my work

All posts tagged my work

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, showing two flowcharts, one on top of the other. The top one is labeled "today" and has the top level condition "task" branching to both a confused looking human user and state called "learned function" which is determined by a previous state labeled "machine learning process" which is determined by a state labeled "training data." "Learned Function" feeds "Decision or Recommendation" to the human user, who has several questions about the model's beaviour, such as "why did you do that?" and "when can i trust you?" The bottom one is labeled "XAI" and has the top level condition "task" branching to both a happy and confident looking human user and state called "explainable model/explanation interface" which is determined by a previous state labeled "new machine learning process" which is determined by a state labeled "training data." "explainable model/explanation interface" feeds choices to the human user, who can feed responses BACK to the system, and who has several confident statements about the model's beaviour, such as "I understand why" and "I know when to trust you."

An “Explainable AI” diagram from DARPA

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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.]

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Much of my research deals with the ways in which bodies are disciplined and how they go about resisting that discipline. In this piece, adapted from one of the answers to my PhD preliminary exams written and defended two months ago, I “name the disciplinary strategies that are used to control bodies and discuss the ways that bodies resist those strategies.” Additionally, I address how strategies of embodied control and resistance have changed over time, and how identifying and existing as a cyborg and/or an artificial intelligence can be understood as a strategy of control, resistance, or both.

In Jan Golinski’s Making Natural Knowledge, he spends some time discussing the different understandings of the word “discipline” and the role their transformations have played in the definition and transmission of knowledge as both artifacts and culture. In particular, he uses the space in section three of chapter two to discuss the role Foucault has played in historical understandings of knowledge, categorization, and disciplinarity. Using Foucault’s work in Discipline and Punish, we can draw an explicit connection between the various meanings “discipline” and ways that bodies are individually, culturally, and socially conditioned to fit particular modes of behavior, and the specific ways marginalized peoples are disciplined, relating to their various embodiments.

This will demonstrate how modes of observation and surveillance lead to certain types of embodiments being deemed “illegal” or otherwise unacceptable and thus further believed to be in need of methodologies of entrainment, correction, or reform in the form of psychological and physical torture, carceral punishment, and other means of institutionalization.

Locust, “Master and Servant (Depeche Mode Cover)”

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Below are the slides, audio, and transcripts for my talk “SFF and STS: Teaching Science, Technology, and Society via Pop Culture” given at the 2019 Conference for the Society for the Social Studies of Science, in early September.

(Cite as: Williams, Damien P. “SFF and STS: Teaching Science, Technology, and Society via Pop Culture,” talk given at the 2019 Conference for the Society for the Social Studies of Science, September 2019)

[Direct Link to the Mp3]

[Damien Patrick Williams]

Thank you, everybody, for being here. I’m going to stand a bit far back from this mic and project, I’m also probably going to pace a little bit. So if you can’t hear me, just let me know. This mic has ridiculously good pickup, so I don’t think that’ll be a problem.

So the conversation that we’re going to be having today is titled as “SFF and STS: Teaching Science, Technology, and Society via Pop Culture.”

I’m using the term “SFF” to stand for “science fiction and fantasy,” but we’re going to be looking at pop culture more broadly, because ultimately, though science fiction and fantasy have some of the most obvious entrees into discussions of STS and how making doing culture, society can influence technology and the history of fictional worlds can help students understand the worlds that they’re currently living in, pop Culture more generally, is going to tie into the things that students are going to care about in a way that I think is going to be kind of pertinent to what we’re going to be talking about today.

So why we are doing this:

Why are we teaching it with science fiction and fantasy? Why does this matter? I’ve been teaching off and on for 13 years, I’ve been teaching philosophy, I’ve been teaching religious studies, I’ve been teaching Science, Technology and Society. And I’ve been coming to understand as I’ve gone through my teaching process that not only do I like pop culture, my students do? Because they’re people and they’re embedded in culture. So that’s kind of shocking, I guess.

But what I’ve found is that one of the things that makes students care the absolute most about the things that you’re teaching them, especially when something can be as dry as logic, or can be as perhaps nebulous or unclear at first, I say engineering cultures, is that if you give them something to latch on to something that they are already from with, they will be more interested in it. If you can show to them at the outset, “hey, you’ve already been doing this, you’ve already been thinking about this, you’ve already encountered this, they will feel less reticent to engage with it.”

