gt; 00:09:38,890<br \/>\nIn the lower right corner you&#8217;ve got a GIF of a graph, modeling the Word2Vec software<\/p>\n<p>83<br \/>\n00:09:38,870 &#8211;&gt; 00:09:45,770<br \/>\nwhere certain correlations are made along gendered lines between words like &#8220;King&#8221; and &#8220;Man,&#8221; &#8220;Queen&#8221; and &#8220;Woman.&#8221;<\/p>\n<p>84<br \/>\n00:09:46,460 &#8211;&gt; 00:09:54,950<br \/>\nWithin the same study, Caliskan et al., in 2017, you&#8217;ll see that there&#8217;s correlations made between &#8220;CEO&#8221; and &#8220;man,&#8221;<\/p>\n<p>85<br \/>\n00:09:55,160 &#8211;&gt; 00:10:03,380<br \/>\n&#8220;secretary&#8221; and &#8220;woman,&#8221; &#8220;doctor&#8221; and &#8220;man,&#8221; &#8220;nurse&#8221; and &#8220;woman,&#8221; &#8220;President&#8221; and &#8220;man,&#8221; that kind of thing.<\/p>\n<p>86<br \/>\n00:10:03,870 &#8211;&gt; 00:10:14,130<br \/>\nThis gendered bias gets encoded in Word2Vec systems, but it has also persisted in GPT-3 systems in a kind of even more nuanced and systemic way,<\/p>\n<p>87<br \/>\n00:10:14,130 &#8211;&gt; 00:10:21,240<br \/>\nwhere whole hosts of disciplines that GPT-3 gets trained on\u2014 to kind of mimic those writing styles\u2014<\/p>\n<p>88<br \/>\n00:10:22,830 &#8211;&gt; 00:10:29,310<br \/>\nit will cast whole disciplines as meaningless or inadequate or frivolous,<\/p>\n<p>89<br \/>\n00:10:29,820 &#8211;&gt; 00:10:33,900<br \/>\nas it did with philosophy when it was tasked to write a philosophy paper.<\/p>\n<p>90<br \/>\n00:10:35,850 &#8211;&gt; 00:10:44,370<br \/>\nThings on the horizon include the NeuraLink AI system, the Amazon Halo, benefits determination systems are going to increase their proliferation,<\/p>\n<p>91<br \/>\n00:10:45,930 &#8211;&gt; 00:10:54,000<br \/>\nincluding things like COVID determinations, who gets what shots when, who gets what treatments in what scenarios, those kinds of things.<\/p>\n<p>92<br \/>\n00:10:54,870 &#8211;&gt; 00:11:00,480<br \/>\nNeuraLink AI is the brain chip interface from Elon Musk, and Co.<\/p>\n<p>93<br \/>\n00:11:00,720 &#8211;&gt; 00:11:08,340<br \/>\nAmazon Halo is meant to be a kind of full-suite biometric reader, where it tells your heart rate, tells your perspiration,<\/p>\n<p>94<br \/>\n00:11:08,340 &#8211;&gt; 00:11:12,120<br \/>\nit tells your blood-ox. level, tells, y&#8217;know, how much water you need.<\/p>\n<p>95<br \/>\n00:11:12,330 &#8211;&gt; 00:11:19,470<br \/>\nBut it also is meant to do things like tell you what your tone is, in conversations, and whether you might want to modulate your tone.<\/p>\n<p>96<br \/>\n00:11:20,490 &#8211;&gt; 00:11:30,360<br \/>\nNow, one of the things that&#8217;s always been true in the United States is that things like a Black woman&#8217;s tone are often up to scrutiny.<\/p>\n<p>97<br \/>\n00:11:31,230 &#8211;&gt; 00:11:36,750<br \/>\nBlack people are more harshly judged on the whole as to their comportment in social situations,<\/p>\n<p>98<br \/>\n00:11:36,930 &#8211;&gt; 00:11:43,680<br \/>\nand black women&#8217;s tones, in particular, are often policed, for how they interact with each other<\/p>\n<p>99<br \/>\n00:11:43,680 &#8211;&gt; 00:11:49,260<br \/>\nand comport themselves in conversation\u2014 often told that they&#8217;re being overly agitated or angry.<\/p>\n<p>100<br \/>\n00:11:49,480 &#8211;&gt; 00:11:58,930<br \/>\nNow, if the Amazon Halo is trained on general human interactions\u2014 or what it&#8217;s programmers and designers think of as &#8220;General human interaction&#8221;\u2014<\/p>\n<p>101<br \/>\n00:11:59,170 &#8211;&gt; 00:12:07,870<br \/>\nthe hosts of assumptions about what kind of tone is the &#8220;right kind&#8221; of tone to strike in conversation is *inherently* cultural,<\/p>\n<p>102<br \/>\n00:12:08,440 &#8211;&gt; 00:12:15,070<br \/>\nand if the culture of the people who design and program this tool, don&#8217;t take into account the kind of inherent biases that there are<\/p>\n<p>103<br \/>\n00:12:15,070 &#8211;&gt; 00:12:24,700<br \/>\ntowards certain types of comportment, expression, lived experience, and behavior, those things will then replicate in terms of<\/p>\n<p>104<br \/>\n00:12:24,730 &#8211;&gt; 00:12:32,500<br \/>\nthe Amazon Halo suggesting to Black people that, &#8220;hey, maybe you want to calm down,&#8221; when they&#8217;re just having a normal conversation.<\/p>\n<p>105<br \/>\n00:12:33,820 &#8211;&gt; 00:12:48,100<br \/>\nInstantiating the microaggression of the &#8220;angry black man&#8221; or &#8220;angry black woman&#8221; into a systemic, culture-wide device that everyone has monitoring their speech at all times.<\/p>\n<p>106<br \/>\n00:12:51,250 &#8211;&gt; 00:12:52,270<br \/>\nSo what does all this mean?