alondra nelson

All posts tagged alondra nelson

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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