Reviewable Automated Decision-Making: A Framework for Accountable Algorithmic Systems

ACM Conference on Fairness, Accountability, and Transparency (FAccT ‘21), March 1–10, 2021, Virtual Event, Canada

12 Pages Posted: 12 Feb 2021

See all articles by Jennifer Cobbe

Jennifer Cobbe

University of Cambridge - Computer Laboratory

Michelle Seng Ah Lee

University of Cambridge

Jatinder Singh

University of Cambridge -- Dept. Computer Science & Technology (Computer Laboratory)

Date Written: December 17, 2020

Abstract

This paper introduces reviewability as a framework for improving the accountability of automated and algorithmic decision-making (ADM) involving machine learning. We draw on an understanding of ADM as a socio-technical process involving both human and technical elements, beginning before a decision is made and extending beyond the decision itself. While explanations and other model-centric mechanisms may assist some accountability concerns, they often provide insufficient information of these broader ADM processes for regulatory oversight and assessments of legal compliance. Reviewability involves breaking down the ADM process into technical and organisational elements to provide a systematic framework for determining the contextually appropriate record-keeping mechanisms to facilitate meaningful review - both of individual decisions and of the process as a whole. We argue that a reviewability framework, drawing on administrative law's approach to reviewing human decision-making, offers a practical way forward towards more a more holistic and legally-relevant form of accountability for ADM.

Keywords: Algorithmic systems, automated decision-making, accountability, audit, artificial intelligence, machine learning

Suggested Citation

Cobbe, Jennifer and Lee, Michelle Seng Ah and Singh, Jatinder, Reviewable Automated Decision-Making: A Framework for Accountable Algorithmic Systems (December 17, 2020). ACM Conference on Fairness, Accountability, and Transparency (FAccT ‘21), March 1–10, 2021, Virtual Event, Canada, Available at SSRN: https://ssrn.com/abstract=3772964

Jennifer Cobbe (Contact Author)

University of Cambridge - Computer Laboratory ( email )

15 JJ Thomson Avenue
William Gates Building
Cambridge, CB3 0FD
United Kingdom

Michelle Seng Ah Lee

University of Cambridge ( email )

15 JJ Thomson Ave
William Gates Building
Cambridge, Cambridgeshire CB3 0FD
United Kingdom

HOME PAGE: http://compacctsys.soc.srcf.net/team/

Jatinder Singh

University of Cambridge -- Dept. Computer Science & Technology (Computer Laboratory) ( email )

15 JJ Thomson Avenue
William Gates Building
Cambridge, CB3 0FD
United Kingdom

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
101
Abstract Views
448
rank
313,703
PlumX Metrics