Cathy O'Neil Quotes
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Occupy provided me a lens through which to see systemic discrimination.
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The most important goal I had in mind was to convince people to stop blindly trusting algorithms and assuming that they are inherently fair and objective.
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The disconnect I was experiencing was that people hated Wall Street, but they loved tech.
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People felt like they were friends with Google, and they believed in the "Do No Evil" thing that Google said. They trusted Google more than they trusted the government, and I never understood that.
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The NSA buys data from private companies, so the private companies are the source of all this stuff.
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Evidence of harm is hard to come by.
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I set up a company, an algorithmic auditing company myself. I have no clients.
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My fantasy is that there is a new regulatory body that is in charge of algorithmic auditing.
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Because of my experience in Occupy, instead of asking the question, "Who will benefit from this system I'm implementing with the data?" I started to ask the question, "What will happen to the most vulnerable?" Or "Who is going to lose under this system? How will this affect the worst-off person?" Which is a very different question from "How does this improve certain people's lives?"
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The Facebook algorithm designers chose to let us see what our friends are talking about. They chose to show us, in some sense, more of the same. And that is the design decision that they could have decided differently. They could have said, "We're going to show you stuff that you've probably never seen before." I think they probably optimized their algorithm to make the most amount of money, and that probably meant showing people stuff that they already sort of agreed with, or were more likely to agree with.
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I think what's happened is that the general public has become much more aware of the destructive power of Wall Street.
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That's what we do when we work in Silicon Valley tech startups: We think about who's going to benefit from this. That's almost the only thing we think about.
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So much of our society as a whole is gearing us to maximize our salary or bonus. Basically, we just think in terms of money. Or, if not money, then, if you're in academia, it's prestige. It's a different kind of currency. And there's this unmeasured dimension of all jobs, which is whether it's improving the world.
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Especially from my experience as a quant in a hedge fund - I naively went in there thinking that I would be making the market more efficient and then was like, oh my God, I'm part of this terrible system that is blowing up the world's economy, and I don't want to be a part of that.
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There's less of a connection for a lot of people between the technical decisions we make and the ethical ramifications we are responsible for.
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We don't let a car company just throw out a car and start driving it around without checking that the wheels are fastened on. We know that would result in death; but for some reason we have no hesitation at throwing out some algorithms untested and unmonitored even when they're making very important life-and-death decisions.
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The public trusts big data way too much.
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The training one receives when one becomes a technician, like a data scientist - we get trained in mathematics or computer science or statistics - is entirely separated from a discussion of ethics.
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I think big data companies only like good news. So I think they're just hoping that they don't get sued, essentially.
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There might never be that moment when everyone says, "Oh my God, big data is awful."
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I don't think anybody's ever notified that they were sentenced to an extra two years because their recidivism score had been high, or notified that this beat cop happened to be in their neighborhood checking people's pockets for pot because of a predictive policing algorithm. That's just not how it works.
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I would argue that one of the major problems with our blind trust in algorithms is that we can propagate discriminatory patterns without acknowledging any kind of intent.
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Obviously the more transparency we have as auditors, the more we can get, but the main goal is to understand important characteristics about a black box algorithm without necessarily having to understand every single granular detail of the algorithm.
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When I think about whether I want to take a job, I don't just think about whether it's technically interesting, although I do consider that. I also consider the question of whether it's good for the world.
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I know how models are built, because I build them myself, so I know that I'm embedding my values into every single algorithm I create and I am projecting my agenda onto those algorithms.
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You'll never be able to really measure anything, right? Including teachers.
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Most people don't have any association in their minds with what they do and with ethics. They think they somehow moved past the questions of morality or values or ethics, and that's something that I've never imagined to be true.
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Google is so big you have no idea what a given person does.
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We can't just throw something out there and assume it works just because it has math in it.
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Micro-targeting is the ability for a campaign to profile you, to know much more about you than you know about it, and then to choose exactly what to show you.
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