algorithmic justice

All posts tagged algorithmic justice

Further Thoughts on the “Blueprint for an AI Bill of Rights”

Posted by Damien P. Williams on December 3, 2022

Posted in: Uncategorized. Tagged: A Future Worth Thinking About, ai, algorithmic bias, algorithmic intelligence, algorithmic justice, algorithmic systems, algorithms, alondra nelson, Arati Prabhakar, artificial intelligence, autonomous systems, autonomous weapons systems, bias, blueprint for an ai bill of rights, brain-computer interface, department of defense, disability, disability studies, drones, epistemology, ethics washing, ethnicity, facebook, facial recognition, google, humanities, intersectionality, Invisible Architecture of Bias, Invisible Architectures of Bias, justice, machine learning, my work, my writing, neuralink, oversight board, philosophy, philosophy of technology, police militarization, police overreach, politics, public policy, race, racism, science and technology studies, science technology and society, social construction of technology, social dynamics, social sciences, sociology, sts, surveillance culture, technological ethics, the overton window, timnit gebru, twitter, u.s. politics, values, white house office of science and technology policy, youtube. Leave a Comment

So with the job of White House Office of Science and Technology Policy director having gone to Dr. Arati Prabhakar back in October, rather than Dr. Alondra Nelson, and the release of the “Blueprint for an AI Bill of Rights” (henceforth “BfaAIBoR” or “blueprint”) a few weeks after that, I am both very interested also pretty worried to see what direction research into “artificial intelligence” is actually going to take from here.

To be clear, my fundamental problem with the “Blueprint for an AI bill of rights” is that while it pays pretty fine lip-service to the ideas of community-led oversight, transparency, and abolition of and abstaining from developing certain tools, it begins with, and repeats throughout, the idea that sometimes law enforcement, the military, and the intelligence community might need to just… ignore these principles. Additionally, Dr. Prabhakar was director of DARPA for roughly five years, between 2012 and 2015, and considering what I know for a fact got funded within that window? Yeah.

To put a finer point on it, 14 out of 16 uses of the phrase “law enforcement” and 10 out of 11 uses of “national security” in this blueprint are in direct reference to why those entities’ or concept structures’ needs might have to supersede the recommendations of the BfaAIBoR itself. The blueprint also doesn’t mention the depredations of extant military “AI” at all. Instead, it points to the idea that the Department Of Defense (DoD) “has adopted [AI] Ethical Principles, and tenets for Responsible Artificial Intelligence specifically tailored to its [national security and defense] activities.” And so with all of that being the case, there are several current “AI” projects in the pipe which a blueprint like this wouldn’t cover, even if it ever became policy, and frankly that just fundamentally undercuts Much of the real good a project like this could do.

For instance, at present, the DoD’s ethical frames are entirely about transparency, explainability, and some lipservice around equitability and “deliberate steps to minimize unintended bias in Al …” To understand a bit more of what I mean by this, here’s the DoD’s “Responsible Artificial Intelligence Strategy…” pdf (which is not natively searchable and I had to OCR myself, so heads-up); and here’s the Office of National Intelligence’s “ethical principles” for building AI. Note that not once do they consider the moral status of the biases and values they have intentionally baked into their systems.

An “Explainable AI” diagram from DARPA

I’m Not Afraid of AI Overlords— I’m Afraid of Whoever’s Training Them To Think That Way

Posted by Damien P. Williams on October 8, 2021

Posted in: Uncategorized. Tagged: ai, algorithmic bias, algorithmic intelligence, algorithmic justice, algorithmic systems, algorithms, alondra nelson, alvaro bedoya, anna lauren hoffman, artificial intelligence, ashley shew, bias, cambridge analytica, clare garvie, disability, disability studies, epistemology, ethics washing, facebook, facial recognition, frances haugen, google, humanities, Invisible Architecture of Bias, Invisible Architectures of Bias, joy buolamwini, justice, kim crayton, machine learning, my words, my work, my writing, oversight board, philosophy, philosophy of technology, politics, public policy, ruha benjamin, safiya noble, science and technology studies, science technology and society, social construction of technology, social dynamics, social sciences, sociology, sts, technological ethics, timnit gebru, twitter, u.s. politics, values, virginia eubanks, white house office of science and technology policy, youtube. 3 comments

I’m Not Afraid of AI Overlords— I’m Afraid of Whoever’s Training Them To Think That Way

by Damien P. Williams

I want to let you in on a secret: According to Silicon Valley’s AI’s, I’m not human.

