amplifying bias, attempts to detect and prevent such biases have intensified. An approach that has received considerable … contributions to understanding and addressing issues around bias in computer systems, outlines the current debates on algorithmic … bias and fairness in machine learning, and discusses how such debates could profit from VSD-derived insights and …

This paper evaluates the impact of machine learning (ML) and alternative data (AD) on ethnic/racial bias in residential … mortgage underwriting. Based on the newly enhanced Home Mortgage Disclosure Act data, we simulate data bias and AD bias and … construct multiple test scenarios by interacting the two sources of bias. The distributional impact of ML is compared with …

We derive general, yet simple, sharp bounds on the size of the omitted variable bias for a broad class of causal … how the bound on the bias depends only on the additional variation that the latent variables create both in the outcome … variables (in explaining treatment and outcome variation) are sufficient to place overall bounds on the size of the bias …