<\/p>\n<p>107<br \/>\n00:12:53,470 &#8211;&gt; 00:13:03,580<br \/>\nThis is a meme that I made. [Laughs] It&#8217;s Zoidberg from the show &#8216;Futurama,&#8217; sitting in a opera house and tuxedo, and he&#8217;s yelling,<\/p>\n<p>108<br \/>\n00:13:03,760 &#8211;&gt; 00:13:08,050<br \/>\n&#8220;Your AI and Algorithmic Facial Recognition Applications Are Bad, and You Should Feel Bad!&#8221;<\/p>\n<p>109<br \/>\n00:13:08,990 &#8211;&gt; 00:13:16,910<br \/>\nAll of these tools, replicate and instantiate the lived experiences and the perspectives and the assumptions the people who program them.<\/p>\n<p>110<br \/>\n00:13:17,330 &#8211;&gt; 00:13:27,830<br \/>\nThey instantiate and iterate upon the assumptions and the values of the people who have commissioned them, who have programmed them, who have trained them,<\/p>\n<p>111<br \/>\n00:13:28,050 &#8211;&gt; 00:13:36,540<br \/>\nand all of the interactions that these systems have when they&#8217;re out in the world form components of the data on which it learns how to be<\/p>\n<p>112<br \/>\n00:13:36,540 &#8211;&gt; 00:13:39,540<br \/>\nand how to do what it is meant to do in the world.<\/p>\n<p>113<br \/>\n00:13:41,070 &#8211;&gt; 00:13:52,470<br \/>\nSo what this means is that what we have to do here is ensure that there is no work done in these realms, without the perspectives of marginalized individuals<\/p>\n<p>114<br \/>\n00:13:52,680 &#8211;&gt; 00:14:02,760<br \/>\nbeing not just tokenisticly &#8220;included,&#8221; not just polled and mined for perspectives or, or opinions about the way that these systems come to be,<\/p>\n<p>115<br \/>\n00:14:03,020 &#8211;&gt; 00:14:13,130<br \/>\nbut actively engaged and put at the forefront of the conversations we have and the development we do around AI and algorithmic systems.<\/p>\n<p>116<br \/>\n00:14:14,720 &#8211;&gt; 00:14:17,990<br \/>\nThis isn&#8217;t the first time we&#8217;ve had these kinds of conversations.<\/p>\n<p>117<br \/>\n00:14:18,020 &#8211;&gt; 00:14:23,510<br \/>\nThese conversations have been at play throughout the history of technology and science.<\/p>\n<p>118<br \/>\n00:14:23,960 &#8211;&gt; 00:14:28,850<br \/>\nAnd we can see it in the lives and the lived experiences and the contributions of many different people.<\/p>\n<p>119<br \/>\n00:14:29,300 &#8211;&gt; 00:14:32,960<br \/>\nThis page is a raft of seven different pictures.<\/p>\n<p>120<br \/>\n00:14:33,380 &#8211;&gt; 00:14:46,010<br \/>\nWe have at the top left an image of Dr. Ruha Benjamin, whose work on algorithmic justice and the nature of carceral surveillance<\/p>\n<p>121<br \/>\n00:14:46,000 &#8211;&gt; 00:14:52,360<br \/>\nand certain types of abolitionist perspectives regarding AI and facial recognition systems<\/p>\n<p>122<br \/>\n00:14:52,450 &#8211;&gt; 00:15:00,070<br \/>\nhas said that, ultimately, certain things maybe just shouldn&#8217;t be developed, because there&#8217;s no just way to develop them in the world.<\/p>\n<p>123<br \/>\n00:15:01,230 &#8211;&gt; 00:15:15,150<br \/>\nThe work that is just kind of fundamental to this idea that some things are just impossible to do in a way that is without real, lasting meaningful harm.<\/p>\n<p>124<br \/>\n00:15:16,470 &#8211;&gt; 00:15:23,190<br \/>\nNext we have Wendy Carlos, the trans woman whose work is at the forefront of all electronic music,<\/p>\n<p>125<br \/>\n00:15:23,370 &#8211;&gt; 00:15:29,850<br \/>\nwho was instrumental in developing the tools to translate music into an electronic format.<\/p>\n<p>126<br \/>\n00:15:31,440 &#8211;&gt; 00:15:37,350<br \/>\nNext, going to the right we have Dr. Ashley Shew, whose work on technology and disability,<\/p>\n<p>127<br \/>\n00:15:37,350 &#8211;&gt; 00:15:41,940<br \/>\non the lives of disabled people and how they interface with their technologies on a day to day basis,<\/p>\n<p>128<br \/>\n00:15:41,940 &#8211;&gt; 00:15:54,420<br \/>\nis doing real kind of long-lasting investigations into what is available to the disabled community versus what the disabled community,<\/p>\n<p>129<br \/>\n00:15:54,420 &#8211;&gt; 00:16:01,650<br \/>\nand members of the disabled community individually, say that they need from technologies that they need to live their lives.<\/p>\n<p>130<br \/>\n00:16:02,830 &#8211;&gt; 00:16:13,480<br \/>\nBelow Dr. Shew&#8217;s image we have the image of Dr. Alondra Nelson, who is now the social science coordinator for the Office of Science Technology Research for the White House, under Joe Biden&#8217;s<br \/>\nadministration.<\/p>\n<p>131<br \/>\n00:16:14,260 &#8211;&gt; 00:16:26,170<br \/>\nHer work is crucial in thinking about the ways that social implications of Science and Technology need to be interrogated and understood,<\/p>\n<p>132<br \/>\n00:16:27,370 &#8211;&gt; 00:16:32,440<br \/>\nthought about at the outset, rather than as an after the fact, kind of post hoc consideration.