safiya noble

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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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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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