Research Summary: Applications of Risk Science to AI Fairness Evaluation: Principles, Challenges, and Best Practices

This research explores the application of risk science principles to the evaluation of AI fairness, questioning whether current AI evaluation scholarship aligns with established risk science methodologies. The study focuses on systematically examining how AI's societal impacts, particularly risks, are understood, assessed, communicated, managed, and governed within academic discourse.

Why it matters

Understanding the alignment of AI fairness evaluation with risk science is crucial for robust risk management and ethical deployment of AI. This ensures that potential societal impacts are systematically identified, assessed, and mitigated, which is vital for maintaining public trust and regulatory compliance in AI adoption.

Key insights

• The societal impacts and risks of proliferating technologies, especially AI and algorithmic systems, are significant objects of study beyond scientific communities.
• A key open question is whether current AI evaluation scholarship, particularly concerning bias and fairness, adheres to the principles and best practices of risk science.
• Risk science provides a framework for systematically generating knowledge related to understanding, assessing, communicating, managing, and governing risk.
• The research involves a literature review to evaluate the alignment of scholarly work on AI bias and fairness with risk science principles.

Source

arXiv — Computers and Society — https://arxiv.org/abs/2608.29478