<\/p>\n<p>133<br \/>\n00:16:33,320 &#8211;&gt; 00:16:43,640<br \/>\nNext, going to the left, we have Dr. Anna Lauren Hoffman. Anna Lauren Hoffman&#8217;s work focuses on the ways that technology and gender collide,<\/p>\n<p>134<br \/>\n00:16:43,630 &#8211;&gt; 00:16:53,350<br \/>\nand specifically, one of the things that Dr. Hoffman is talking about is this notion of &#8220;data violence&#8221; and the ways that perspectives on trans lived experience,<\/p>\n<p>135<br \/>\n00:16:53,000 &#8211;&gt; 00:17:05,330<br \/>\ntransgender individuals&#8217; experience with technology in the world, is kind of predicated upon other people&#8217;s assumptions about what a transgender lived experience ought to be.<\/p>\n<p>136<br \/>\n00:17:05,540 &#8211;&gt; 00:17:12,110<br \/>\nYou see this in everything from just day-to-day life to things like TSA body scanners, and the kinds of assumptions that get made by the<\/p>\n<p>137<br \/>\n00:17:12,270 &#8211;&gt; 00:17:16,680<br \/>\nhuman individuals at work, there, but also the algorithmic systems at work, there.<\/p>\n<p>138<br \/>\n00:17:17,190 &#8211;&gt; 00:17:23,490<br \/>\nAnd then next to Dr. Hoffman, we have Katherine Johnson, whose work on the Apollo Project got human beings to the moon,<\/p>\n<p>139<br \/>\n00:17:23,490 &#8211;&gt; 00:17:29,550<br \/>\nwho was famously almost completely excluded from being able to work in that space because of her race.<\/p>\n<p>140<br \/>\n00:17:29,940 &#8211;&gt; 00:17:33,810<br \/>\nIn the center of all of this we have seven members of the team who are known as the Gallaudet 11.<\/p>\n<p>141<br \/>\n00:17:34,140 &#8211;&gt; 00:17:47,550<br \/>\nThis is a team of Deaf individuals who were brought in by NASA to test the effects of weightlessness and disorientation on individuals who didn&#8217;t have inner ear concerns.<\/p>\n<p>142<br \/>\n00:17:47,000 &#8211;&gt; 00:17:57,560<br \/>\nFor those of you who don&#8217;t know, Gallaudet University is a Deaf university in Washington, DC, and all of the students there are Deaf or hard of hearing.<\/p>\n<p>143<br \/>\n00:17:57,680 &#8211;&gt; 00:18:08,330<br \/>\nSo the Gallaudet 11, were eleven Deaf and Hard of Hearing men, whose experiences with the inner ear were drastically different than individuals who hear &#8220;normally.&#8221;<\/p>\n<p>144<br \/>\n00:18:09,830 &#8211;&gt; 00:18:20,480<br \/>\nAnd as a result, they were, according to NASA, prime subjects, for being able to (sorry about that), being able to you know, test these notions.<\/p>\n<p>145<br \/>\n00:18:21,440 &#8211;&gt; 00:18:30,740<br \/>\nYou know, ask the question about, &#8220;what kind of life in space, in weightlessness\u2014 what kind of disorientation might human beings suffer?&#8221;<\/p>\n<p>146<br \/>\n00:18:32,720 &#8211;&gt; 00:18:34,850<br \/>\nThere has never been a Deaf astronaut.<\/p>\n<p>147<br \/>\n00:18:36,220 &#8211;&gt; 00:18:44,710<br \/>\nDeaf people have been used to train astronauts, data from Deaf individuals has been used to train astronauts, but there has never been a Deaf astronaut.<\/p>\n<p>148<br \/>\n00:18:50,150 &#8211;&gt; 00:18:59,180<br \/>\nAt the very end of all of this, this question of why AI research needs disabled and marginalized perspectives,<\/p>\n<p>149<br \/>\n00:18:59,600 &#8211;&gt; 00:19:09,020<br \/>\ncomes down to this notion of &#8220;whose perspectives, whose lived experiences animate the technology that we make, and to which we are all made subject?&#8221;<\/p>\n<p>150<br \/>\n00:19:09,000 &#8211;&gt; 00:19:20,220<br \/>\nAs AI research increases its reach and its depth and its breadth and its power, we need to be ensuring that the perspectives, the values that get encoded into these systems,<\/p>\n<p>151<br \/>\n00:19:20,400 &#8211;&gt; 00:19:34,410<br \/>\nare values and perspectives that will not just, again, post hoc accommodate or repair or seek to &#8220;include&#8221; in a tokenistic manner, the experiences of marginalized individuals,<\/p>\n<p>152<br \/>\n00:19:34,620 &#8211;&gt; 00:19:43,020<br \/>\nbut take those perspectives into account at the outset. Because those perspectives have something to teach us that is otherwise inaccessible to us.<\/p>\n<p>153<br \/>\n00:19:45,390 &#8211;&gt; 00:19:50,760<br \/>\nWe have to ensure that the perspectives and lived experiences of marginalized people are heeded in this conversation<\/p>\n<p>154<br \/>\n00:19:51,630 &#8211;&gt; 00:19:58,410<br \/>\nabout the design and implementation of algorithmic applications, even and perhaps *especially* when those perspectives make us uncomfortable.<\/p>\n<p>155<br \/>\n00:19:58,990 &#8211;&gt; 00:20:05,710<br \/>\nThe perspectives and lived experiential knowledge of women, disabled people, trans and gender non conforming individuals,<\/p>\n<p>156<br \/>\n00:20:05,920 &#8211;&gt; 00:20:13,450<br \/>\nBlack people, Indigenous people, other marginalized identities are, in large part, informed by being made subject to<\/p>\n<p>157<br \/>\n00:20:13,450 &#8211;&gt; 00:20:17,650<br \/>\nthe worst excesses of technology, up to and including AI.