A failure at Stanford teaches us the limits of medical algorithms
This story was co-authored by Dr. Rumman Chowdhury, CEO of Parity, an enterprise algorithmic audit platform company.
“The algorithm did it” has become a popular defense for powerful entities who turn to math to make complex moral choices. It’s an excuse that recalls a time when the public was content to understand computer code as somehow objective. But the past few years have demonstrated conclusively that technology is not neutral and instead reflects the values of those who design it, and it is fraught with all the usual shortcomings and oversights that humans suffer in our daily lives.
by Liz O’Sullivan and Susannah Shattuck
Last week, hundreds of Google workers shocked the world by announcing their unionizing efforts to the public. To those on the outside looking in, the fact that Google employees are organizing to make demands might be surprising — what could they possibly want, with their high salaries, free meals, and cushy perks? To many of us in the industry, however, the Alphabet Workers’ Union is just one part of a long-overdue reckoning for tech — I quit my job at AI company Clarifai almost two years ago for some of the same reasons that…
How algorithmic fairness applies to recommendation systems beyond the classic “binary classifier” case.
As we turn to digital sources for news and entertainment, recommendation engines are increasingly influencing the daily experience of life, especially in a world where folks are encouraged to stay indoors. These systems are not just responsible for suggesting what we read or watch for fun, but also for doling out news and political content, and for surfacing potential connections with other people online. When we talk about bias in AI systems, we often read about unintentional discrimination in ways that apply only to simple binary classifiers (e.g. in the question “Should we let this prisoner out on parole?”, there…
We recently sat down with Arthur’s latest research fellow, Sahil Verma to talk about his work both inside and outside of the company.
Can you tell us a little bit about your background?
I’m a first year PhD student at the Paul G. Allen School of Science and Engineering at UW, Seattle — I know that’s a long name! Before joining my grad school program I worked on a couple of things as an undergrad and one of the areas was fairness in machine learning. I published my first paper in this area in 2018 (“Fairness Definitions Explained”) expounding on…
I hardly recognize the person I was a year ago. Around this time in 2018, I was working for a little computer vision company that had really big problems. There was a lot of quiet dissent and discontent, hardly any executive transparency, and a whole lot of sudden, unexpected change. I had just been promoted in October — shedding all of the customer-facing responsibilities I had amassed in exchange for managing the company’s data needs.
Things had been changing for a while, but we were frogs slowly boiled. It was hard for me to notice the trend at the time…
The UK’s Data Protection Authority just issued much-anticipated guidance that clarifies the complicated issue of the GDPR’s ‘right to explanation’. Here is some background on the issue and what the new information means.
Ever since the enactment of the GDPR, many in the AI industry have been quietly worrying about the enforcement of some of the policy’s lesser-known provisions. EU governments have been hard at work extracting massive fines from businesses left and right for their privacy practices, where lawmakers have thus far earned more than €335 million from companies within the last two years. The majority of fines have come from the abuse and accidental exposure of personal information.
Many questions around the enforcement of the GDPR remain. One in particular, which could result in fines as high as 4% of a…
Liz O'Sullivan
AI Activist, Pragmatic Pacifist, & Lover of Tinfoil Hats. VP Arthur AI (arthur.ai); Technology Director STOP (stopspying.org); Member ICRAC (.net). Views mine.
