Algorithmic Decision

Algorithmic decision-making focuses on developing and evaluating algorithms that automate decisions across various domains, aiming to improve efficiency and fairness. Current research emphasizes mitigating biases in these algorithms, particularly through the development of novel fairness metrics and the application of techniques like causal inference and interpretable machine learning to understand and correct discriminatory outcomes. This field is crucial for ensuring equitable and transparent use of AI in high-stakes applications such as loan applications, hiring, and public policy, impacting both the scientific understanding of fairness and the ethical deployment of AI systems.