implicit bias

All posts tagged implicit bias

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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We do a lot of work and have a lot of conversations around here with people working on the social implications of technology, but some folx sometimes still don’t quite get what I mean when I say that our values get embedded in our technological systems, and that the values of most internet companies, right now, are capitalist brand engagement and marketing. To that end, I want to take a minute to talk to you about something that happened, this week and just a heads-up, this conversation is going to mention sexual assault and the sexual predatory behaviour of men toward young girls.
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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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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.

Last week, I talked to The Atlantic’s Ed Yong about new research in crowd sentiment tipping points, how it could give hope and dread for those working for social change, and how it might be used by bad actors to create/enhance already-extant sentiment-manipulation factories.

From the article:

…“You see this clump of failures below 25 percent and this clump of successes above 25 percent,” Centola says. “Mathematically, we predicted that, but seeing it in a real population was phenomenal.”

“What I think is happening at the threshold is that there’s a pretty high probability that a noncommitted actor”—a person who can be swayed in any direction—“will encounter a majority of committed minority actors, and flip to join them,” says Pamela Oliver, a sociologist at the University of Wisconsin at Madison. “There is therefore a good probability that enough non-committed actors will all flip at the same time that the whole system will flip.”

We talked about a lot, and much of it didn’t make it into the article, but one of the things that matters most about all of this is that we’re going to have to be increasingly mindful and intentional about the information we take in. We now know that we have the ability to move the needle of conversation, with not too much effort, and with this knowledge we can make progressive social change. We can use this to fight against the despair that can so easily creep into this work of spreading compassion and trying to create a world where we can all flourish.

https://upload.wikimedia.org/wikipedia/commons/thumb/6/65/Argentina_-_Mt_Tronador_Ascent_-_65_-_Casa%C3%B1o_Overa_glacier_%286834408616%29.jpg/640px-Argentina_-_Mt_Tronador_Ascent_-_65_-_Casa%C3%B1o_Overa_glacier_%286834408616%29.jpg

[Argentina’s Mt Tronador Casaño Overa glacier, by McKay Savage]

But we have to know that there will also be those who see this as a target number to hit so that they might better disrupt and destabilize groups and beliefs. We already know that many such people are hard at work, trying to sow doubt and mistrust. We already have evidence that these actors will make other people’s lives unpleasant for the sake of it. With this new research, they’ll be encouraged, as well. As I said to Ed Yong:

“There are already a number of people out there who are gaming group dynamics in careful ways… If they know what target numbers they have to hit, it’s easy to see how they could take this information and create [or increase the output of the existing] sentiment-manipulation factory.”

The infiltration of progressive groups to move them toward chaos and internal strife is not news, just like the infiltration (and origin) of police and military groups by white supremacists is not news.

And so, while I don’t want to add to a world in which people feel like they have to continually mistrust each other, we do have to be intentional about the work we do, and how we do it, and we have to be mindful of who is trying to get us to believe what, and why they want us to believe it. Especially if we want to get others to believe and value as we do.

This research gives us a useful set of tools and a good to place to start.

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.

[Direct Link to Mp3]

Above is the (heavily edited) audio of my final talk for the SRI Technology and Consciousness Workshop Series. The names and voices of other participants have been removed in accordance with the Chatham House Rule.

Below you’ll find the slide deck for my presentation, and below the cut you’ll find the Outline and my notes. For now, this will have to stand in for a transcript, but if you’ve been following the Technoccult Newsletter or the Patreon, then some of this will be strikingly familiar.

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