Broussard has also recently recovered from breast cancer, and after reading the high-quality print of her electronic medical records, she realized that an AI had played a component in her diagnosis—something that’s increasingly common. That discovery led her to run her own experiment to learn more about how good AI was at cancer diagnostics.

We sat right down to speak about what she discovered, in addition to the issues with the usage of technology by police, the boundaries of “AI fairness,” and the solutions she sees for a number of the challenges AI is posing. The conversation has been edited for clarity and length.

I used to be struck by a private story you share within the book about AI as a part of your individual cancer diagnosis. Are you able to tell our readers what you probably did and what you learned from that have?

Initially of the pandemic, I used to be diagnosed with breast cancer. I used to be not only stuck inside since the world was shut down; I used to be also stuck inside because I had major surgery. As I used to be poking through my chart sooner or later, I noticed that one in all my scans said, . I assumed, No one had mentioned this to me. It was just in some obscure a part of my electronic medical record. I got really interested in the cutting-edge in AI-based cancer detection, so I devised an experiment to see if I could replicate my results. I took my very own mammograms and ran them through an open-source AI in an effort to see if it might detect my cancer. What I discovered was that I had a variety of misconceptions about how AI in cancer diagnosis works, which I explore within the book.

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One among the things I spotted, as a cancer patient, was that the doctors and nurses and health-care employees who supported me in my diagnosis and recovery were so amazing and so crucial. I don’t need a type of sterile, computational future where you go and get your mammogram done after which a bit red box will say . That’s not actually a future anybody wants once we’re talking a few life-threatening illness, but there aren’t that many AI researchers on the market who’ve their very own mammograms. 

You sometimes hear that when AI bias is sufficiently “fixed,” the technology may be way more ubiquitous. You write that this argument is problematic. Why? 

One among the massive issues I even have with this argument is this concept that someway AI goes to achieve its full potential, and that that’s the goal that everyone should strive for. AI is just math. I don’t think that every thing on the earth ought to be governed by math. Computers are really good at solving mathematical issues. But they aren’t excellent at solving social issues, yet they’re being applied to social problems. This sort of imagined endgame of isn’t a future that I cosign on.

You furthermore may write about facial recognition. I recently heard an argument that the movement to ban facial recognition (especially in policing) discourages efforts to make the technology more fair or more accurate. What do you consider that?

I definitely fall within the camp of people that don’t support using facial recognition in policing. I understand that’s discouraging to individuals who really need to make use of it, but one in all the things that I did while researching the book is a deep dive into the history of technology in policing, and what I discovered was not encouraging. 

I began with the wonderful book by [NYU professor of Media, Culture, and Communication] Charlton McIlwain and he writes about IBM wanting to sell a variety of their recent computers at the identical time that we had the so-called War on Poverty within the Nineteen Sixties. We had individuals who really desired to sell machines looking around for an issue to use them to, but they didn’t understand the social problem. Fast-forward to today—we’re still living with the disastrous consequences of the choices that were made back then.