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Appendix A: An Imagined and Incomplete Conversation about “Consciousness” and “AI,” Across Time

Every so often, I think about the fact of one of the best things my advisor and committee members let me write and include in my actual doctoral dissertation, and I smile a bit, and since I keep wanting to share it out into the world, I figured I should put it somewhere more accessible.

So with all of that said, we now rejoin An Imagined and Incomplete Conversation about “Consciousness” and “AI,” Across Time, already (still, seemingly unendingly) in progress:

René Descartes (1637):
The physical and the mental have nothing to do with each other. Mind/soul is the only real part of a person.

Norbert Wiener (1948):
I don’t know about that “only real part” business, but the mind is absolutely the seat of the command and control architecture of information and the ability to reflexively reverse entropy based on context, and input/output feedback loops.

Alan Turing (1952):
Huh. I wonder if what computing machines do can reasonably be considered thinking?

Wiener:
I dunno about “thinking,” but if you mean “pockets of decreasing entropy in a framework in which the larger mass of entropy tends to increase,” then oh for sure, dude.

John Von Neumann (1958):
Wow things sure are changing fast in science and technology; we should maybe slow down and think about this before that change hits a point beyond our ability to meaningfully direct and shape it— a singularity, if you will.

Clynes & Klines (1960):
You know, it’s funny you should mention how fast things are changing because one day we’re gonna be able to have automatic tech in our bodies that lets us pump ourselves full of chemicals to deal with the rigors of space; btw, have we told you about this new thing we’re working on called “antidepressants?”

Gordon Moore (1965):
Right now an integrated circuit has 64 transistors, and they keep getting smaller, so if things keep going the way they’re going, in ten years they’ll have 65 THOUSAND. :-O

Donna Haraway (1991):
We’re all already cyborgs bound up in assemblages of the social, biological, and techonological, in relational reinforcing systems with each other. Also do you like dogs?

Ray Kurzweil (1999):
Holy Shit, did you hear that?! Because of the pace of technological change, we’re going to have a singularity where digital electronics will be indistinguishable from the very fabric of reality! They’ll be part of our bodies! Our minds will be digitally uploaded immortal cyborg AI Gods!

Tech Bros:
Wow, so true, dude; that makes a lot of sense when you think about it; I mean maybe not “Gods” so much as “artificial super intelligences,” but yeah.

90’s TechnoPagans:
I mean… Yeah? It’s all just a recapitulation of The Art in multiple technoscientific forms across time. I mean (*takes another hit of salvia*) if you think about the timeless nature of multidimensional spiritual architectures, we’re already—

DARPA:
Wait, did that guy just say something about “Uploading” and “Cyborg/AI Gods?” We got anybody working on that?? Well GET TO IT!

Disabled People, Trans Folx, BIPOC Populations, Women:
Wait, so our prosthetics, medications, and relational reciprocal entanglements with technosocial systems of this world in order to survive makes us cyborgs?! :-O

[Simultaneously:]

Kurzweil/90’s TechnoPagans/Tech Bros/DARPA:
Not like that.
Wiener/Clynes & Kline:
Yes, exactly.

Haraway:
I mean it’s really interesting to consider, right?

Tech Bros:
Actually, if you think about the bidirectional nature of time, and the likelihood of simulationism, it’s almost certain that there’s already an Artificial Super Intelligence, and it HATES YOU; you should probably try to build it/never think about it, just in case.

90’s TechnoPagans:
…That’s what we JUST SAID.

Philosophers of Religion (To Each Other):
…Did they just Pascal’s Wager Anselm’s Ontological Argument, but computers?

Timnit Gebru and other “AI” Ethicists:
Hey, y’all? There’s a LOT of really messed up stuff in these models you started building.

Disabled People, Trans Folx, BIPOC Populations, Women:
Right?

Anthony Levandowski:
I’m gonna make an AI god right now! And a CHURCH!

The General Public:
Wait, do you people actually believe this?

Microsoft/Google/IBM/Facebook:
…Which answer will make you give us more money?

Timnit Gebru and other “AI” Ethicists:
…We’re pretty sure there might be some problems with the design architectures, too…

Some STS Theorists:
Honestly this is all a little eugenics-y— like, both the technoscientific and the religious bits; have you all sought out any marginalized people who work on any of this stuff? Like, at all??

Disabled People, Trans Folx, BIPOC Populations, Women:
Hahahahah! …Oh you’re serious?

Anthony Levandowski:
Wait, no, nevermind about the church.

Some “AI” Engineers:
I think the things we’re working on might be conscious, or even have souls.

“AI” Ethicists/Some STS Theorists:
Anybody? These prejudices???

Wiener/Tech Bros/DARPA/Microsoft/Google/IBM/Facebook:
“Souls?” Pfffft. Look at these whackjobs, over here. “Souls.” We’re talking about the technological singularity, mind uploading into an eternal digital universal superstructure, and the inevitability of timeless artificial super intelligences; who said anything about “Souls?”

René Descartes/90’s TechnoPagans/Philosophers of Religion/Some STS Theorists/Some “AI” Engineers:

[Scene]


Read more of this kind of thing at:
Williams, Damien Patrick. Belief, Values, Bias, and Agency: Development of and Entanglement with “Artificial Intelligence.” PhD diss., Virginia Tech, 2022. https://vtechworks.lib.vt.edu/handle/10919/111528.

As of this week, I have a new article in the July-August 2023 Special Issue of American Scientist Magazine. It’s called “Bias Optimizers,” and it’s all about the problems and potential remedies of and for GPT-type tools and other “A.I.”

This article picks up and expands on thoughts started in “The ‘P’ Stands for Pre-Trained” and in a few threads on the socials, as well as touching on some of my comments quoted here, about the use of chatbots and “A.I.” in medicine.

I’m particularly proud of the two intro grafs:

Recently, I learned that men can sometimes be nurses and secretaries, but women can never be doctors or presidents. I also learned that Black people are more likely to owe money than to have it owed to them. And I learned that if you need disability assistance, you’ll get more of it if you live in a facility than if you receive care at home.

At least, that is what I would believe if I accepted the sexist, racist, and misleading ableist pronouncements from today’s new artificial intelligence systems. It has been less than a year since OpenAI released ChatGPT, and mere months since its GPT-4 update and Google’s release of a competing AI chatbot, Bard. The creators of these systems promise they will make our lives easier, removing drudge work such as writing emails, filling out forms, and even writing code. But the bias programmed into these systems threatens to spread more prejudice into the world. AI-facilitated biases can affect who gets hired for what jobs, who gets believed as an expert in their field, and who is more likely to be targeted and prosecuted by police.

As you probably well know, I’ve been thinking about the ethical, epistemological, and social implications of GPT-type tools and “A.I.” in general for quite a while now, and I’m so grateful to the team at American Scientist for the opportunity to discuss all of those things with such a broad and frankly crucial audience.

I hope you enjoy it.

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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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.]

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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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