Sociotechnical Simulation Study Source Data

In this work, we present a simulation-based approach to explore how statistical definitions of algorithmic fairness translate to fairness in long-term outcomes, using AI-assisted breast cancer screening as a case example.

We evaluate four fairness criteria and their impact on mortality rates and socioeconomic disparities, while also considering how radiologists’ reliance on AI and patients’ access to healthcare affect outcomes. Our results highlight how algorithmic fairness does not directly translate into fair and equitable outcomes, underscoring the importance of integrating sociotechnical perspectives in order to gain a holistic understanding of fairness in AI.