How to Fight Bias in ML Based Experiences

Forrester piece with interesting interview and links:

How To Fight Bias In ML-Based Experiences  By Andrew Hogan in Forrester Blog

Recently, I interviewed Carol Smith about AI and ethics — she’s a Senior Research Scientist at Carnegie Mellon’s Software Engineering Institute, and she told me:

“You’re bringing yourself to the projects you do at work, and we’re all biased and flawed. We must accept that building a fancy system doesn’t change that. We’re going to make mistakes and there will be issues with what we make. The more imaginative we can be early on, the more prepared we can be for failure.”

I asked her about her paper “Designing Trustworthy AI: A Human-Machine Teaming Framework to Guide Development” because Forrester has written extensively about AI and ethics in reports and blog posts like this one: “The Ethics Of AI: Don’t Build Racist Models” and highlighted the importance of diverse teams in reports like this one Data-Fueled Products: How To Thrive On The Design And Data Science Collision. Here are the highlights that stood out to me from Carol’s paper and our conversation:  ... "