Detecting discriminatory risk through data annotation based on Bayesian inferences

TL;DR

This paper introduces a Bayesian inference-based data annotation method to identify and warn about potential racial discrimination risks in datasets used for machine learning, emphasizing the importance of sampling practices.

Contribution

The paper presents a novel Bayesian inference approach for data annotation that highlights sampling biases and discrimination risks, addressing a gap in ethical data collection practices.

Findings

Effective in detecting racial discrimination risks in datasets

Provides insights into sampling biases affecting model fairness

Applicable to multiple datasets for bias assessment

Abstract

Thanks to the increasing growth of computational power and data availability, the research in machine learning has advanced with tremendous rapidity. Nowadays, the majority of automatic decision making systems are based on data. However, it is well known that machine learning systems can present problematic results if they are built on partial or incomplete data. In fact, in recent years several studies have found a convergence of issues related to the ethics and transparency of these systems in the process of data collection and how they are recorded. Although the process of rigorous data collection and analysis is fundamental in the model design, this step is still largely overlooked by the machine learning community. For this reason, we propose a method of data annotation based on Bayesian statistical inference that aims to warn about the risk of discriminatory results of a given data set. In particular, our method aims to deepen knowledge and promote awareness about the sampling practices employed to create the training set, highlighting that the probability of success or failure conditioned to a minority membership is given by the structure of the data available. We empirically test our system on three datasets commonly accessed by the machine learning community and we investigate the risk of racial discrimination.