being susceptible to adversarial attacks (poisoning a training set, or just probing model in production and constructing adversarial inputs ).

We were provided with several famous and less-famous examples of where AI tools have failed ethical or security tests: Several other talks and workshops also had a focus on ethics, and even more talks at least mentioned ethical concerns. When I exported my first-draft schedule to my calendar, I had a number of time slots that were quadruple-booked! Data EthicsĪll three keynote talks had a significant focus on ethics in ML/AI. It also seemed like a great lineup of talks. I tried to attend talks from all the focus areas, and below I’ll try to aggregate key takeaways it’s worth noting that there were ~200 talks, so other attendees may have left with very different experiences. (There was also one pre-conference day of bootcamps and a career fair.) Talks had a variety of focus areas, which I’d categorize as: ML/DS techniques, platforms, ethics, MLOps, and business needs. The last two days focused on shorter talks. The first two days focused on a Business Summit and longer workshops/trainings. I’m here to discuss the Open Data Science Conference that several of us from Enova’s Analytics team attended. As he mentions, one of the nice learning opportunities at Enova includes conference travel. Not long ago, Robert Chen from Software Engineering wrote a blog post about the Strata Data Conference.

Strata data conference sf 2019