seeBias: A Comprehensive Tool for Assessing and Visualizing AI Fairness

Fairness requires looking beyond accuracy metrics: Current AI fairness tools often only measure classification performance disparities, but seeBias demonstrates that evaluating calibration (how well predicted probabilities match actual outcomes) is equally critical—the paper shows this approach uncovers disparities that conventional metrics miss.

Comprehensive evaluation is essential for high-stakes decisions: In domains like criminal justice and healthcare where AI predictions directly affect people's lives, the paper argues that fairness assessment must integrate multiple performance domains simultaneously rather than examining them in isolation.

Visualization tools enable transparency and accountability: seeBias includes customizable visualizations designed to support transparent reporting of fairness findings, suggesting that making bias assessment results understandable to non-technical stakeholders is a key component of responsible AI implementation.

Existing tools have significant gaps: The paper identifies that most current fairness toolkits have "limited attention to other critical aspects such as calibration," indicating that organizations relying on standard fairness packages may have incomplete pictures of whether their AI systems treat different groups equitably.

Practical resources for fairness assessment are now more accessible: By providing an open-source R package (with Python version in development) and demonstrating its application on real criminal justice and healthcare datasets, the authors make comprehensive fairness evaluation more feasible for practitioners implementing responsible AI.