Well, maybe they think I’m human, but they don’t think I’m me. Or, if they think I’m me and that I’m human, they think I don’t deserve expensive medical care. Or that I pose a higher risk of criminal recidivism. Or that my fidgeting behaviours or culturally-perpetuated shame about my living situation or my race mean I’m more likely to be cheating on a test. Or that I want to see morally repugnant posts that my friends have commented on to call morally repugnant. Or that I shouldn’t be given a home loan or a job interview or the benefits I need to stay alive.

Now, to be clear, “AI” is a misnomer, for several reasons, but we don’t have time, here, to really dig into all the thorny discussion of values and beliefs about what it means to think, or to be a pow3rmind— especially because we need to take our time talking about why values and beliefs matter to conversations about “AI,” at all. So instead of “AI,” let’s talk specifically about algorithms, and machine learning.

Machine Learning (ML) is the name for a set of techniques for systematically reinforcing patterns, expectations, and desired outcomes in various computer systems. These techniques allow those systems to make sought after predictions based on the datasets they’re trained on. ML systems learn the patterns in these datasets and then extrapolate them to model a range of statistical likelihoods of future outcomes.

Algorithms are sets of instructions which, when run, perform functions such as searching, matching, sorting, and feeding the outputs of any of those processes back in on themselves, so that a system can learn from and refine itself. This feedback loop is what allows algorithmic machine learning systems to provide carefully curated search responses or newsfeed arrangements or facial recognition results to consumers like me and you and your friends and family and the police and the military. And while there are many different types of algorithms which can be used for the above purposes, they all remain sets of encoded instructions to perform a function.

And so, in these systems’ defense, it’s no surprise that they think the way they do: That’s exactly how we’ve told them to think.

[Image of Michael Emerson as Harold Finch, in season 2, episode 1 of the show Person of Interest, “The Contingency.” His face is framed by a box of dashed yellow lines, the words “Admin” to the top right, and “Day 1” in the lower right corner.]

Hello Everyone.

Here is my prerecorded talk for the NC State R.L. Rabb Symposium on Embedding AI in Society.

There are captions in the video already, but I’ve also gone ahead and C/P’d the SRT text here, as well.
[2024 Note: Something in GDrive video hosting has broken the captions, but I’ve contacted them and hopefully they’ll be fixed soon.]

There were also two things I meant to mention, but failed to in the video:

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: Fitting the description: historical and sociotechnical elements of facial recognition and anti-black surveillance”.)

2) Not only does GPT-3 fail to write about humanities-oriented topics with respect, it still can’t write about ISLAM AT ALL without writing in connotations of violence and hatred.

Also I somehow forgot to describe the slide with my email address and this website? What the hell Damien.

Anyway.

I’ve embedded the content of the resource slides in the transcript, but those are by no means all of the resources on this, just the most pertinent.

All of that begins below the cut.

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 & red, one of wide untreated pine, and a large monitor with a printer and papers on the stand beneath it.

[First conference of the year; figured i might as well get gussied up.]

