social dynamics

All posts tagged social dynamics

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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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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[Direct Link to Mp3]

Above is the (heavily edited) audio of my final talk for the SRI Technology and Consciousness Workshop Series. The names and voices of other participants have been removed in accordance with the Chatham House Rule.

Below you’ll find the slide deck for my presentation, and below the cut you’ll find the Outline and my notes. For now, this will have to stand in for a transcript, but if you’ve been following the Technoccult Newsletter or the Patreon, then some of this will be strikingly familiar.

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This summer I participated in SRI International’s Technology and Consciousness Workshop Series. The meetings were held under the auspices of the Chatham House Rule, which means that there are many things I can’t tell you about them, such as who else was there, or what they said in the context of the meetings; however I can tell you what I talked about. In light of this recent piece in The Boston Globe and the ongoing developments in the David Slater/PETA/Naruto case, I figured that now was a good time to do so.

I presented three times—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. This is my outline and notes for the first of those talks.

I. Overview
In a 2013 aeon Article Michael Hanlon said he didn’t think we’d ever solve “The Hard Problem,” and there’s been some skepticism about it, elsewhere. I’ll just say that said question seems to completely miss a possibly central point. Something like consciousness is, and what it is is different for each thing that displays anything like what we think it might be. If we manage to generate at least one mind that is similar enough to what humans experience as “conscious” that we may communicate with it, what will we owe it and what would it be able to ask from us? How might our interactions be affected by the fact that its mind (or their minds) will be radically different from ours? What will it be able to know that we cannot, and what will we have to learn from it?

So I’m going to be talking today about intersectionality, embodiment, extended minds, epistemic valuation, phenomenological experience, and how all of these things come together to form the bases for our moral behavior and social interactions. To do that, I’m first going to need ask you some questions:

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(Direct Link to the Mp3)

On Friday, I needed to do a thread of a thing, so if you hate threads and you were waiting until I collected it, here it is.

But this originally needed to be done in situ. It needed to be a serialized and systematized intervention and imposition into the machinery of that particular flow of time. That day…

There is a principle within many schools of magical thought known as “shielding.” In practice and theory, it’s closely related to the notion of “grounding” and the notion of “centering.” (If you need to think of magical praxis as merely a cipher for psychological manipulation toward particular behaviours or outcomes, these all still scan.)

When you ground, when you centre, when you shield, you are anchoring yourself in an awareness of a) your present moment, your temporality; b) your Self and all emotions and thoughts; and c) your environment. You are using your awareness to carve out a space for you to safely inhabit while in the fullness of that awareness. It’s a way to regroup, breathe, gather yourself, and know what and where you are, and to know what’s there with you.

You can shield your self, your home, your car, your group of friends, but moving parts do increase the complexity of what you’re trying to hold in mind, which may lead to anxiety or frustration, which kind of opposes the exercise’s point. (Another sympathetic notion, here, is that of “warding,” though that can be said to be more for objects,not people.)

So what is the point?

The point is that many of us are being drained, today, this week, this month, this year, this life, and we need to remember to take the time to regroup and recharge. We need to shield ourselves, our spaces, and those we love, to ward them against those things that would sap us of strength and the will to fight. We know we are strong. We know that we are fierce, and capable. But we must not lash out wildly, meaninglessly. We mustn’t be lured into exhausting ourselves. We must collect ourselves, protect ourselves, replenish ourselves, and by “ourselves” I also obviously mean “each other.”

Mutual support and endurance will be crucial.

…So imagine that you’ve built a web out of all the things you love, and all of the things you love are connected to each other and the strands between them vibrate when touched. And you touch them all, yes?

And so you touch them all and they all touch you and the energy you generate is cyclically replenished, like ocean currents and gravity. And you use what you build—that thrumming hum of energy—to blanket and to protect and to energize that which creates it.

And we’ll do this every day. We’ll do this like breathing. We’ll do this like the way our muscles and tendons and bones slide across and pull against and support each other. We’ll do this like heartbeats. Cyclical. Mutually supporting. The burden on all of us, together, so that it’s never on any one of us alone.

So please take some time today, tomorrow, very soon to build your shields. Because, soon, we’re going to need you to deploy them more and more.

Thank you, and good luck.


The audio and text above are modified versions of this Twitter thread. This isn’t the first time we’ve talked about the overlap of politics, psychology, philosophy, and magic, and if you think it’ll be the last, then you haven’t been paying attention.

Sometimes, there isn’t much it feels like we can do, but we can support  and shield each other. We have to remember that, in the days, weeks, month, and years to come. We should probably be doing our best to remember it forever.

Anyway, I hope this helps.

Until Next Time

So I’m quoted in this article in The Atlantic on the use of technology in leveraging sociological dynamics to combat online harassment: “Why Online Allies Matter in Fighting Harassment.”

An experiment by Kevin Munger used bots to test which groups white men responded to when being called out on their racist harassment online. Findings largely unsurprising (Powerful white men; they responded favourably to powerful white men), save for the fact that anonimity INCREASED effectiveness of treatment, and visible identity decreased it. That one was weird. But it’s still nice to see all of this codified.

Good to see use of Bertrand & Mullainathan’s “Are Emily and Greg more employable than Lakisha and Jamal?” as the idea of using “Black Sounding Names” to signal purported ethnicity of bot thus clearly models what he thought those he expected to be racist would think, rather than indicating his own belief. (However, it could be asked whether there’s a meaningful difference, here, as he still had to choose the names he thought would “sound black.”)

The Reactance study Munger discusses—the one that shows that people double down on factually incorrect prejudices—is the same one I used in “On The Invisible Architecture of Bias

A few things Ed Yong and I talked about that didn’t get into the article, due to space:

-Would like to see this experimental model applied to other forms of prejudice (racist, sexist, homophobic, transphobic, ableist, etc language), and was thus very glad to see the footnote about misogynist harassment.

-I take some exception to the use of Dovidio/Gaertner and Crandall et al definitions of racism, as those leave out the sociological aspects of power dynamics (“Racism/Sexism/Homophobia/Transphobia/Ableism= Prejudice + Power”) which seem crucial to understanding the findings of Munger’s experiment. He skirts close to this when he discusses the greater impact of “high status” individuals, but misses the opportunity to lay out the fact that:
–Institutionalised power dynamics as related to the interplay of in-group and out-group behaviour are pretty clearly going to affect why white people are more likely to listen to those they perceive as powerful white men, because
–The interplay of Power and status, interpersonally, is directly related to power and status institutionally.

-Deindividuation (loss of sense of self in favour of group identity) as a key factor and potential solution is very interesting.

Something we didn’t get to talk about but which I think is very important is the question of how we keep this from being used as a handbook. That is, what do we do in the face of people understand these mechanisms and who wish to use them to sow division and increase acceptance of racist, sexist, homophobic, transphobic, ableist, etc ideals? Do we, then, become engaged in some kind of rolling arms race of sociological pressure?

…Which, I guess, has pretty much always been true, and we call it “civilization.”

Anyway, hope you enjoy it.