I finally got around to linking to my slides for the keynote I delivered at ICWSM. The talk was recorded and the video will eventually appear, and in the meantime, here are my slides.
I’m deeply honored that the organizers of the 10th ICWSM (The AAAI conference on weblogs and social media) have invited me to kick off the conference with an opening keynote on May 18. Here’s what I’ll be talking about.
Algorithmic Fairness: From social good to mathematical framework
Machine learning has taken over our world, in more ways than we realize. You might get book recommendations, or an efficient route to your destination, or even a winning strategy for a game of Go. But you might also be admitted to college, granted a loan, or hired for a job based on algorithmically enhanced decision-making. We believe machines are neutral arbiters: cold, calculating entities that always make the right decision, that can see patterns that our human minds can’t or won’t. But are they? Or is decision-making-by-algorithm a way to amplify, extend and make inscrutable the biases and discrimination that is prevalent in society?
To answer these questions, we need to go back — all the way to the original ideas of justice and fairness in society. We also need to go forward — towards a mathematical framework for talking about justice and fairness in machine learning. I will talk about the growing landscape of research in algorithmic fairness: how we can reason systematically about biases in algorithms, and how we can make our algorithms fair(er).