Expeditions in Experiential AI Series: Just Machine Learning

Seminar series hosted by the Institute for Experiential AI: Expeditions in Experiential AI and the Distinguished Lecturer Series and organized by Dr. Ricardo Baeza-Yates


Risk assessment is a popular task when machine learning is used for automated decision making. For example, Jack’s risk of defaulting on a loan is 8, Jill’s is 2; Ed’s risk of recidivism is 9, Peter’s is 1. We know that this task definition comes with impossibility results for group fairness, where one cannot simultaneously satisfy desirable probabilistic measures of fairness. I will highlight recent findings in terms of these impossibility results. Next, I will present work on how machine learning can be used to generate aspirational data (i.e., data that are free from biases of real-world data). Such data are useful for recognizing and detecting sources of unfairness in machine learning models besides biased data. Time-permitting, I will discuss steps in measuring our algorithmically infused societies.