A Tool for Data Scientists, Decision Makers, and Journalists to Assess Fairness in ML Models

Explore a powerful tool for assessing fairness in machine learning models, designed for data scientists, decision-makers, and journalists. Delve into the concept of recourse in classification models used for critical decisions such as loan approvals, job offers, and insurance provisions. Learn how to empower individuals to understand and potentially change model decisions that affect their lives. Discover a new auditing method for linear classification models that focuses on actionable input variables, promoting transparency and agency in AI-driven decision-making processes. Gain insights into the ethical implications of machine learning applications and the growing need for explainable AI. Examine the challenges of demographic differences and confounding factors in model fairness. Equip yourself with knowledge to create more equitable and transparent machine learning models that respect individual agency and promote fairness in high-stakes decisions.