Tutorial: Using Harms and Benefits to Ground Practical AI Fairness Assessments in Finance

Gradient’s Chief Practitioner Lachlan McCalman and Principal Researcher Dan Steinberg presented a tutorial, together with our colleagues from Element AI, at the ACM Fairness Accountability and Transparency Conference (FAccT 2021) on 4 March 2021. You can watch a video recording of the full tutorial below.

This tutorial introduces one of the first practical algorithmic fairness assessment methodologies to be implemented (as guidance) by a major regulator. The methodology is a process for judging an AI system’s alignment with the Monetary Authority of Singapore’s “FEAT” (Fairness, Ethics, Accountability, Transparency) Principles, which promote responsible adoption of AI in Singapore’s finance sector. The presenters, along with a larger group, have spent the last year developing this methodology in close collaboration with the Monetary Authority of Singapore (MAS) and Financial Services Institutions (FSIs), including applying it to in-production systems within two large banks. The tutorial aims to introduce the audience to:

  • the fairness assessment methodology itself
  • two illustrative case-studies the authors conducted with FSI partners assessing AI systems for credit scoring and customer marketing
  • how the authors approached designing a methodology that is broadly applicable to different systems and organisations, but still practical to implement
  • the operational requirements and challenges for FSIs implementing the methodology
  • the work done to tailor the methodology to the unique Singaporean legal and societal context.