Bias, Fairness, and Accountability with AI and ML Algorithms

By Nengfeng Zhou et al

Published on May 6, 2021

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Table of Contents

Abstract
1. Introduction
2. Scope of AI/ML Algorithms
3. Potential Sources of Bias and Discrimination
3.1 Data Bias
3.2 Algorithmic Bias

Summary

The document discusses the impact of AI and ML algorithms on bias and fairness issues. It explores the challenges and opportunities presented by these technologies in various fields such as banking, finance, criminal justice, and more. The paper delves into the types and sources of data bias, algorithmic unfairness, and the implications of bias in decision-making processes. It also provides insights into fairness metrics, de-biasing techniques, and guidelines for ensuring fairness in AI/ML models. The document highlights the importance of addressing bias and discrimination to promote accountability and fairness in the use of AI and ML algorithms.