artificial intelligence

All posts tagged artificial intelligence

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.

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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Much of my research deals with the ways in which bodies are disciplined and how they go about resisting that discipline. In this piece, adapted from one of the answers to my PhD preliminary exams written and defended two months ago, I “name the disciplinary strategies that are used to control bodies and discuss the ways that bodies resist those strategies.” Additionally, I address how strategies of embodied control and resistance have changed over time, and how identifying and existing as a cyborg and/or an artificial intelligence can be understood as a strategy of control, resistance, or both.

In Jan Golinski’s Making Natural Knowledge, he spends some time discussing the different understandings of the word “discipline” and the role their transformations have played in the definition and transmission of knowledge as both artifacts and culture. In particular, he uses the space in section three of chapter two to discuss the role Foucault has played in historical understandings of knowledge, categorization, and disciplinarity. Using Foucault’s work in Discipline and Punish, we can draw an explicit connection between the various meanings “discipline” and ways that bodies are individually, culturally, and socially conditioned to fit particular modes of behavior, and the specific ways marginalized peoples are disciplined, relating to their various embodiments.

This will demonstrate how modes of observation and surveillance lead to certain types of embodiments being deemed “illegal” or otherwise unacceptable and thus further believed to be in need of methodologies of entrainment, correction, or reform in the form of psychological and physical torture, carceral punishment, and other means of institutionalization.

Locust, “Master and Servant (Depeche Mode Cover)”

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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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Kirsten and I spent the week between the 17th and the 21st of September with 18 other utterly amazing people having Chatham House Rule-governed conversations about the Future of Artificial Intelligence.

We were in Norway, in the Juvet Landscape Hotel, which is where they filmed a lot of the movie Ex Machina, and it is even more gorgeous in person. None of the rooms shown in the film share a single building space. It’s astounding as a place of both striking architectural sensibility and also natural integration as they built every structure in the winter to allow the dormancy cycles of the plants and animals to dictate when and where they could build, rather than cutting anything down.

And on our first full day here, Two Ravens flew directly over my and Kirsten’s heads.

Yes.

[Image of a rainbow rising over a bend in a river across a patchy overcast sky, with the river going between two outcropping boulders, trees in the foreground and on either bank and stretching off into the distance, and absolutely enormous mountains in the background]

I am extraordinarily grateful to Andy Budd and the other members of the Clear Left team for organizing this, and to Cennydd Bowles for opening the space for me to be able to attend, and being so forcefully enthused about the prospect of my attending that he came to me with a full set of strategies in hand to get me to this place. That kind of having someone in your corner means the world for a whole host of personal reasons, but also more general psychological and socially important ones, as well.

I am a fortunate person. I am a person who has friends and resources and a bloody-minded stubbornness that means that when I determine to do something, it will more likely than not get fucking done, for good or ill.

I am a person who has been given opportunities to be in places many people will never get to see, and have conversations with people who are often considered legends in their fields, and start projects that could very well alter the shape of the world on a massive scale.

Yeah, that’s a bit of a grandiose statement, but you’re here reading this, and so you know where I’ve been and what I’ve done.

I am a person who tries to pay forward what I have been given and to create as many spaces for people to have the opportunities that I have been able to have.

I am not a monetarily wealthy person, measured against my society, but my wealth and fortune are things that strike me still and make me take stock of it all and what it can mean and do, all over again, at least once a week, if not once a day, as I sit in tension with who I am, how the world perceives me, and what amazing and ridiculous things I have had, been given, and created the space to do, because and in violent spite of it all.

So when I and others come together and say we’re going to have to talk about how intersectional oppression and the lived experiences of marginalized peoples affect, effect, and are affected and effected BY the wider techoscientific/sociotechnical/sociopolitical/socioeconomic world and what that means for how we design, build, train, rear, and regard machine minds, then we are going to have to talk about how intersectional oppression and the lived experiences of marginalized peoples affect, effect, and are affected and effected by the wider techoscientific/sociotechnical/sociopolitical/socioeconomic world and what that means for how we design, build, train, rear, and regard machine minds.