<\/p>\n<p>158<br \/>\n00:20:18,790 &#8211;&gt; 00:20:26,320<br \/>\nPutting them at the forefront of our conversations about AI may require us to radically rethink our founding assumptions about what AI and automation are for.<\/p>\n<p>159<br \/>\n00:20:27,470 &#8211;&gt; 00:20:32,930<br \/>\nBut for millions of people, doing this will very literally mean the difference between life and death.<\/p>\n<p>160<br \/>\n00:20:35,630 &#8211;&gt; 00:20:48,050<br \/>\nI have here a whole host of resources, papers, videos, articles, I highly recommend taking them, spending some time with them,<\/p>\n<p>161<br \/>\n00:20:48,000 &#8211;&gt; 00:20:52,470<br \/>\nand thinking about the ways that we animate our conversations about this.<\/p>\n<\/div>\n<div data-offset-key=\"6vrun-0-0\">\n<ul>\n<li>Ahmed, Sara. <em>The Cultural Politics of Emotion<\/em>. New York: Routledge, 2004.<\/li>\n<li>\u201cAmazon\u2019s Face Recognition Falsely Matched 28 Members of Congress With Mugshots.\u201d Jacob Snow, Technology &amp; Civil Liberties Attorney, ACLU of Northern California. July 26, 2018. <u><a href=\"https:\/\/www.aclu.org\/blog\/privacy-technology\/surveillance-technologies\/amazons-face-recognition-falsely-matched-28\">https:\/\/www.aclu.org\/blog\/privacy-technology\/surveillance-technologies\/amazons-face-recognition-falsely-matched-28<\/a><\/u>.<\/li>\n<li>aoun, sarah; Ahmed, Nasma. \u201cDon\u2019t Include Us, Thank You\u201d (2018) <u><a href=\"https:\/\/livestream.com\/internetsociety\/ttw18\/videos\/174091941\">https:\/\/livestream.com\/internetsociety\/ttw18\/videos\/174091941<\/a><\/u>.<\/li>\n<li>Benjamin, Ruha. 2019. <em>Race after technology: Abolitionist tools for the new Jim code<\/em>. Cambridge: Polity.<\/li>\n<li>Bennett, Cynthia L., Keyes, Os. \u201cWhat is the Point of Fairness? Disability, AI and The Complexity of Justice.\u201d 2019. <u><a href=\"https:\/\/arxiv.org\/abs\/1908.01024?fbclid=IwAR2hqtHAYxC3KabyV_Rgf3mfzRmqh9Y-sG-llvQoeWo_6ZjL6-R3l8s7rZE\">https:\/\/<\/a><a href=\"https:\/\/arxiv.org\/abs\/1908.01024?fbclid=IwAR2hqtHAYxC3KabyV_Rgf3mfzRmqh9Y-sG-llvQoeWo_6ZjL6-R3l8s7rZE\">org\/abs\/1908.01024<\/a><\/u><\/li>\n<li>Braun, Lundy <em>Breathing Race into the Machine: the Surprising Career of the Spirometer from Plantation to Genetics<\/em>. Minneapolis, MN: University of Minnesota Press, 2014. doi:10.5749\/minnesota\/9780816683574.001.0001.<\/li>\n<li>Brown, Lydia X. Z., Michelle Richardson, Ridhi Shetty, Andrew Crawford. &#8220;Report: Challenging the Use of Algorithm-driven Decision-making in Benefits Determinations Affecting People with Disabilities.&#8221; Center For Democracy and Technology. October 2020. <u><a href=\"https:\/\/cdt.org\/insights\/report-challenging-the-use-of-algorithm-driven-decision-making-in-benefits-determinations-affecting-people-with-disabilities\/\">https:\/\/cdt.org\/insights\/report-challenging-the-use-of-algorithm-driven-decision-making-in-benefits-determinations-affecting-people-with-disabilities\/<\/a><\/u>.<\/li>\n<li>Browne, Simone. <em>Dark Matters: On the Surveillance of Blackness<\/em>. (Durham: Duke University Press, 2015)<\/li>\n<li>Buolamwini, Joy, and Timnit Gebru. &#8220;Gender shades: Intersectional accuracy disparities in commercial gender classification.&#8221; In <em>Conference on fairness, accountability and transparency<\/em>, pp. 77-91. 2018.<\/li>\n<li>Caliskan, Aylin; Bryson, Joanna J.; Narayanan, Arvind. \u201cSemantics Derived Automatically From Language Corpora Contain Human-Like Biases.\u201d 14 Apr 2017 : 183-186. <u><a href=\"http:\/\/science.sciencemag.org\/content\/356\/6334\/183.full\">http:\/\/science.sciencemag.org\/content\/356\/6334\/183.full<\/a><\/u>.<\/li>\n<li>Cave, Stephen, and Kanta Dihal. &#8220;The Whiteness of AI.&#8221; <em>Philosophy &amp; Technology<\/em>4 (2020): 685-703. <u><a href=\"https:\/\/doi.org\/10.1007\/s13347-020-00415-6\">https:\/\/doi.org\/10.1007\/s13347-020-00415-6<\/a><\/u>.<\/li>\n<li>del Barco, Mandalit. \u201cHow Kodak\u2019s Shirley Cards Set Photography\u2019s Skin-Tone Standard.\u201d November 13, 2014. NPR <u><a href=\"https:\/\/www.npr.org\/2014\/11\/13\/363517842\/for-decades-kodak-s-shirley-cards-set-photography-s-skin-tone-standard\">https:\/\/www.npr.org\/2014\/11\/13\/363517842\/for-decades-kodak-s-shirley-cards-set-photography-s-skin-tone-standard<\/a><\/u>.<\/li>\n<li>Eubanks, Virginia. <em>Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor<\/em>. New York, NY : St. Martin\u2019s Press, 2018.<\/li>\n<li>Farivar, Cyrus. \u201cCentral Londoners to be subjected to facial recognition test this week.\u201d Ars Technica. December 17, 2018. <u><a href=\"https:\/\/arstechnica.com\/tech-policy\/2018\/12\/londons-police-will-be-testing-facial-recognition-in-public-for-2-days\/\">https:\/\/arstechnica.com\/tech-policy\/2018\/12\/londons-police-will-be-testing-facial-recognition-in-public-for-2-days<\/a><a href=\"https:\/\/arstechnica.com\/tech-policy\/2018\/12\/londons-police-will-be-testing-facial-recognition-in-public-for-2-days\/\">\/<\/a><\/u><\/li>\n<li>Hamraie, Aimi, &amp; Fritsch, Kelly. \u201cCrip technoscience manifesto.