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ChatGPT is Actively Marketing to Students During University Finals Season

It’s really disheartening and honestly kind of telling that in spite of everything, ChatGPT is actively marketing itself to students in the run-up to college finals season.

We’ve talked many (many) times before about the kinds of harm that can come from giving over too much epistemic and heuristic authority over to systems built by people who have repeatedly, doggedly proven that they will a) buy into their own hype and b) refuse to ever question their own biases and hubris. But additionally, there’s been at least two papers in the past few months alone, and more in the last two years (1, 2, 3), demonstrating that over-reliance on “AI” tools diminishes critical thinking capacity and prevents students from building the kinds of foundational skills which allow them to learn more complex concepts, adapt to novel situations, and grow into experts.

Screenshot of ChatpGPT page:ChaptGPT Promo: 2 months free for students ChatGPT Plus is now free for college students through May Offer valid for students in the US and Canada [Buttons reading "Claim offer" and "learn more" An image of a pencil scrawling a scribbly and looping line] ChatGPT Plus is here to help you through finals

Screenshot of ChatGPT[.]com/students showing an introductory offer for college students during finals; captured 04/04/2025

That lack of expertise and capacity has a direct impact on people’s ability to discern facts, produce knowledge, and even participate in civic/public life. The diminishment of critical thinking skills makes people more susceptible to propaganda and other forms of dis- and misinformation— problems which, themselves, are already being exacerbated by the proliferation of “Generative AI” text and image systems and people not fulling understanding them for the bullshit engines they are.

The abovementioned susceptibility allows authoritarian-minded individuals and groups to thus further degrade belief in shared knowledge and consensus reality and to erode trust in expertise, thus exacerbating and worsening the next turn on the cycle when it starts all over again.

All of this creates the very conditions by which authoritarians seek to cement their control: by undercutting the individual tools and social mechanisms which can empower the populace to understand and challenge the kinds of damage dictators, theocrats, fascists, and kleptocrats seek to do on the path to enriching themselves and consolidating power.

And here’s OpenAI flagrantly encouraging said over-reliance. The original post on linkedIn even has an image of someone prompting ChatGPT to guide them on “mastering [a] calc 101 syllabus in two weeks.” So that’s nice.

No wait; the other thing… Terrible. It’s terrible.

View Kate Rouch’s graphic linkKate RouchKate Rouch • 3rd+Premium • 3rd+ Chief Marketing Officer at OpenAI.Chief Marketing Officer at OpenAI. 21h • Edited • 21 hours ago • Edited • Visible to anyone on or off LinkedIn ChatGPT Plus is free during finals! We can’t achieve our mission without empowering young people to use AI. Fittingly, today we launched our first scaled marketing campaign. The campaign shows students different ways to take advantage of ChatGPT as they study, work out, try to land jobs, and plan their summers. It also offers ChatGPT Plus’s more advanced capabilities to students for free through their finals. You’ll see creative on billboards, digital ads, podcasts, and more throughout the coming weeks. We hope you learn something useful! If you’re a college student in the US or Canada, you can claim the offer at www.chatgpt.com/students

Screenshot of a linkedIn post from OpenAI’s chief marketing officer. Captured 04/04/2025

Understand this. Push back against it. Reject its wholesale uncritical adoption and proliferation. Demand a more critical and nuanced stance on “AI” from yourself, from your representatives at every level, and from every company seeking to shove this technology down our throats.

Audio, Slides, and Transcript for my 2024 SEAC Keynote

Back in October, I was the keynote speaker for the Society for Ethics Across the Curriculum‘s 25th annual conference. My talk was titled “On Truth, Values, Knowledge, and Democracy in the Age of Generative ‘AI,’” and it touched on a lot of things that I’ve been talking and writing about for a while (in fact, maybe the title is familiar?), but especially in the past couple of years. Covered deepfakes, misinformation, disinformation, the social construction of knowledge, artifacts, and consensus reality, and more. And I know it’s been a while since the talk, but it’s not like these things have gotten any less pertinent, these past months.