So let’s talk about what that means.

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Previously, I told you about The Human Futures and Intelligent Machines Summit at Virginia Tech, and now that it’s over, I wanted to go ahead and put the full rundown of the events all in one place.

The goals for this summit were to start looking at the ways in which issues of algorithms, intelligent machine systems, human biotech, religion, surveillance, and more will intersect and affect us in the social, academic, political spheres. The big challenge in all of this was seen as getting better at dealing with this in the university and public policy sectors, in America, rather than the seeming worse we’ve gotten, so far.

Here’s the schedule. Full notes, below the cut.

Friday, June 8, 2018

  • Josh Brown on “the distinction between passive and active AI.”
  • Daylan Dufelmeier on “the potential ramifications of using advanced computing in the criminal justice arena…”
  • Mario Khreiche on the effects of automation, Amazon’s Mechanical Turk, and the Microlabor market.
  • Aaron Nicholson on how technological systems are used to support human social outcomes, specifically through the lens of policing  in the city of Atlanta
  • Ralph Hall on “the challenges society will face if current employment and income trends persist into the future.”
  • Jacob Thebault-Spieker on “how pro-urban and pro-wealth biases manifest in online systems, and  how this likely influences the ‘education’ of AI systems.”
  • Hani Awni on the sociopolitical of excluding ‘relational’ knowledge from AI systems.

Saturday, June 9, 2018

  • Chelsea Frazier on rethinking our understandings of race, biocentrism, and intelligence in relation to planetary sustainability and in the face of increasingly rapid technological advancement.
  • Ras Michael Brown on using the religions technologies of West Africa and the West African Diaspora to reframe how we think about “hybrid humanity.”
  • Damien Williams on how best to use interdisciplinary frameworks in the creation of machine intelligence and human biotechnological interventions.
  • Sara Mattingly-Jordan on the implications of the current global landscape in AI ethics regulation.
  • Kent Myers on several ways in which the intelligence community is engaging with human aspects of AI, from surveillance to sentiment analysis.
  • Emma Stamm on the idea that datafication of the self and what about us might be uncomputable.
  • Joshua Earle on “Morphological Freedom.”

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I talked with Hewlett Packard Enterprise’s Curt Hopkins, for their article “4 obstacles to ethical AI (and how to address them).” We spoke about the kinds of specific tools and techniques by which people who populate or manage artificial intelligence design teams can incorporate expertise from the humanities and social sciences. We also talked about compelling reasons why they should do this, other than the fact that they’re just, y’know, very good ideas.

From the Article:

To “bracket out” bias, Williams says, “I have to recognize how I create systems and code my understanding of the world.” That means making an effort early on to pay attention to the data entered. The more diverse the group, the less likely an AI system is to reinforce shared bias. Those issues go beyond gender and race; they also encompass what you studied, the economic group you come from, your religious background, all of your experiences.

That becomes another reason to diversify the technical staff, says Williams. This is not merely an ethical act. The business strategy may produce more profit because the end result may be a more effective AI. “The best system is the one that best reflects the wide range of lived experiences and knowledge in the world,” he says.

[Image of two blank, white, eyeless faces, partially overlapping each other.]

To be clear, this is an instance in which I tried to find capitalist reasons that would convince capitalist people to do the right thing. To that end, you should imagine that all of my sentences start with “Well if we’re going to continue to be stuck with global capitalism until we work to dismantle it…” Because they basically all did.

I get how folx might think that framing would be a bit of a buzzkill for a tech industry audience, but I do want to highlight and stress something: Many of the ethical problems we’re concerned with mitigating or ameliorating are direct products of the capitalist system in which we are making these choices and building these technologies.

All of that being said, I’m not the only person there with something interesting to say, and you should go check out the rest of my and other people’s comments.

Until Next Time.

This weekend, Virginia Tech’s Center for the Humanities is hosting The Human Futures and Intelligent Machines Summit, and there is a link for the video cast of the events. You’ll need to Download and install Zoom, but it should be pretty straightforward, other than that.

You’ll find the full Schedule, below the cut.

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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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