Towards Algorithm Auditing: A Survey on Managing Legal, Ethical and Technological Risks of AI, ML and Associated Algorithms

31 Pages Posted: 15 Feb 2021

See all articles by Adriano Koshiyama

Adriano Koshiyama

Department of Computer Science, University College London

Emre Kazim

University College London ; Holistic AI

Philip Treleaven

University College London

Pete Rai

Cisco Systems

Lukasz Szpruch

University of Edinburgh - School of Mathematics

Giles Pavey

Unilever - Unilever PLC London

Ghazi Ahamat

Centre for Data Ethics and Innovation

Franziska Leutner

University College London

Randy Goebel

University of Alberta

Andrew Knight

Royal Institution of Chartered Surveyors

Janet Adams

Ainstein AI

Christina Hitrova

Technical University Munich

Jeremy Barnett

University College London

Parashkev Nachev

University College of London

David Barber

University College London

Tomas Chamorro-Premuzic

University of London - Department of Psychology

Konstantin Klemmer

University of Warwick

Miro Gregorovic

London Stock Exchange

Shakeel Khan

ValidateAI

Elizabeth Lomas

University College London

Date Written: January 2021

Abstract

Business reliance on algorithms are becoming ubiquitous, and companies are increasingly concerned about their algorithms causing major financial or reputational damage. High-profile cases include VW’s Dieselgate scandal with fines worth of $34.69B, Knight Capital’s bankruptcy (~$450M) by a glitch in its algorithmic trading system, and Amazon’s AI recruiting tool being scrapped after showing bias against women. In response, governments are legislating and imposing bans, regulators fining companies, and the Judiciary discussing potentially making algorithms artificial “persons” in Law.

Soon there will be ‘billions’ of algorithms making decisions with minimal human intervention; from autonomous vehicles and finance, to medical treatment, employment, and legal decisions. Indeed, scaling to problems beyond the human is a major point of using such algorithms in the first place. As with Financial Audit, governments, business and society will require Algorithm Audit; formal assurance that algorithms are legal, ethical and safe. A new industry is envisaged: Auditing and Assurance of Algorithms (cf. Data privacy), with the remit to professionalize and industrialize AI, ML and associated algorithms.

The stakeholders range from those working on policy and regulation, to industry practitioners and developers. We also anticipate the nature and scope of the auditing levels and framework presented will inform those interested in systems of governance and compliance to regulation/standards. Our goal in this paper is to survey the key areas necessary to perform auditing and assurance, and instigate the debate in this novel area of research and practice.

Keywords: Robustness, Explainability, Privacy, Bias, Fairness, Transparency, Accountability, Governance, Compliance

Suggested Citation

Koshiyama, Adriano and Kazim, Emre and Treleaven, Philip and Rai, Pete and Szpruch, Lukasz and Pavey, Giles and Ahamat, Ghazi and Leutner, Franziska and Goebel, Randy and Knight, Andrew and Adams, Janet and Hitrova, Christina and Barnett, Jeremy and Nachev, Parashkev and Barber, David and Chamorro-Premuzic, Tomas and Klemmer, Konstantin and Gregorovic, Miro and Khan, Shakeel and Lomas, Elizabeth, Towards Algorithm Auditing: A Survey on Managing Legal, Ethical and Technological Risks of AI, ML and Associated Algorithms (January 2021). Available at SSRN: https://ssrn.com/abstract=3778998 or http://dx.doi.org/10.2139/ssrn.3778998

Adriano Koshiyama (Contact Author)

Department of Computer Science, University College London ( email )

Gower Street
London, London WC1E 6BT
United Kingdom

Emre Kazim

University College London ( email )

United Kingdom

Holistic AI ( email )

18 Soho Square
London, W1D 3QH

Philip Treleaven

University College London ( email )

Gower Street
London, WC1E 6BT
United Kingdom

Pete Rai

Cisco Systems ( email )

San Jose, CA 95134
United States

Lukasz Szpruch

University of Edinburgh - School of Mathematics ( email )

James Clerk Maxwell Building
Peter Guthrie Tait Rd
Edinburgh, EH9 3FD
United Kingdom

Giles Pavey

Unilever - Unilever PLC London ( email )

P.O. Box 68
Blackfriars
London EC4P 4BQ
United States

Ghazi Ahamat

Centre for Data Ethics and Innovation ( email )

London
United Kingdom

Franziska Leutner

University College London ( email )

Gower Street
London, WC1E 6BT
United Kingdom

Randy Goebel

University of Alberta ( email )

Edmonton, Alberta T6G 2R3
Canada

Andrew Knight

Royal Institution of Chartered Surveyors ( email )

London
United Kingdom

Janet Adams

Ainstein AI ( email )

Christina Hitrova

Technical University Munich ( email )

Germany

Jeremy Barnett

University College London ( email )

Gower Street
London, WC1E 6BT
United Kingdom
07976292166 (Phone)

Parashkev Nachev

University College of London ( email )

Gower Street
London, WC1E 6BT
United Kingdom

David Barber

University College London ( email )

Gower Street
London, WC1E 6BT
United Kingdom

Tomas Chamorro-Premuzic

University of London - Department of Psychology ( email )

New Cross
London, SE14 6NW
United Kingdom
020 7919 7885 (Phone)

Konstantin Klemmer

University of Warwick ( email )

Gibbet Hill Rd.
Coventry, West Midlands CV4 8UW
United Kingdom

Miro Gregorovic

London Stock Exchange ( email )

London EC2N 1HP
United States

Shakeel Khan

ValidateAI ( email )

Elizabeth Lomas

University College London ( email )

Gower Street
London, WC1E 6BT
United Kingdom

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