Archive (Page 1 of 6)

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.

AI Blindspot

AI Blindspot is a dis­cov­ery process for spot­ting uncon­scious bias­es and struc­tur­al inequal­i­ties in AI sys­tems.

Data & Society Databite #119: Mary L. Gray on Ghost Work

I’m just going to say it, I would like to com­plete­ly blow up employ­ment clas­si­fi­ca­tion as we know it. I do not think that defin­ing full-time work as the place where you get ben­e­fits, and part-time work as the place where you have to fight to get a full-time job, is an appro­pri­ate way of address­ing this labor mar­ket.

What Sci-Fi Futures Can (and Can’t) Teach Us About AI Policy, open­ing and clos­ing com­ments

AI Policy Futures is a research effort to explore the rela­tion­ship between sci­ence fic­tion around AI and the social imag­i­nar­ies of AI. What those social mea­sures can teach us about real tech­nol­o­gy pol­i­cy today. We seem to tell the same few sto­ries about AI, and they’re not very help­ful.

Bridging AI Fact and Fiction

This is going to be a con­ver­sa­tion about sci­ence fic­tion not just as a cul­tur­al phe­nom­e­non, or a body of work of dif­fer­ent kinds, but also as a kind of method or a tool.

Untold AI — What AI Stories Should We Be Telling Ourselves?

How peo­ple think about AI depends large­ly on how they know AI. And to the point, how the most peo­ple know AI is through sci­ence fic­tion, which sort of rais­es the ques­tion, yeah? What sto­ries are we telling our­selves about AI in sci­ence fic­tion?

How Sci-Fi Reflects Our AI Hopes and Fears

We came up with the idea to write a short paper…trying to make some sense of those many nar­ra­tives that we have around arti­fi­cial intel­li­gence and see if we could divide them up into dif­fer­ent hopes and dif­fer­ent fears.

AI in Reality

When data sci­en­tists talk about bias, we talk about quan­tifi­able bias that is a result of let’s say incom­plete or incor­rect data. And data sci­en­tists love liv­ing in that world—it’s very com­fort­able. Why? Because once it’s quan­ti­fied if you can point out the error you just fix the error. What this does not ask is should you have built the facial recog­ni­tion tech­nol­o­gy in the first place?

AI in Sci-Fi

What I hope we can do in this pan­el is have a slight­ly more lit­er­ary dis­cus­sion to try to answer well why were those the sto­ries that we were telling and what has been the point of telling those sto­ries even though they don’t now nec­es­sar­i­ly always align with the pol­i­cy prob­lems that we’re hav­ing.

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