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Forbidden Research: Why We Can’t Do That

Quite often when we’re ask­ing the­se dif­fi­cult ques­tions we’re ask­ing about ques­tions where we might not even know how to ask where the line is. But in oth­er cas­es, when researchers work to advance pub­lic knowl­edge, even on uncon­tro­ver­sial top­ics, we can still find our­selves for­bid­den from doing the research or dis­sem­i­nat­ing the research.

Applying Algorithms to Minimize Risk

The United States plants more than 170 mil­lion acres of corn and soy­beans a year, more than any coun­try in the world. And the pri­ma­ry mech­a­nism in the US that we use to sub­si­dize agri­cul­ture is actu­al­ly called the Federal Crop Insurance Program. So, the crop insur­ance pro­gram in the US is also the largest such pro­gram glob­al­ly, with over $100 bil­lion in lia­bil­i­ties annu­al­ly. So it’s a very big pro­gram.

Who and What Will Get to Think the Future?

There’s already a kind of cog­ni­tive invest­ment that we make, you know. At a cer­tain point, you have years of your per­son­al his­to­ry liv­ing in somebody’s cloud. And that goes beyond mere­ly being a mem­o­ry bank, it’s also a cog­ni­tive bank in some way.

What Our Algorithms Will Know in 2100

A lot of the sci­ence fic­tion I love the most is not about the­se big ques­tions. You read a book like The Diamond Age and the most inter­est­ing thing in The Diamond Age is the medi­a­tron­ic chop­sticks, the small detail that Stephenson says okay, well if you have nan­otech­nol­o­gy, peo­ple are going to use this tech­nol­o­gy in the most pedes­tri­an, kind of ordi­nary ways.

What Should We Know About Algorithms?

When I go talk about this, the thing that I tell peo­ple is that I’m not wor­ried about algo­rithms tak­ing over human­i­ty, because they kind of suck at a lot of things, right. And we’re real­ly not that good at a lot of things we do. But there are things that we’re good at. And so the exam­ple that I like to give is Amazon rec­om­mender sys­tems. You all run into this on Netflix or Amazon, where they rec­om­mend stuff to you. And those algo­rithms are actu­al­ly very sim­i­lar to a lot of the sophis­ti­cat­ed arti­fi­cial intel­li­gence we see now. It’s the same under­neath.

What Do Algorithms Know?

The Tyranny of Algorithms is obvi­ous­ly a polem­i­cal title to start a con­ver­sa­tion around com­pu­ta­tion and cul­ture. But I think that it helps us get into the cul­tur­al, the polit­i­cal, the legal, the eth­i­cal dimen­sions of code. Because we so often think of code, and code is so often con­struct­ed, in a pure­ly tech­ni­cal frame­work, by peo­ple who see them­selves as solv­ing tech­ni­cal prob­lems.

Automation and Algorithms in the Digital Age

I want to think more broad­ly about the future of cyber state, and think about accu­mu­la­tions of pow­er both cen­tral­ized and dis­trib­ut­ed that might require trans­paren­cy in bound­aries we wouldn’t be used to.

Safiya Noble at Biased Data

I often try to tell peo­ple that Google is not pro­vid­ing infor­ma­tion retrieval algo­rithms, it’s pro­vid­ing adver­tis­ing algo­rithms. And that is a very impor­tant dis­tinc­tion when we think about what kind of infor­ma­tion is avail­able in the­se corporate-controlled spaces.

Four Trends for the Digital World

This quote’s from Andy Warhol. He was look­ing at America and say­ing America’s dif­fer­ent. He’s say­ing, Well, Elizabeth Taylor’s drink­ing Coke and I’m drink­ing Coke and the bum on the street’s drink­ing Coke, and it’s all the same thing.” For the first time in his­to­ry, mass mar­ket cul­ture has allowed us all to enjoy the same thing. This is not cham­pag­ne. The bum on the street can’t afford cham­pag­ne.

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