Counterfactual Explanations Help Identify Sources of Bias

Sun, 08 Mar 2020 00:00:00 +0000

By the end of 2020, the topic of eXplainable Artificial Intelligence (XAI) has become quite mainstream. One important developlment is counterfactual explanations, which (among other benefits) can to identify and reduce bias in machine learning models. Counterfactual explanations provide insights by showing how minimal changes in input features can alter model predictions. This approach has been crucial in exposing biased behavior, especially in sensitive applications like credit scoring or hiring. By identifying how protected attributes (e.g., gender or race) affect outcomes, practitioners could better address and mitigate unfair biases in AI systems (Verma et al., 2020).

Reference: Verma, S., & Rubin, J. (2020). Fairness Definitions Explained. Proceedings of the 2020 ACM/IEEE International Workshop on Software Fairness.

International Women's Day 2020

Sun, 08 Mar 2020 00:00:00 +0000

Profile: Cynthia Rudin #

Today, for International Women’s Day, I wanted to share my huge respect for Cynthia Rudin. She is a leading academic in the research field in which I am currently involved for my PhD - interpretability in machine learning. Her work is very widely cited and comes up in all searches related to solving the “black box” problem of machine learning. She is a true thought leader.

Here is excellent podcast where Cynthia succinctly and eruditely explains the current state, problems and solutions of ML interpretability. It’s well worth a listen and, I believe, entirely understandable by non-expert listeners.

Here are a couple of profile sites where you can find out more about this brilliant and accomplished woman who is a true thought leader in Machine Learning and Data Science.

https://en.wikipedia.org/wiki/Cynthia_Rudin

https://users.cs.duke.edu/~cynthia/

Whenever I can - and not only on IWD - I try to raise the profile of prominent women in STEM (science, technology, engineering and mathematics). Under-representation and a lack of visible role-models is one barrier to increasing diversity and equality in any field. Certainly, there is no lack of talented women in STEM but if young women and girls at school aren’t given examples of successful women, this can limit aspirations at the grass roots level.

Interpretability on XAI Today

Counterfactual Explanations Help Identify Sources of Bias

International Women's Day 2020

Profile: Cynthia Rudin #