Audio, Transcripts, and Slides from “Any Sufficiently Advanced Neglect is Indistinguishable from Malice”

Posted by Damien P. Williams on August 20, 2019

Posted in: Uncategorized. Tagged: A Future Worth Thinking About, ableism, algorithmic bias, algorithmic justice, algorithmic systems, amazon, animal ethics, audio, bias, biomedical ethics, biotech ethics, compassion, consciousness, damien patrick williams, debbie chachra, disability, disability studies, embodied cognition, ethics, facebook, facial recognition, feminist ethics, Gayatri Spivak, gender, google, homophobia, implicit bias, intersubjectivity, Invisible Architecture of Bias, Invisible Architectures of Bias, joy buolamwini, kimberlé williams crenshaw, Machine ethics, machine intelligence, misogyny, my voice, my words, my work, my writing, neurodiversity, neuroethics, personhood rights, phenomenology, philosophy of mind, philosophy of technology, race, racism, ruha benjamin, science and technology studies, science technology and society, sexism, social cognition, social dynamics, Society for Philosophy and Technology, surveillance culture, systemic disparity, technological ethics, technology, transcripts, transphobia. 2 comments

Below are the slides, audio, and transcripts for my talk ‘”Any Sufficiently Advanced Neglect is Indistinguishable from Malice”: Assumptions and Bias in Algorithmic Systems,’ given at the 21st Conference of the Society for Philosophy and Technology, back in May 2019.

(Cite as: Williams, Damien P. ‘”Any Sufficiently Advanced Neglect is Indistinguishable from Malice”: Assumptions and Bias in Algorithmic Systems;’ talk given at the 21st Conference of the Society for Philosophy and Technology; May 2019)

Now, I’ve got a chapter coming out about this, soon, which I can provide as a preprint draft if you ask, and can be cited as “Constructing Situated and Social Knowledge: Ethical, Sociological, and Phenomenological Factors in Technological Design,” appearing in Philosophy And Engineering: Reimagining Technology And Social Progress. Guru Madhavan, Zachary Pirtle, and David Tomblin, eds. Forthcoming from Springer, 2019. But I wanted to get the words I said in this talk up onto some platforms where people can read them, as soon as possible, for a couple of reasons.

First, the Current Occupants of the Oval Office have very recently taken the policy position that algorithms can’t be racist, something which they’ve done in direct response to things like Google’s Hate Speech-Detecting AI being biased against black people, and Amazon claiming that its facial recognition can identify fear, without ever accounting for, i dunno, cultural and individual differences in fear expression?

[Free vector image of a white, female-presenting person, from head to torso, with biometric facial recognition patterns on her face; incidentally, go try finding images—even illustrations—of a non-white person in a facial recognition context.]

All these things taken together are what made me finally go ahead and get the transcript of that talk done, and posted, because these are events and policy decisions about which I a) have been speaking and writing for years, and b) have specific inputs and recommendations about, and which are, c) frankly wrongheaded, and outright hateful.

And I want to spend time on it because I think what doesn’t get through in many of our discussions is that it’s not just about how Artificial Intelligence, Machine Learning, or Algorithmic instances get trained, but the processes for how and the cultural environments in which HUMANS are increasingly taught/shown/environmentally encouraged/socialized to think is the “right way” to build and train said systems.

That includes classes and instruction, it includes the institutional culture of the companies, it includes the policy landscape in which decisions about funding and get made, because that drives how people have to talk and write and think about the work they’re doing, and that constrains what they will even attempt to do or even understand.

All of this is cumulative, accreting into institutional epistemologies of algorithm creation. It is a structural and institutional problem.

So here are the Slides:

The Audio:

[Direct Link to Mp3]

And the Transcript is here below the cut:

We do a lot of work and have a lot of conversations around here with people working on the social implications of technology, but some folx sometimes still don’t quite get what I mean when I say that our values get embedded in our technological systems, and that the values of most internet companies, right now, are capitalist brand engagement and marketing. To that end, I want to take a minute to talk to you about something that happened, this week and just a heads-up, this conversation is going to mention sexual assault and the sexual predatory behaviour of men toward young girls.
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My Appearance on The Machine Ethics Podcast’s A.I. Retreat Episode