As a heads-up, I didn’t record the Q&A because I didn’t get the audience’s permission ahead of time, and considering how much of this is about consent, that’d be a little weird, yeah? Anyway, it was in the Q&A section where we got deep into the environmental concerns of water and power use, including ways to use those facts to get through to students who possibly don’t care about some of the other elements. There were a honestly a lot of really trenchant questions from this group, and I was extremely glad to meet and think with them. Really hoping to do so more in the future, too.

A Black man with natural hair shaved on the sides & long in the center, grey square-frame glasses, wearing a silver grey suit jacket, a grey dress shirt with a red and black Paisley tie, and a black N95 medical mask stands on a stage behind a lectern and in front of a large screen showing a slide containing the words On Truth, Values, Knowledge,and Democracy in the Age of Generative “AI”Dr. Damien Patrick Williams Assistant Professor of Philosophy Assistant Professor of Data Science University of North Carolina at Charlotte, and an image of the same man, unmasked, with a beard, wearing a silver-grey pinstriped waistcoat & a dark grey shirt w/ a purple paisley tie in which bookshelves filled w/ books & framed degrees are visible in the background

Me at the SEAC conference; photo taken by Jason Robert (see alt text for further detailed description).

Below, you’ll find the audio, the slides, and the lightly edited transcript (so please forgive any typos and grammatical weirdnesses). All things being equal, a goodly portion of the concepts in this should also be getting worked into a longer paper coming out in 2025.

Hope you dig it.

Until Next Time.

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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 ‘”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:

Audio Player

[Direct Link to Mp3]

And the Transcript is here below the cut:

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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…

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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.

A few weeks ago I had a conversation with David McRaney of the You Are Not So Smart podcast, for his episode on Machine Bias. As he says on the blog:

Now that algorithms are everywhere, helping us to both run and make sense of the world, a strange question has emerged among artificial intelligence researchers: When is it ok to predict the future based on the past? When is it ok to be biased?

“I want a machine-learning algorithm to learn what tumors looked like in the past, and I want it to become biased toward selecting those kind of tumors in the future,” explains philosopher Shannon Vallor at Santa Clara University.  “But I don’t want a machine-learning algorithm to learn what successful engineers and doctors looked like in the past and then become biased toward selecting those kinds of people when sorting and ranking resumes.”

We talk about this,  sentencing algorithms, the notion of how to raise and teach our digital offspring, and more. You can listen to all it here:

[Direct Link to the Mp3 Here]

If and when it gets a transcript, I will update this post with a link to that.

Until Next Time.

Audio Player

[Direct link to Mp3]

My second talk for the SRI International Technology and Consciousness Workshop Series was about how nonwestern philosophies like Buddhism, Hinduism, and Daoism can help mitigate various kinds of bias in machine minds and increase compassion by allowing programmers and designers to think from within a non-zero-sum matrix of win conditions for all living beings, meaning engaging multiple tokens and types of minds, outside of the assumed human “default” of straight, white, cis, ablebodied, neurotypical male. I don’t have a transcript, yet, and I’ll update it when I make one. But for now, here are my slides and some thoughts.

A Discussion on Daoism and Machine Consciousness (Slides as PDF)

(The translations of the Daoist texts referenced in the presentation are available online: The Burton Watson translation of the Chuang Tzu and the Robert G. Hendricks translation of the Tao Te Ching.)

A zero-sum system is one in which there are finite resources, but more than that, it is one in which what one side gains, another loses. So by “A non-zero-sum matrix of win conditions” I mean a combination of all of our needs and wants and resources in such a way that everyone wins. Basically, we’re talking here about trying to figure out how to program a machine consciousness that’s a master of wu-wei and limitless compassion, or metta.

The whole week was about phenomenology and religion and magic and AI and it helped me think through some problems, like how even the framing of exercises like asking Buddhist monks to talk about the Trolley Problem will miss so much that the results are meaningless. That is, the trolley problem cases tend to assume from the outset that someone on the tracks has to die, and so they don’t take into account that an entire other mode of reasoning about sacrifice and death and “acceptable losses” would have someone throw themselves under the wheels or jam their body into the gears to try to stop it before it got that far. Again: There are entire categories of nonwestern reasoning that don’t accept zero-sum thought as anything but lazy, and which search for ways by which everyone can win, so we’ll need to learn to program for contradiction not just as a tolerated state but as an underlying component. These systems assume infinitude and non-zero-sum matrices where every being involved can win.

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