Bayesian approaches to identifying and ameliorating systematic bias in machine learning algorithms and data
Researchers have identified a number of ways in which various standard ma- chine learning approaches can produce systematic bias against underrepresented or minority groups (or more generally, against categories only present in small subsets of data). This project will look at ways in which such systematic bias can arise in Bayesian inference (a fundamental normative model behind many machine learning approaches). This project will also propose techniques for mitigating this bias, and will implement, test and validate these techniques. The aim in this project will be to produce objective measures of the degree of systematic bias produced by standard Bayesian approaches for data sets with particular characteristics, and to produce measures of the degree to which extensions of the Bayesian approach influence or mitigate against such systematic bias. The aim in this project is not just to address the origins of bias in the approximate approaches implemented in various machine-learning techniques, but to also investigate bias in general, by looking at normatively correct models of reasoning such as full Bayesian inference (models which underlie these approximate approaches).