Posted by Damien P. Williams on October 23, 2018

Posted in: Uncategorized. Tagged: algorithmic bias, algorithmic intelligence, algorithmic justice, algorithmic systems, andy budd, artificial intelligence, artificial superintelligence, audio, autonomous created intelligence, autonomous creative intelligence, autonomous generated intelligence, autonomously creative intelligence, ben byford, ben sauer, consciousness, Dan Saffer, design, disability studies, distributed machine consciousness, distributed networked intelligence, embodied machine consciousness, ethics, gender, gender studies, Hollie Lubbock, intelligence, juvet, machine consciousness, Machine ethics, machine intelligence, machine learning, machine minds, magic, magick, meta-ethics, moral agency, moral patiency, my voice, my words, neurodiversity, neuroethics, norway, phenomenology, philosophy of mind, philosophy of technology, podcasts, race, science and technology studies, science technology and society, Syd Lawrence, the machine ethics podcast, The Second Future of A.I. Retreat. Leave a Comment

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.

I am deeply grateful to Ben Byford for asking me to sit down and talk about this with him. I talk a great deal, and am surprisingly able to (cogently?) get on almost all of my bullshit—technology and magic and the occult, nonhuman personhood, the sham of gender and race and other social constructions of expected lived categories, the invisible architecture of bias, neurodiversity, and philosophy of mind—in a rather short window of time.

So that’s definitely something…

The Second Future of A.I. Retreat

Posted by Damien P. Williams on September 25, 2018

Posted in: Uncategorized. Tagged: A Future Worth Thinking About, afrofuturism, algorithmic bias, algorithmic intelligence, algorithmic justice, algorithmic systems, andy budd, animal ethics, artificial intelligence, artificial superintelligence, assistive technology, autonomous created intelligence, autonomous creative intelligence, autonomous generated intelligence, autonomous systems, autonomous weapons systems, autonomously creative intelligence, auto^2, bias, cennydd bowles, clearleft, communication, data, decolonization, design, disability, disability studies, distributed machine consciousness, economics, education, electronic personhood, embodied machine consciousness, ethics, ethnicity, gender, gender studies, implicit bias, intersectionality, Invisible Architecture of Bias, Invisible Architectures of Bias, juvet, kimberlé williams crenshaw, machine consciousness, Machine ethics, machine intelligence, machine learning, machine minds, man machine coevolution, moral agency, moral patiency, neurodiversity, neuroethics, nonhuman personhood, norway, personhood rights, philosophy of technology, science and technology studies, science technology and society, smart cities, sociology, superintelligence, systemic disparity, technological ethics, technology, the future of a.i. retreat, The Second Future of A.I. Retreat, values. 1 comment

Kirsten and I spent the week between the 17th and the 21st of September with 18 other utterly amazing people having Chatham House Rule-governed conversations about the Future of Artificial Intelligence.

We were in Norway, in the Juvet Landscape Hotel, which is where they filmed a lot of the movie Ex Machina, and it is even more gorgeous in person. None of the rooms shown in the film share a single building space. It’s astounding as a place of both striking architectural sensibility and also natural integration as they built every structure in the winter to allow the dormancy cycles of the plants and animals to dictate when and where they could build, rather than cutting anything down.

And on our first full day here, Two Ravens flew directly over my and Kirsten’s heads.

Yes.

[Image of a rainbow rising over a bend in a river across a patchy overcast sky, with the river going between two outcropping boulders, trees in the foreground and on either bank and stretching off into the distance, and absolutely enormous mountains in the background]

I am extraordinarily grateful to Andy Budd and the other members of the Clear Left team for organizing this, and to Cennydd Bowles for opening the space for me to be able to attend, and being so forcefully enthused about the prospect of my attending that he came to me with a full set of strategies in hand to get me to this place. That kind of having someone in your corner means the world for a whole host of personal reasons, but also more general psychological and socially important ones, as well.