\u201d <em>Catalyst: Feminism, Theory, Technoscience<\/em>, <em>5<\/em>(1), 1-34. 2019. http:\/\/www.catalystjournal.org | ISSN: 2380-3312 <u><a href=\"https:\/\/catalystjournal.org\/index.php\/catalyst\/article\/view\/29607\/24771\">https:\/\/catalystjournal.org\/index.php\/catalyst\/article\/view\/29607\/24771<\/a><\/u>.<\/li>\n<li>Hao, Karen. \u201cCan You Make an AI That Isn\u2019t Ableist?\u201d <em>MIT Technology Review<\/em>. November 28, 2018. <u><a href=\"https:\/\/www.technologyreview.com\/s\/612489\/can-you-make-an-ai-that-isnt-ableist\/\">https:\/\/www.technologyreview.com\/s\/612489\/can-you-make-an-ai-that-isnt-ableist<\/a><a href=\"https:\/\/www.technologyreview.com\/s\/612489\/can-you-make-an-ai-that-isnt-ableist\/\">\/<\/a><\/u>.<\/li>\n<li>Hoffman, Kelly M. &amp; Sophie Trawalter, Jordan R. Axt, M. Norman Oliver. &#8220;Racial bias in pain assessment.&#8221; <em>Proceedings of the National Academy of Sciences<\/em>. April 2016, 113 (16) 4296-4301; DOI: 10.1073\/pnas.1516047113.<\/li>\n<li>Joseph, George and Lipp, Kenneth. \u201cIBM Used NYPD Surveillance Footage to Develop Technology That Lets Police Search by Skin Color.\u201d The Intercept. September 6, 2018. \u00a0<u><a href=\"https:\/\/theintercept.com\/2018\/09\/06\/nypd-surveillance-camera-skin-tone-search\/\">https:\/\/theintercept.com\/2018\/09\/06\/nypd-surveillance-camera-skin-tone-search\/<\/a><\/u>.<\/li>\n<li>Keyes, Os. \u201cAutomating autism: Disability, discourse, and Artificial Intelligence.\u201d<em> Journal of Sociotechnical Critique, 1<\/em>(1), 2020, 1-31. <u><a href=\"https:\/\/doi.org\/10.25779\/89bj-j396\">https:\/\/<\/a><a href=\"https:\/\/doi.org\/10.25779\/89bj-j396\">org\/10.25779\/89bj-j396<\/a><\/u><\/li>\n<li>Noble, Safiya U. <em>Algorithms of Oppression: How Search Engines Reinforce Racism<\/em>. New York : New York University Press, 2018.<\/li>\n<li>&#8220;The Overlooked Reality of Police Violence Against Disabled Black Americans,&#8221; The Takeaway. WNYC. June 15, 2020. <a href=\"https:\/\/www.wnycstudios.org\/podcasts\/takeaway\/segments\/police-violence-disabled-black-americans\">https:\/\/www.wnycstudios.org\/podcasts\/takeaway\/segments\/police-violence-disabled-black-americans<\/a>.<\/li>\n<li>\u201cThe Perpetual Line-up: Unregulated Police Face Recognition in America;\u201d Garvie, Clare; Bedoya, Alvaro; Frankle, Jonathan. \u00a0Georgetown Law\u2019s Center for Privacy &amp; Technology. <u><a href=\"https:\/\/www.law.georgetown.edu\/privacy-technology-center\/publications\/the-perpetual-line-up\/\">https:\/\/www.law.georgetown.edu\/privacy-technology-center\/publications\/the-perpetual-line-up\/<\/a><\/u>.<\/li>\n<li>Rose, Adam. \u201cAre Face-Detection Cameras Racist?\u201d <em>Time<\/em>. January 22, 2010. <u><a href=\"http:\/\/content.time.com\/time\/business\/article\/0,8599,1954643,00.html\">http:\/\/content.time.com\/time\/business\/article\/0,8599,1954643,00.html<\/a><\/u>.<\/li>\n<li>Sauder, Kim. \u201cWhen Celebrating Accessible Technology is Just Reinforcing Ableism.\u201d <em>Crippled Scholar<\/em>. July 4, 2015. <u><a href=\"https:\/\/crippledscholar.com\/2015\/07\/04\/when-celebrating-accessible-technology-is-just-reinforcing-ableism\/\">https:\/\/crippledscholar.com\/2015\/07\/04\/when-celebrating-accessible-technology-is-just-reinforcing-ableism\/<\/a><\/u><\/li>\n<li>Seiberth, Sophi; Yoshioka, Jeremy; and Smith, Daniel (2017). \u201cPhysiognomy.\u201d Measuring Prejudice: Race Sciences of the 18th-19th Centuries. <u><a href=\"http:\/\/scalar.usc.edu\/works\/measuring-prejudice\/blank\">http<\/a><a href=\"http:\/\/scalar.usc.edu\/works\/measuring-prejudice\/blank\">:\/\/scalar.usc.edu\/works\/measuring-prejudice\/blank<\/a><\/u>.<\/li>\n<li>Shew, Ashley. (2020.) \u201cAbleism, Technoableism, and Future AI.\u201d IEEE Technology and Society Magazine, Volume\u00a0 39( 1), 40-85. doi: 10.1109\/MTS.2020.2967492.<\/li>\n<li>Spivak, Gayatri Chakravorty. \u201cCan the Subaltern Speak.\u201d 1988<\/li>\n<li>Stein, Melissa N. <em>Measuring Manhood: Race and the Science of Masculinity, 1830\u20131934<\/em>. University of Minnesota Press, 2015. <a href=\"https:\/\/www.jstor.org\/stable\/10.5749\/j.ctt189ttgm\">https:\/\/<\/a><a href=\"https:\/\/www.jstor.org\/stable\/10.5749\/j.ctt189ttgm\">jstor.org\/stable\/10.5749\/j.ctt189ttgm<\/a>.<\/li>\n<li>Washington, Harriet A. <em>Medical Apartheid: The Dark History of Medical Experimentation on Black Americans from Colonial Times to the Present<\/em>. 1st ed. New York: Doubleday, 2006.<\/li>\n<li>Wells-Jensen, Sheri. \u201cThe Case for Disabled Astronauts.