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Surveillance State of the Union

We want­ed to look at how sur­veil­lance, how these algo­rith­mic deci­sion­mak­ing sys­tems and sur­veil­lance sys­tems feed into this kind of tar­get­ing deci­sion­mak­ing. And in par­tic­u­lar what we’re going to talk about today is the role of the AI research com­mu­ni­ty. How that research ends up in the real world being used with real-world con­se­quences.

Kaleidoscope: Positionality-aware Machine Learning

Positionality is the spe­cif­ic posi­tion or per­spec­tive that an indi­vid­ual takes giv­en their past expe­ri­ences, their knowl­edge; their world­view is shaped by posi­tion­al­i­ty. It’s a unique but par­tial view of the world. And when we’re design­ing machines we’re embed­ding posi­tion­al­i­ty into those machines with all of the choic­es we’re mak­ing about what counts and what does­n’t count.

Parenting a Mind

BJ Copeland states that a strong AI machine would be one, built in the form of a man; two, have the same sen­so­ry per­cep­tion as a human; and three, go through the same edu­ca­tion and learn­ing process­es as a human child. With these three attrib­ut­es, sim­i­lar to human devel­op­ment, the mind of the machine would be born as a child and will even­tu­al­ly mature as an adult.

Artificial Intelligence is Hard to See: Social & Ethical Impacts of AI

The big con­cerns that I have about arti­fi­cial intel­li­gence are real­ly not about the Singularity, which frankly com­put­er sci­en­tists say is…if it’s pos­si­ble at all it’s hun­dreds of years away. I’m actu­al­ly much more inter­est­ed in the effects that we are see­ing of AI now.

Big Data Bodies: Machines and Algorithms in the World

I’m inter­est­ed in data and dis­crim­i­na­tion, in the things that have come to make us unique­ly who we are, how we look, where we are from, our per­son­al and demo­graph­ic iden­ti­ties, what lan­guages we speak. These things are effec­tive­ly incom­pre­hen­si­ble to machines. What is gen­er­al­ly cel­e­brat­ed as human diver­si­ty and expe­ri­ence is trans­formed by machine read­ing into some­thing absurd, some­thing that marks us as dif­fer­ent.

Ethical Machines episode 4: David J. Klein

One of the most impor­tant insights that I’ve got­ten in work­ing with biol­o­gists and ecol­o­gists is that today it’s actu­al­ly not real­ly known on a sci­en­tif­ic basis how well dif­fer­ent con­ser­va­tion inter­ven­tions will work. And it’s because we just don’t have a lot of data.

Data & Society Databite #101: Machine Learning: What’s Fair and How Do We Decide?

The ques­tion is what are we doing in the indus­try, or what is the machine learn­ing research com­mu­ni­ty doing, to com­bat instances of algo­rith­mic bias? So I think there is a cer­tain amount of good news, and it’s the good news that I want­ed to focus on in my talk today.

Ethical Machines episode 1: Mark Riedl

Computers can tell sto­ries but they’re always sto­ries that humans have input into a com­put­er, which are then just being regur­gi­tat­ed. But they don’t make sto­ries up on their own. They don’t real­ly under­stand the sto­ries that we tell. They’re not kind of aware of the cul­tur­al impor­tance of sto­ries. They can’t watch the same movies or read the same books we do. And this seems like this huge miss­ing gap between what com­put­ers can do and humans can do if you think about how impor­tant sto­ry­telling is to the human con­di­tion.

Sleepwalking into Surveillant Capitalism, Sliding into Authoritarianism

We have increas­ing­ly smart, sur­veil­lant per­sua­sion archi­tec­tures. Architectures aimed at per­suad­ing us to do some­thing. At the moment it’s click­ing on an ad. And that seems like a waste. We’re just click­ing on an ad. You know. It’s kind of a waste of our ener­gy. But increas­ing­ly it is going to be per­suad­ing us to sup­port some­thing, to think of some­thing, to imag­ine some­thing.

Artificial Intelligence: Challenges of Extended Intelligence

Machine learn­ing sys­tems that we have today have become so pow­er­ful and are being intro­duced into every­thing from self-driving cars, to pre­dic­tive polic­ing, to assist­ing judges, to pro­duc­ing your news feed on Facebook on what you ought to see. And they have a lot of soci­etal impacts. But they’re very dif­fi­cult to audit.

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