I am a fortunate person. I am a person who has friends and resources and a bloody-minded stubbornness that means that when I determine to do something, it will more likely than not get fucking done, for good or ill.

I am a person who has been given opportunities to be in places many people will never get to see, and have conversations with people who are often considered legends in their fields, and start projects that could very well alter the shape of the world on a massive scale.

Yeah, that’s a bit of a grandiose statement, but you’re here reading this, and so you know where I’ve been and what I’ve done.

I am a person who tries to pay forward what I have been given and to create as many spaces for people to have the opportunities that I have been able to have.

I am not a monetarily wealthy person, measured against my society, but my wealth and fortune are things that strike me still and make me take stock of it all and what it can mean and do, all over again, at least once a week, if not once a day, as I sit in tension with who I am, how the world perceives me, and what amazing and ridiculous things I have had, been given, and created the space to do, because and in violent spite of it all.

So when I and others come together and say we’re going to have to talk about how intersectional oppression and the lived experiences of marginalized peoples affect, effect, and are affected and effected BY the wider techoscientific/sociotechnical/sociopolitical/socioeconomic world and what that means for how we design, build, train, rear, and regard machine minds, then we are going to have to talk about how intersectional oppression and the lived experiences of marginalized peoples affect, effect, and are affected and effected by the wider techoscientific/sociotechnical/sociopolitical/socioeconomic world and what that means for how we design, build, train, rear, and regard machine minds.

So let’s talk about what that means.

Human Futures and Intelligent Machines (Part 2)

Posted by Damien P. Williams on July 9, 2018

Posted in: Uncategorized. Tagged: A Future Worth Thinking About, Aaron Nicholson, african diaspora, afrofuturism, algorithmic bias, algorithmic justice, algorithmic systems, artificial intelligence, autonomous created intelligence, autonomous generated intelligence, autonomous weapons systes, bias, bioethics, biotech ethics, biotechnology, Chelsea Frazier, comparative religion, cybernetics, cyborgs, Daylan Dufelmeier, distributed machine consciousness, economics, embodied machine consciousness, Emma Stamm, ethics, facial recognition, feminist ethics, gender, Hani Awni, human augmentation, human biotechnological intervention, Jacob Thebault-Spieker, Josh Brown, Joshua Earle, Kent Myers, Machine ethics, machine learning, magic, magick, Mario Khreiche, nonhuman personhood, philosophy of engineering, philosophy of mind, philosophy of technology, police body cameras, police militarization, post-work economy, post-worker economy, race, Ralph Hall, Ras Michael Brown, religious history, religious studies, Sara Mattingly-Jordan, science and technology studies, science technology and society, sociology, surveillance culture, Sylvester Johnson, technoccult, technological ethics, technology and religion, values. Leave a Comment

Previously, I told you about The Human Futures and Intelligent Machines Summit at Virginia Tech, and now that it’s over, I wanted to go ahead and put the full rundown of the events all in one place.

The goals for this summit were to start looking at the ways in which issues of algorithms, intelligent machine systems, human biotech, religion, surveillance, and more will intersect and affect us in the social, academic, political spheres. The big challenge in all of this was seen as getting better at dealing with this in the university and public policy sectors, in America, rather than the seeming worse we’ve gotten, so far.

Here’s the schedule. Full notes, below the cut.

Friday, June 8, 2018

Josh Brown on “the distinction between passive and active AI.”
Daylan Dufelmeier on “the potential ramifications of using advanced computing in the criminal justice arena…”
Mario Khreiche on the effects of automation, Amazon’s Mechanical Turk, and the Microlabor market.
Aaron Nicholson on how technological systems are used to support human social outcomes, specifically through the lens of policing in the city of Atlanta
Ralph Hall on “the challenges society will face if current employment and income trends persist into the future.”
Jacob Thebault-Spieker on “how pro-urban and pro-wealth biases manifest in online systems, and how this likely influences the ‘education’ of AI systems.”
Hani Awni on the sociopolitical of excluding ‘relational’ knowledge from AI systems.