\u201d <em>Scientific American: Observations<\/em>. May 30, 2018. <u><a href=\"https:\/\/blogs.scientificamerican.com\/observations\/the-case-for-disabled-astronauts\/\">https:\/\/blogs.scientificamerican.com\/observations\/the-case-for-disabled-astronauts\/<\/a><\/u><\/li>\n<li>Williams, Damien Patrick. \u201cTechnology, Disability, &amp; Human Augmentation.\u201d A Future Worth Thinking About. April 15, 2017. <a href=\"https:\/\/afutureworththinkingabout.com\/?p=5162\">https:\/\/afutureworththinkingabout.com\/?p=5162<\/a>\n<ul>\n<li>\u201cWhat It\u2019s Like To Be a Bot,\u201d Real Life Magazine, May 7, 2018. <a href=\"http:\/\/reallifemag.com\/what-its-like-to-be-a-bot\/\">http:\/\/reallifemag.com\/what-its-like-to-be-a-bot<\/a><a href=\"http:\/\/reallifemag.com\/what-its-like-to-be-a-bot\/\">\/<\/a><\/li>\n<li>\u201cConsciousness and Conscious Machines: What\u2019s At Stake?\u201d appearing in Papers of the 2019 Towards Conscious AI Systems Symposium, co-located with the Association for the Advancement of Artificial Intelligence 2019 Spring Symposium Series (AAAI SSS-19), Stanford, CA, March 25-27, 2019. <a href=\"http:\/\/ceur-ws.org\/Vol-2287\/paper5.pdf\">http<\/a><a href=\"http:\/\/ceur-ws.org\/Vol-2287\/paper5.pdf\">:\/\/<\/a><a href=\"http:\/\/ceur-ws.org\/Vol-2287\/paper5.pdf\">ceur-ws.org\/Vol-2287\/paper5.pdf<\/a><\/li>\n<li>\u201cHeavenly Bodies: Why It Matters That Cyborgs Have Always Been About Disability, Mental Health, and Marginalization.\u201d (June 8, 2019). Available at SSRN: https:\/\/ssrn.com\/abstract=3401342 or <a href=\"http:\/\/dx.doi.org\/10.2139\/ssrn.3401342\">http:\/\/<\/a><a href=\"http:\/\/dx.doi.org\/10.2139\/ssrn.3401342\">doi.org\/10.2139\/ssrn.3401342<\/a><\/li>\n<li>\u201cFitting the Description: Historical and Sociotechnical Elements of Facial Recognition and Anti-Black Surveillance,\u201d appearing in <em><u><a href=\"https:\/\/www.tandfonline.com\/tjri20\">The Journal of Responsible Innovation<\/a><\/u><\/em>, edited by Shannon N. Conley, Erik Fisher, and Emily York; published by Taylor and Francis. <u><a href=\"https:\/\/doi.org\/10.1080\/23299460.2020.1831365\">https:\/\/doi.org\/10.1080\/23299460.2020.1831365<\/a><\/u>.<\/li>\n<\/ul>\n<\/li>\n<li>Williams, Rua Mae. (2019). \u201cMetaeugenics and Metaresistance: from manufacturing the \u2018includable body\u2019 to walking away from the broom closet,\u201d Canadian Journal of Children\u2019s Rights, (in press).\n<ul>\n<li>(2018). \u201cAutonomously Autistic: exposing the locus of autistic pathology,\u201d Canadian Journal of Disability Studies, vol. 7, no. 2, pp. 60\u201382.<\/li>\n<li>With Gilbert, J. E. (2019). \u201c\u2018Nothing About Us Without Us\u2019: Transforming Participatory Research and Ethics in Human Systems Engineering\u201d in Diversity, Inclusion, and Social Justice in Human Systems Engineering. Human Factors and Ergonomics Society. (In press)<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/div>\n<p>162<br \/>\n00:20:52,800 &#8211;&gt; 00:21:05,790<br \/>\nWho is in the room when we make these decisions? Who is driving these questions that we ask? And who is shaping the answers that we give?<\/p>\n<p>163<br \/>\n00:21:06,690 &#8211;&gt; 00:21:11,970<br \/>\nNot just at the end of the day, but at the very beginning of the day.<\/p>\n<p>164<br \/>\n00:21:14,190 &#8211;&gt; 00:21:15,210<br \/>\nThank you very much.<\/p>\n<p>165<br \/>\n00:21:16,080 &#8211;&gt; 00:21:23,550<br \/>\nThis is where you can find me online. This is my email. If you have any questions, I will be happy to answer them at the end.<\/p>\n<div class=\"_2TO-components-SimpleRichTextEditor--paragraphElement\" data-block=\"true\" data-editor=\"30hem\" data-offset-key=\"f2oah-0-0\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\" data-offset-key=\"f2oah-0-0\"><span data-offset-key=\"f2oah-0-0\">&#8212;&#8212;&#8212;&#8212;&#8212;&#8212;&#8212;&#8212;&#8212;&#8212;&#8212;-<\/span><\/div>\n<div data-offset-key=\"f2oah-0-0\">And that&#8217;s all of that. I hope you got something useful and meaningful from it.<\/div>\n<div data-offset-key=\"f2oah-0-0\"><\/div>\n<\/div>\n<div class=\"_2TO-components-SimpleRichTextEditor--paragraphElement\" data-block=\"true\" data-editor=\"30hem\" data-offset-key=\"5s0j7-0-0\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\" data-offset-key=\"5s0j7-0-0\"><span data-offset-key=\"5s0j7-0-0\">Until Next Time.