Ethics, Equity, Inclusion

Stanford HAI researchers create eight new AI benchmarks that could help developers reduce bias in AI models, potentially making them fairer and less likely to case harm.

HAI believes that all researchers and funding agencies have a responsibility to mitigate potential long-term harms from their research. HAI provides select examples, on-the-ground experiences, and perspectives from devising and administering an ethical reflection process for research. While HAI believes that ethical reflection can be integrated into any grantmaking process, the exact process can and should be tailored to the needs of the institution.

New research tests large language models for consistency across diverse topics, revealing that while they handle neutral topics reliably, controversial issues lead to varied answers.

This brief explores the complexities of accounting for race in clinical algorithms for evaluating kidney disease and the implications for tackling deep-seated health inequities.

The approach paves the way for faster and more accurate compliance with California’s anti-discrimination law. 

This white paper, produced in collaboration with Black in AI, presents considerations for the Congressional Black Caucus’s policy initiatives by highlighting where AI holds the potential to deepen racial inequalities and where it can benefit Black communities.