Designing an ML Auditing Criteria Catalog as Starting Point for the Development of a Framework

Abstract

Background: Although AI algorithms and applications become more and popular in the healthcare sector, only few institutions have an operational AI strategy. Identifying the best suited processes for ML algorithm implementation and adoption is a big challenge. Also, raising human confidence in AI systems is elementary to building trustworthy, socially beneficial and responsible AI. A commonly agreed AI auditing framework that provides best practices and tools could help speeding up the adoption process. In this paper, we first highlight important concepts in the field of AI auditing and then restructure and subsume them into an ML auditing core criteria catalog. Methods: We conducted a scoping study where we analyzed sources being associated with the term "Auditable AI" in a qualitative way. We utilized best practices from Mayring [1], Miles and Huberman [2] as well as Bortz and Döring [3]. Based on referrals, additional relevant white papers and sources in the field of This article has been accepted for publication in IEEE Access.