<\/span><\/div>\n<\/div>\n<p><span class=\"sc-1di2uql-1 vYcWR\" data-tag=\"post-title\">\u00a0<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Hello Everyone. Here is my prerecorded talk for the NC State R.L. Rabb Symposium on Embedding AI in Society. \ufeff There are captions in the video already, but I&#8217;ve also gone ahead and C\/P&#8217;d the SRT text here, as well. [2024 Note: Something in GDrive video hosting has broken the captions, but I&#8217;ve contacted them [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2},"jetpack_post_was_ever_published":false},"categories":[1],"tags":[967,1481,1081,1300,1474,1475,73,1120,1115,1021,106,1471,1472,1117,1131,1470,1478,1479,944,560,562,1338,678,1409,960,1480,1477,1473,1476],"class_list":["post-5558","post","type-post","status-publish","format-standard","hentry","category-uncategorized","tag-ai","tag-alexandra-reeve-givens","tag-algorithmic-bias","tag-algorithmic-justice","tag-alondra-nelson","tag-anna-lauren-hoffman","tag-artificial-intelligence","tag-ashley-shew","tag-biomedical-ethics","tag-biotech-ethics","tag-biotechnology","tag-black-lives-matter","tag-disability-rights","tag-disability-studies","tag-gender","tag-justice","tag-katherine-johnson","tag-lydia-x-z-brown","tag-my-voice","tag-my-words","tag-my-writing","tag-nasa","tag-race","tag-ruha-benjamin","tag-surveillance-culture","tag-the-center-for-democracy-and-technology","tag-the-gallaudet-eleven","tag-wendy-carlos","tag-white-house-office-of-science-and-technology-policy"],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/p5WByP-1rE","jetpack_likes_enabled":true,"jetpack-related-posts":[{"id":5316,"url":"https:\/\/afutureworththinkingabout.com\/?p=5316","url_meta":{"origin":5558,"position":0},"title":"My Appearance on The Machine Ethics Podcast&#8217;s A.I. Retreat Episode","author":"Damien P. Williams","date":"October 23, 2018","format":false,"excerpt":"As you already know, we went to the second Juvet A.I. Retreat, back in September. If you want to hear several of us talk about what we got up to at the then you're in luck because here are several conversations conducted by Ben Byford of the Machine Ethics Podcast.\u2026","rel":"","context":"In \"algorithmic bias\"","block_context":{"text":"algorithmic bias","link":"https:\/\/afutureworththinkingabout.com\/?tag=algorithmic-bias"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/img.youtube.com\/vi\/ownE2zxTN2U\/0.jpg?resize=350%2C200","width":350,"height":200},"classes":[]},{"id":5899,"url":"https:\/\/afutureworththinkingabout.com\/?p=5899","url_meta":{"origin":5558,"position":1},"title":"ChatGPT is Actively Marketing to Students During University Finals Season","author":"Damien P. Williams","date":"April 4, 2025","format":false,"excerpt":"It's really disheartening and honestly kind of telling that in spite of everything, ChatGPT is actively marketing itself to students in the run-up to college finals season. We've talked many (many) times before about the kinds of harm that can come from giving over too much epistemic and heuristic authority\u2026","rel":"","context":"In \"A Future Worth Thinking About\"","block_context":{"text":"A Future Worth Thinking About","link":"https:\/\/afutureworththinkingabout.com\/?tag=a-future-worth-thinking-about"},"img":{"alt_text":"Screenshot of ChatpGPT page:ChaptGPT Promo: 2 months free for students ChatGPT Plus is now free for college students through May Offer valid for students in the US and Canada [Buttons reading \"Claim offer\" and \"learn more\" An image of a pencil scrawling a scribbly and looping line] ChatGPT Plus is here to help you through finals","src":"https:\/\/cdn.bsky.app\/img\/feed_fullsize\/plain\/did:plc:ybkylffhwhn2an2ic2lxh76k\/bafkreidh6mhffosfxhbgnxx6aybjycvgj3c2ygzto2xhzvsohdsv3g6evm@jpeg","width":350,"height":200,"srcset":"https:\/\/cdn.bsky.app\/img\/feed_fullsize\/plain\/did:plc:ybkylffhwhn2an2ic2lxh76k\/bafkreidh6mhffosfxhbgnxx6aybjycvgj3c2ygzto2xhzvsohdsv3g6evm@jpeg 1x, https:\/\/cdn.bsky.app\/img\/feed_fullsize\/plain\/did:plc:ybkylffhwhn2an2ic2lxh76k\/bafkreidh6mhffosfxhbgnxx6aybjycvgj3c2ygzto2xhzvsohdsv3g6evm@jpeg 1.5x, https:\/\/cdn.bsky.app\/img\/feed_fullsize\/plain\/did:plc:ybkylffhwhn2an2ic2lxh76k\/bafkreidh6mhffosfxhbgnxx6aybjycvgj3c2ygzto2xhzvsohdsv3g6evm@jpeg 2x"},"classes":[]},{"id":6406,"url":"https:\/\/afutureworththinkingabout.com\/?p=6406","url_meta":{"origin":5558,"position":2},"title":"Reimagining &#8220;AI&#8217;s&#8221; Environmental and Sociotechnical Materialities","author":"Damien P. Williams","date":"May 21, 2025","format":false,"excerpt":"There's a new open-access book of collected essays called Reimagining AI for Environmental Justice and Creativity, and I happen to have an essay in it. The collection is made of contributions from participants in the October 2024 \"Reimagining AI for Environmental Justice and Creativity\" panels and workshops put on by\u2026","rel":"","context":"In \"A Future Worth Thinking About\"","block_context":{"text":"A Future Worth Thinking About","link":"https:\/\/afutureworththinkingabout.com\/?tag=a-future-worth-thinking-about"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/img.youtube.com\/vi\/vgYOWd5o54g\/0.jpg?resize=350%2C200","width":350,"height":200},"classes":[]},{"id":6422,"url":"https:\/\/afutureworththinkingabout.com\/?p=6422","url_meta":{"origin":5558,"position":3},"title":"Failures of &#8220;AI&#8221; Promise: Critical