Fairness, Transparency, and Accountability encompasses PAI’s large body of research and programming around algorithmic fairness, explainability, criminal justice, and diversity and inclusion. In 2020 alone, this work examined the challenges organizations face when they seek to measure and mitigate algorithmic bias using demographic data, provide meaningful explanations to diverse stakeholders, address bias in recidivism risk assessment tools, and build more inclusive AI teams.

With ABOUT ML, PAI is leading a multistakeholder effort to develop guidelines for the documentation of machine learning systems, setting new industry norms for transparency in AI. This means not just identifying the necessary components of transparency, but releasing actionable resources to help organizations operationalize transparency at scale. Developed through an iterative, multistakeholder process, these resources pool the collective efforts and insights of academic researchers, industry practitioners, civil society organizations, and the impacted public.