Saturday, June 9, 2018

Chelsea Frazier on rethinking our understandings of race, biocentrism, and intelligence in relation to planetary sustainability and in the face of increasingly rapid technological advancement.
Ras Michael Brown on using the religions technologies of West Africa and the West African Diaspora to reframe how we think about “hybrid humanity.”
Damien Williams on how best to use interdisciplinary frameworks in the creation of machine intelligence and human biotechnological interventions.
Sara Mattingly-Jordan on the implications of the current global landscape in AI ethics regulation.
Kent Myers on several ways in which the intelligence community is engaging with human aspects of AI, from surveillance to sentiment analysis.
Emma Stamm on the idea that datafication of the self and what about us might be uncomputable.
Joshua Earle on “Morphological Freedom.”

I talked with Hewlett Packard Enterprise’s Curt Hopkins, for their article “4 obstacles to ethical AI (and how to address them).” We spoke about the kinds of specific tools and techniques by which people who populate or manage artificial intelligence design teams can incorporate expertise from the humanities and social sciences. We also talked about compelling reasons why they should do this, other than the fact that they’re just, y’know, very good ideas.

From the Article:

To “bracket out” bias, Williams says, “I have to recognize how I create systems and code my understanding of the world.” That means making an effort early on to pay attention to the data entered. The more diverse the group, the less likely an AI system is to reinforce shared bias. Those issues go beyond gender and race; they also encompass what you studied, the economic group you come from, your religious background, all of your experiences.
…
That becomes another reason to diversify the technical staff, says Williams. This is not merely an ethical act. The business strategy may produce more profit because the end result may be a more effective AI. “The best system is the one that best reflects the wide range of lived experiences and knowledge in the world,” he says.

[Image of two blank, white, eyeless faces, partially overlapping each other.]

To be clear, this is an instance in which I tried to find capitalist reasons that would convince capitalist people to do the right thing. To that end, you should imagine that all of my sentences start with “Well if we’re going to continue to be stuck with global capitalism until we work to dismantle it…” Because they basically all did.

I get how folx might think that framing would be a bit of a buzzkill for a tech industry audience, but I do want to highlight and stress something: Many of the ethical problems we’re concerned with mitigating or ameliorating are direct products of the capitalist system in which we are making these choices and building these technologies.

All of that being said, I’m not the only person there with something interesting to say, and you should go check out the rest of my and other people’s comments.

Until Next Time.

The Human Futures and Intelligent Machines Summit at Virginia Tech

Posted by Damien P. Williams on June 8, 2018

Posted in: Uncategorized. Tagged: A Future Worth Thinking About, afrofuturism, algorithmic bias, algorithmic justice, algorithmic systems, artificial intelligence, autonomous created intelligence, autonomous generated intelligence, autonomous weapons systes, bioethics, biotech ethics, biotechnology, cybernetics, cyborgs, distributed machine consciousness, embodied machine consciousness, ethics, feminist ethics, gender, human augmentation, human biotechnological intervention, Machine ethics, machine learning, nonhuman personhood, philosophy of engineering, philosophy of mind, philosophy of technology, police body cameras, police militarization, post-work economy, post-worker economy, race, science and technology studies, science technology and society, sociology, technoccult, technological ethics, values. 1 comment

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, below the cut.

A Future Worth Thinking About

Thinking about magic, cyborgs, robots, and artificial intelligence--and why some of those words could use changing--since 1982.

algorithmic justice

All posts tagged algorithmic justice

Video and Transcript: “Why AI Research Needs Disabled and Marginalized Perspectives”

My Appearance on The Machine Ethics Podcast’s A.I. Retreat Episode

The Second Future of A.I. Retreat

Human Futures and Intelligent Machines (Part 2)

At HPE: “4 obstacles to ethical AI (and how to address them)”

The Human Futures and Intelligent Machines Summit at Virginia Tech

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