Literature DB >> 30322999

The fallacy of inscrutability.

Joshua A Kroll1.   

Abstract

Contrary to the criticism that mysterious, unaccountable black-box software systems threaten to make the logic of critical decisions inscrutable, we argue that algorithms are fundamentally understandable pieces of technology. Software systems are designed to interact with the world in a controlled way and built or operated for a specific purpose, subject to choices and assumptions. Traditional power structures can and do turn systems into opaque black boxes, but technologies can always be understood at a higher level, intensionally in terms of their designs and operational goals and extensionally in terms of their inputs, outputs and outcomes. The mechanisms of a system's operation can always be examined and explained, but a focus on machinery obscures the key issue of power dynamics. While structural inscrutability frustrates users and oversight entities, system creators and operators always determine that the technologies they deploy are fit for certain uses, making no system wholly inscrutable. We investigate the contours of inscrutability and opacity, the way they arise from power dynamics surrounding software systems, and the value of proposed remedies from disparate disciplines, especially computer ethics and privacy by design. We conclude that policy should not accede to the idea that some systems are of necessity inscrutable. Effective governance of algorithms comes from demanding rigorous science and engineering in system design, operation and evaluation to make systems verifiably trustworthy. Rather than seeking explanations for each behaviour of a computer system, policies should formalize and make known the assumptions, choices, and adequacy determinations associated with a system.This article is part of the theme issue 'Governing artificial intelligence: ethical, legal, and technical opportunities and challenges'.
© 2018 The Author(s).

Keywords:  accountability; artificial intelligence; governance; machine learning

Year:  2018        PMID: 30322999      PMCID: PMC6191668          DOI: 10.1098/rsta.2018.0084

Source DB:  PubMed          Journal:  Philos Trans A Math Phys Eng Sci        ISSN: 1364-503X            Impact factor:   4.226


  12 in total

1.  Governing artificial intelligence: ethical, legal and technical opportunities and challenges.

Authors:  Corinne Cath
Journal:  Philos Trans A Math Phys Eng Sci       Date:  2018-10-15       Impact factor: 4.226

2.  Operationalising AI governance through ethics-based auditing: an industry case study.

Authors:  Jakob Mökander; Luciano Floridi
Journal:  AI Ethics       Date:  2022-05-31

3.  Environmental Adaptation and Differential Replication in Machine Learning.

Authors:  Irene Unceta; Jordi Nin; Oriol Pujol
Journal:  Entropy (Basel)       Date:  2020-10-03       Impact factor: 2.524

4.  Transparency as design publicity: explaining and justifying inscrutable algorithms.

Authors:  Michele Loi; Andrea Ferrario; Eleonora Viganò
Journal:  Ethics Inf Technol       Date:  2020-10-20

5.  Ethics-Based Auditing of Automated Decision-Making Systems: Nature, Scope, and Limitations.

Authors:  Jakob Mökander; Jessica Morley; Mariarosaria Taddeo; Luciano Floridi
Journal:  Sci Eng Ethics       Date:  2021-07-06       Impact factor: 3.525

6.  From What to How: An Initial Review of Publicly Available AI Ethics Tools, Methods and Research to Translate Principles into Practices.

Authors:  Jessica Morley; Luciano Floridi; Libby Kinsey; Anat Elhalal
Journal:  Sci Eng Ethics       Date:  2019-12-11       Impact factor: 3.525

7.  Ensuring trustworthy use of artificial intelligence and big data analytics in health insurance.

Authors:  Calvin W L Ho; Joseph Ali; Karel Caals
Journal:  Bull World Health Organ       Date:  2020-02-25       Impact factor: 9.408

8.  Risk mitigation in algorithmic accountability: The role of machine learning copies.

Authors:  Irene Unceta; