I recently watched all of Star Trek: Picard, and while I was definitely on board with the vast majority of it, and extremely pleased with certain elements of it, some things kind of bothered me.
And so, as with much of the pop culture I love, I want to spend some time with the more critical perspective, in hopes that it’ll be taken as an opportunity to make it even better.
[Promotional image for Star Trek: Picard, featuring all of the series main cast.]
(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.]
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:
And the Transcript is here below the cut:
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 my name card from the Technology & Consciousness workshop series.]
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.
[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]
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…
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
Saturday, June 9, 2018
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.]
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.
[Argentina’s Mt Tronador Casaño Overa glacier, by McKay Savage]
“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.
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.
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).