Thinking, Misinformation, Prosociality, &#038; Trust","author":"Damien P. Williams","date":"December 6, 2025","format":false,"excerpt":"So, new research shows that a) LLM-type \"AI\" chatbots are extremely persuasive and able to get voters to shift their positions, and that b) the more effective they are at that, the less they hew to factual reality. Which: Yeah. A bunch of us told you this. Again: the Purpose\u2026","rel":"","context":"In \"A Future Worth Thinking About\"","block_context":{"text":"A Future Worth Thinking About","link":"https:\/\/afutureworththinkingabout.com\/?tag=a-future-worth-thinking-about"},"img":{"alt_text":"Kurt Russell as MacReady from The Thing, a white man with shoulder-length hair and a long scruff beard, wearing grey and olive drab, looking exhausted and sitting next to a bottle of J&B Rare Blend Scotch whisky and a pint glass 1\/3 full of the same, saying into a microphone, \u201cNobody trusts anybody now. And we\u2019re all very tired.\u201d","src":"https:\/\/cdn.bsky.app\/img\/feed_fullsize\/plain\/did:plc:hiz3i44kravwkf6hmzg42okb\/bafkreia5rzux45474gfqegihm6lu23lskhsk77r4aoesjlelvkax4gsbyi@jpeg","width":350,"height":200,"srcset":"https:\/\/cdn.bsky.app\/img\/feed_fullsize\/plain\/did:plc:hiz3i44kravwkf6hmzg42okb\/bafkreia5rzux45474gfqegihm6lu23lskhsk77r4aoesjlelvkax4gsbyi@jpeg 1x, https:\/\/cdn.bsky.app\/img\/feed_fullsize\/plain\/did:plc:hiz3i44kravwkf6hmzg42okb\/bafkreia5rzux45474gfqegihm6lu23lskhsk77r4aoesjlelvkax4gsbyi@jpeg 1.5x, https:\/\/cdn.bsky.app\/img\/feed_fullsize\/plain\/did:plc:hiz3i44kravwkf6hmzg42okb\/bafkreia5rzux45474gfqegihm6lu23lskhsk77r4aoesjlelvkax4gsbyi@jpeg 2x, https:\/\/cdn.bsky.app\/img\/feed_fullsize\/plain\/did:plc:hiz3i44kravwkf6hmzg42okb\/bafkreia5rzux45474gfqegihm6lu23lskhsk77r4aoesjlelvkax4gsbyi@jpeg 3x, https:\/\/cdn.bsky.app\/img\/feed_fullsize\/plain\/did:plc:hiz3i44kravwkf6hmzg42okb\/bafkreia5rzux45474gfqegihm6lu23lskhsk77r4aoesjlelvkax4gsbyi@jpeg 4x"},"classes":[]},{"id":5082,"url":"https:\/\/afutureworththinkingabout.com\/?p=5082","url_meta":{"origin":5558,"position":4},"title":"From WIRED: &#8220;Tech Giants Team Up to Keep AI From Getting Out of Hand&#8221;","author":"Damien P. Williams","date":"September 28, 2016","format":false,"excerpt":"I spoke with Klint Finley over at WIRED about Amazon, Facebook, Google, IBM, and Microsoft's new joint ethics and oversight venture, which they've dubbed the \"Partnership on Artificial Intelligence to Benefit People and Society.\" They held a joint press briefing, today, in which Yann LeCun, Facebook's director of AI, and\u2026","rel":"","context":"In \"A Future Worth Thinking About\"","block_context":{"text":"A Future Worth Thinking About","link":"https:\/\/afutureworththinkingabout.com\/?tag=a-future-worth-thinking-about"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":5281,"url":"https:\/\/afutureworththinkingabout.com\/?p=5281","url_meta":{"origin":5558,"position":5},"title":"The Human Futures and Intelligent Machines Summit at Virginia Tech","author":"Damien P. Williams","date":"June 8, 2018","format":false,"excerpt":"This weekend, Virginia Tech's Center for the Humanities is hosting The Human Futures and Intelligent Machines Summit, and there is a link for the video cast of the events. You'll need to Download and install Zoom, but it should be pretty straightforward, other than that. You'll find the full Schedule,\u2026","rel":"","context":"In \"A Future Worth Thinking About\"","block_context":{"text":"A Future Worth Thinking About","link":"https:\/\/afutureworththinkingabout.com\/?tag=a-future-worth-thinking-about"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]}],"_links":{"self":[{"href":"https:\/\/afutureworththinkingabout.com\/index.php?rest_route=\/wp\/v2\/posts\/5558","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/afutureworththinkingabout.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/afutureworththinkingabout.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/afutureworththinkingabout.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/afutureworththinkingabout.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=5558"}],"version-history":[{"count":5,"href":"https:\/\/afutureworththinkingabout.com\/index.php?rest_route=\/wp\/v2\/posts\/5558\/revisions"}],"predecessor-version":[{"id":5827,"href":"https:\/\/afutureworththinkingabout.com\/index.php?rest_route=\/wp\/v2\/posts\/5558\/revisions\/5827"}],"wp:attachment":[{"href":"https:\/\/afutureworththinkingabout.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=5558"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/afutureworththinkingabout.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=5558"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/afutureworththinkingabout.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=5558"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}