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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:

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2017 SRI Technology and Consciousness Workshop Series Final Report

So, as you know, back in the summer of 2017 I participated in SRI International’s Technology and Consciousness Workshop Series. This series was an eight week program of workshops the current state of the field around, the potential future paths toward, and the moral and social implications of the notion of conscious machines. To do this, we brought together a rotating cast of dozens of researchers in AI, machine learning, psychedelics research, ethics, epistemology, philosophy of mind, cognitive computing, neuroscience, comparative religious studies, robotics, psychology, and much more.

Image of a rectangular name card with a stylized "Technology & Consciousness" logo, at the top, the name Damien Williams in bold in the middle, and SRI International italicized at the bottom; to the right a blurry wavy image of what appears to be a tree with a person standing next to it and another tree in the background to the left., all partially mirrored in a surface at the bottom of the image.

[Image of my name card from the Technology & Consciousness workshop series.]

We traveled from Arlington, VA, to Menlo Park, CA, to Cambridge, UK, and back, and while my primary role was that of conference co-ordinator and note-taker (that place in the intro where it says I “maintained scrupulous notes?” Think 405 pages/160,656 words of notes, taken over eight 5-day weeks of meetings), I also had three separate opportunities to present: Once on interdisciplinary perspectives on minds and mindedness; then on Daoism and Machine Consciousness; and finally on a unifying view of my thoughts across all of the sessions. In relation to this report, I would draw your attention to the following passage:

An objection to this privileging of sentience is that it is anthropomorphic “meat chauvinism”: we are projecting considerations onto technology that derive from our biology. Perhaps conscious technology could have morally salient aspects distinct from sentience: the basic elements of its consciousness could be different than ours.

All of these meetings were held under the auspices of the Chatham House Rule, which meant that there were many things I couldn’t tell you about them, such as the names of the other attendees, or what exactly they said in the context of the meetings. What I was able tell you, however, was what I talked about, and I did, several times. But as of this week, I can give you even more than that.

This past Thursday, SRI released an official public report on all of the proceedings and findings from the 2017 SRI Technology and Consciousness Workshop Series, and they have told all of the participants that they can share said report as widely as they wish. Crucially, that means that I can share it with you. You can either click this link, here, or read it directly, after the cut.

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[This paper was prepared for the 2019 Towards Conscious AI Systems Symposium co-located with the Association for the Advancement of Artificial Intelligence 2019 Spring Symposium Series.

Much of this work derived from my final presentation at the 2017 SRI Technology and Consciousness Workshop Series: “Science, Ethics, Epistemology, and Society: Gains for All via New Kinds of Minds”.]

Abstract. This paper explores the moral, epistemological, and legal implications of multiple different definitions and formulations of human and nonhuman consciousness. Drawing upon research from race, gender, and disability studies, including the phenomenological basis for knowledge and claims to consciousness, I discuss the history of the struggles for personhood among different groups of humans, as well as nonhuman animals, and systems. In exploring the history of personhood struggles, we have a precedent for how engagements and recognition of conscious machines are likely to progress, and, more importantly, a roadmap of pitfalls to avoid. When dealing with questions of consciousness and personhood, we are ultimately dealing with questions of power and oppression as well as knowledge and ontological status—questions which require a situated and relational understanding of the stakeholders involved. To that end, I conclude with a call and outline for how to place nuance, relationality, and contextualization before and above the systematization of rules or tests, in determining or applying labels of consciousness.

Keywords: Consciousness, Machine Consciousness, Philosophy of Mind, Phenomenology, Bodyminds

[Overlapping images of an Octopus carrying a shell, a Mantis Shrimp on the sea floor, and a Pepper Robot]

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Late last month, I was at Theorizing the Web, in NYC, to moderate Panel B3, “Bot Phenomenology,” in which I was very grateful to moderate a panel of people I was very lucky to be able to bring together. Johnathan Flowers, Emma Stamm, and Robin Zebrowski were my interlocutors in a discussion about the potential nature of nonbiological phenomenology. Machine consciousness. What robots might feel.

