Fairness, Accuracy, and Unreliable Data

TL;DR

This thesis explores how fairness, strategic classification, and robustness issues affect machine learning reliability, emphasizing the importance of aligning empirical risk minimization with real-world data complexities.

Contribution

It provides a theoretical analysis of challenges in fairness, strategic classification, and robustness, guiding better practices for reliable machine learning systems.

Findings

Identifies limitations of empirical risk minimization in complex data settings

Provides theoretical insights into fairness and robustness challenges

Suggests strategies for designing more reliable machine learning algorithms

Abstract

This thesis investigates three areas targeted at improving the reliability of machine learning; fairness in machine learning, strategic classification, and algorithmic robustness. Each of these domains has special properties or structure that can complicate learning. A theme throughout this thesis is thinking about ways in which a `plain' empirical risk minimization algorithm will be misleading or ineffective because of a mis-match between classical learning theory assumptions and specific properties of some data distribution in the wild. Theoretical understanding in eachof these domains can help guide best practices and allow for the design of effective, reliable, and robust systems.