Pragmatic auditing: a pilot-driven approach for auditing Machine Learning systems

TL;DR

This paper introduces a pragmatic, lifecycle-based auditing procedure for ML systems, emphasizing transparency and accountability, demonstrated through two real-world pilot studies, addressing current gaps in ethical ML auditing practices.

Contribution

It proposes a novel ML auditing approach based on an adapted lifecycle model and risk assessment, extending European guidelines, with practical pilot implementations.

Findings

Identified gaps in current ML auditing practices.

Validated the proposed audit procedure through real-world pilots.

Discussed future challenges and directions for ML algorithmic auditing.

Abstract

The growing adoption and deployment of Machine Learning (ML) systems came with its share of ethical incidents and societal concerns. It also unveiled the necessity to properly audit these systems in light of ethical principles. For such a novel type of algorithmic auditing to become standard practice, two main prerequisites need to be available: A lifecycle model that is tailored towards transparency and accountability, and a principled risk assessment procedure that allows the proper scoping of the audit. Aiming to make a pragmatic step towards a wider adoption of ML auditing, we present a respective procedure that extends the AI-HLEG guidelines published by the European Commission. Our audit procedure is based on an ML lifecycle model that explicitly focuses on documentation, accountability, and quality assurance; and serves as a common ground for alignment between the auditors and the audited organisation. We describe two pilots conducted on real-world use cases from two different organisations and discuss the shortcomings of ML algorithmic auditing as well as future directions thereof.