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.

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.

The societal impacts and risks of proliferating technologies, especially AI and algorithmic systems, are significant objects of study beyond scientific communities.

Forward consideration, not a verified fact.