How do you start to wrap your head around some of the most fundamental issues surrounding new technology and how it impacts society? paid intel
ow do you start to wrap your head around some of the most fundamental issues surrounding new technology and how it impacts society? If you’re Jonathan Zittrain, you take this “brainstorming exercise,” as he calls it, and force it into the real world.
At the end of the 12-week cohort session, the finalized projects are presented to Assembly’s advisory board. And from there, hopefully they make it back into industry. Zittrain sums it all up succinctly, saying, “We try to blend research, engineering, theory, and policy to make sure what we generate is as grounded in reality as possible—and that it can impact that reality.
“These aren’t issues that most professionals think about on a day-to-day basis,” she says, “so we tried to create a space where people who don’t have this kind of background can start to think about it.”It turns out the intersection of AI and governance is actually a fairly broad one, and the 2018 cohort ultimately resulted in six projects being presented, all surprisingly different from one another.
Verifying that an algorithm is coded properly is part of that job, but an even bigger part is ensuring that the data on which an algorithm is trained is itself fair and unbiased. For example, if you want to train a new facial recognition algorithm, you have to find a data set full of pictures of faces on which your algorithm can practice.
The Data Nutrition Project doesn’t solve the problem of biased data sets; it merely flags them so users and other data scientists know if the data is “incomplete, misunderstood, or historically problematic,” says Chmielinski, the Data Nutrition project lead.
The photo would look untouched to the human eye, but to the computer, you might as well be guacamole. Ultimately, the goal is to encourage photo-oriented enterprises—particularly where human faces are involved—to give users the option to “equalize” their photos, thus preventing machine learning algorithms from including them in machine learning data sets.
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