New HuffPost Blog: Jennifer Chayes on How Machine Learning Advances Will Improve the Fairness of Algorithms
August 24, 2017
ACM contributes a monthly blog on current issues in technology and society to HuffPost. Written by ACM members and guest authors, these blogs offer unique insights of computing professionals and are intended to enhance public understanding of technology issues. ACM bloggers also explore the ways in which computing and information technologies impact how we interact with one another and how these forces are reshaping the basic frameworks of society.
In her August 23 post How Machine Learning Advances Will Improve the Fairness of Algorithms, Microsoft Research Distinguished Scientist and ACM Fellow Jennifer T. Chayes addresses bias in algorithms. She argues that with careful algorithm design, computers can be fairer than typical human decision makers: “Just as we teach our children that anyone has the potential to have any job despite who they see working in those jobs, we can teach intelligent algorithms how to disregard … discriminatory biases in their training data.”
Chayes points out that many computer scientists care deeply about the fairness of machine learning algorithms. She notes that a group of researchers at Microsoft Research and Harvard University recently designed an intelligent algorithm that looks directly at “protected attributes” like race or gender, and produces decisions that are sometimes less biased than human judgements.
Read all ACM contributors' posts on ACM's HuffPost Blog page.