I led them through with questions like “What do you take phenomenology to mean?” and “what do you think of the possibility of a machine having a phenomenology of its own?” We discussed different definitions of “language” and “communication” and “body,” and unfortunately didn’t have a conversation about how certain definitions of those terms mean that what would be considered language between cats would be a cat communicating via signalling to humans.

It was a really great conversation and the Live Stream video for this is here, and linked below (for now, but it may go away at some point, to be replaced by a static youtube link; when I know that that’s happened, I will update links and embeds, here).

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Earlier this month I was honoured to have the opportunity to sit and talk to Douglas Rushkoff on his TEAM HUMAN podcast. If you know me at all, you know this isn’t by any means the only team for which I play, or even the only way I think about the construction of our “teams,” and that comes up in our conversation. We talk a great deal about algorithms, bias, machine consciousness, culture, values, language, and magick, and the ways in which the nature of our categories deeply affect how we treat each other, human and nonhuman alike. It was an absolutely fantastic time.

From the page:

In this episode, Williams and Rushkoff look at the embedded biases of technology and the values programed into our mediated lives. How has a conception of technology as “objective” blurred our vision to the biases normalized within these systems? What ethical interrogation might we apply to such technology? And finally, how might alternative modes of thinking, such as magick, the occult, and the spiritual help us to bracket off these systems for pause and critical reflection? This conversation serves as a call to vigilance against runaway systems and the prejudices they amplify.

As I put it in the conversation: “Our best interests are at best incidental to [capitalist systems] because they will keep us alive long enough to for us to buy more things from them.” Following from that is the fact that we build algorithmic systems out of those capitalistic principles, and when you iterate out from there—considering all attendant inequalities of these systems on the merely human scale—we’re in deep trouble, fast.

Check out the rest of this conversation to get a fuller understanding of how it all ties in with language and the occult. It’s a pretty great ride, and I hope you enjoy it.

Until Next Time.

So, many of you may remember that back in June of 2016, I was invited to the Brocher Institute in Hermance, Switzerland, on the shores of Lake Geneva, to take part in the Frankenstein’s Shadow Symposium sponsored by Arizona State University’s Center for Science and the Imagination as part of their Frankenstein Bicentennial project.

While there, I and a great many other thinkers in art, literature, history, biomedical ethics, philosophy, and STS got together to discuss the history and impact of Mary Shelley’s Frankenstein. Since that experience, the ASU team compiled and released a book project: A version of Mary Shelley’s seminal work that is filled with annotations and essays, and billed as being “For Scientists, Engineers, and Creators of All Kinds.”

[Image of the cover of the 2017 edited, annotated edition of Mary Shelley’s Frankenstein, “Annotated for Scientists, Engineers, and Creators of All Kinds.”]

Well, a few months ago, I was approached by the organizers and asked to contribute to a larger online interactive version of the book—to provide an annotation on some aspect of the book I deemed crucial and important to understand. As of now, there is a full functional live beta version of the website, and you can see my contribution and the contributions of many others, there.

From the About Page:

Frankenbook is a collective reading and collaborative annotation experience of the original 1818 text of Frankenstein; or, The Modern Prometheus, by Mary Wollstonecraft Shelley. The project launched in January 2018, as part of Arizona State University’s celebration of the novel’s 200th anniversary. Even two centuries later, Shelley’s modern myth continues to shape the way people imagine science, technology, and their moral consequences. Frankenbook gives readers the opportunity to trace the scientific, technological, political, and ethical dimensions of the novel, and to learn more about its historical context and enduring legacy.

To learn more about Arizona State University’s celebration of Frankenstein’s bicentennial, visit frankenstein.asu.edu.

You’ll need to have JavaScript enabled and ad-blocks disabled to see the annotations, but it works quite well. Moving forward, there will be even more features added, including a series of videos. Frankenbook.org will be the place to watch for all updates and changes.

I am deeply honoured to have been asked to be a part of this amazing project, over the past two years, and I am so very happy that I get to share it with all of you, now. I really hope you enjoy it.

Until Next Time.