Today I start teaching introductory computer science at Rhodes College. It’s the first course in the computer science major sequence, but it is also a recommended elective for non-majors. I’ve been told to expect non-majors to make up as much as three-quarters of the class. It goes without saying that Read more
An AI made a decision. You want to know why it decided the way it did. Seems reasonable. Well, reasonable depending upon the AI technique being used. Some are more transparent than others. First, we need to know what we’re asking for: interpretability or explainability. Both seek to understand the Read more
Last week in the Reservoir of Ideas, I discussed bias in generative models and the impact of the data that’s fed to them. As luck would have it, a couple days later, a colleague reposted a TikTok video of a woman reading the ChatGPT analysis of HR-1, a.k.a. “The Big Beautiful Read more
One of the most significant areas of Data Ethics concern is bias in generative AI models. Concerns about algorithmic bias are not new. Associative model (i.e. neural network) bias is not new. Even the ceding of consequential decisions to these models is not new. Just a few examples: AI is Read more
In the holiday classic, It’s the Great Pumpkin, Charlie Brown, and earlier in the October 25, 1961 Peanuts comic strip, Linus Van Pelt famously says: There are three things I’ve learned never to discuss with people: religion, politics, and the Great Pumpkin. Unfortunately, it’s hard to talk about Data Ethics Read more
In a 1993 Seinfeld episode, Jerry describes the psychological leverage that doctors use on their patients. They tell you to go into the little room and take your pants off and wait. Then, “anybody that comes in with pants on seems like they know what they’re talking about. In any Read more