Algorithm Auditing

Algorithm auditing aims to assess the fairness, reliability, and ethical implications of algorithms, particularly in high-stakes applications like fraud detection and hiring. Current research focuses on developing robust auditing methodologies, including correspondence experiments and active learning techniques, to evaluate various fairness metrics and identify biases across different subgroups. These efforts are crucial for ensuring algorithmic accountability and mitigating potential harms stemming from biased or unreliable AI systems, impacting both the development of fairer algorithms and the legal and regulatory landscape surrounding their deployment.