Counterfactual Explanations Help Identify Sources of Bias
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).
International Women's Day 2020
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.
Faithful and Customizable Explanations of Black Box Models
The authors of “Faithful and Customizable Explanations of Black Box Models” (MUSE) share a common goal with my own research: addressing the challenge of making machine learning models interpretable. Both emphasize the importance of transparency in decision-making, particularly in scenarios where human trust and understanding are critical, such as healthcare, judicial decisions, and financial assessments. Both they and I see decision rule structures as the ideal explanation format to explain model behaviour.