Christo Wilson: Hi! Good evening. So I’m Christo Wilson. I’m a com­put­er sci­ence pro­fes­sor over at Northeastern. And like Karrie I con­sid­er myself to be an algo­rithm audi­tor. So what does that mean? Well, I’m inher­ent­ly a sus­pi­cious per­son. When I start inter­act­ing with a new ser­vice, or a new app, and it appears to be doing some­thing dynam­ic, I imme­di­ate­ly begin to ques­tion what is going on inside the black box, right? What is pow­er­ing these dynam­ics? And ulti­mate­ly what is the impact of this? Is it chang­ing peo­ple’s per­cep­tions? Is infor­ma­tion being hid­den? What is going on here?

So to give you a lit­tle fla­vor for how this work plays out, I want to talk about one of my favorite audits that we con­duct­ed, where we looked at surge pric­ing on Uber. Now of course, Uber is a good, upstand­ing cor­po­rate cit­i­zen, right. There’s no rea­son to sus­pect that they might be tin­ker­ing with the prices just to charge you more mon­ey. But nonethe­less, we were curi­ous how these prices got cal­cu­lat­ed. So we signed up for dozens of Uber accounts. We spoofed their GPS coor­di­nates, to place them in a grid through­out a met­ro­pol­i­tan area. And that enabled us to see all the cars that were dri­ving around that were avail­able. When those cars dis­ap­peared, that implied that they got booked and we could see all the pre­vail­ing prices.

So, the good thing that we found from this is that indeed, what we saw was that the surge prices strong­ly cor­re­lat­ed with sup­ply and demand for vehi­cles. And that match­es our expec­ta­tions for how we think these num­bers should be cal­cu­lat­ed. So that’s good.

The bad thing was that we also found that Uber was ran­dom­ly giv­ing users incor­rect prices for about sev­en months. And they did­n’t real­ize that until we got in touch with them and talked to their engi­neers.

Screenshot of Uber's transparency blog post, "Peeking Under the Hoot at Uber," which is nearly identical to the title of Wilson's research paper

Another pos­i­tive out­come of our audit was that Uber opened a trans­paren­cy blog the day our study got pub­lished. So this is not you know, full trans­paren­cy, right. It’s just crack­ing the win­dow a lit­tle bit. But some­thing is bet­ter than noth­ing. I just wish that they had cho­sen a dif­fer­ent title for their blog. You know, emu­la­tion is the sin­cer­est form of flat­tery, but…you know, what­ev­er, it’s fine, right.

So actu­al­ly what I would like to talk about in a lit­tle bit more detail is some of our ongo­ing work look­ing at Google search. It feels like social media has sort of got­ten most of the blow­back from the fake news and the mis­in­for­ma­tion deba­cle. But real­ly, Google search remains the pri­ma­ry lens through which peo­ple con­sume and locate con­tent on the Web—much more so even than Facebook to this day. So under­stand­ing how Google search works, how it presents con­tent to users, is incred­i­bly impor­tant.

One of my PhD stu­dents has been run­ning in-lab exper­i­ments where we bring peo­ple in and we show them search results that are heav­i­ly biased, right. And then we sur­vey them before and after to see if their polit­i­cal beliefs or their per­cep­tions of the can­di­dates have changed.

And the shock­ing thing is that when you show peo­ple heavily-biased search results it can have a very large impact on their per­cep­tions of the can­di­dates. Large enough to even swing peo­ple’s vot­ing posi­tions if they were sort of on the fence to begin with.

Now, this an in-lab exper­i­ment. And I’m not imply­ing that Google is doing any­thing like this. The prob­lem is that we just don’t know, right. We need to go out and mea­sure Google to see what infor­ma­tion they’re actu­al­ly pre­sent­ing to peo­ple. And that itself, though, is a chal­lenge. I can go write a scraper that runs auto­mat­ed Google search­es and col­lects data, but that’s a fun­da­men­tal­ly incom­plete pic­ture of what’s com­ing out of the sys­tem. We all know that Google search is heav­i­ly per­son­al­ized.

In this case, these results are dif­fer­ent because of loca­tion. But there’s oth­er fac­tors like what’s in your Gmail inbox; what have you searched for and clicked on late­ly; have you inter­act­ed with adver­tise­ments; that could all poten­tial­ly impact the out­put of the search engine. So to real­ly get an under­stand­ing of how this sys­tem works, the kind of information—especially polit­i­cal information—that it’s dis­play­ing to users, we need to enlist your help.

So, in the next cou­ple of months we’re going to be rolling out it’s what we call a col­lab­o­ra­tive audit. So this is a brows­er exten­sion that we’re going to try to get peo­ple to install that allows us to essen­tial­ly bor­row your cook­ies. You install it and that gives us the abil­i­ty to run search­es in your brows­er.

Now, we’re not going to be col­lect­ing your search­es, and your search his­to­ry, right. That’s a pri­va­cy vio­la­tion, and super creepy. I just want to bor­row your brows­er so I can run some polit­i­cal search­es to see what would Google have shown you giv­en the infor­ma­tion they have about you, ver­sus what they would show to me, or Nathan, or any­one else. This kind of col­lab­o­ra­tive audit gives us the abil­i­ty to get a sort of broad-ranging view of how the sys­tem func­tions in the real world, track its behav­ior over time, and ulti­mate­ly, the next time there’s some kind of crazy fake news con­tro­ver­sy, we can look ret­ro­spec­tive­ly and see how did this hap­pen on Google? Who was see­ing it? How preva­lent is it? What is going on? Thank you very much.


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