Cathy O'Neil Quotes About Algorithms
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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 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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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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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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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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With recidivism algorithms, for example, I worry about racist outcomes. With personality tests [for hiring], I worry about filtering out people with mental health problems from jobs. And with a teacher value-added model algorithm [used in New York City to score teachers], I worry literally that it's not meaningful. That it's almost a random number generator.
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People are starting to be very skeptical of the Facebook algorithm and all kinds of data surveillance.
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There are lots of different ways that algorithms can go wrong, and what we have now is a system in which we assume because it's shiny new technology with a mathematical aura that it's perfect and it doesn't require further vetting. Of course, we never have that assumption with other kinds of technology.
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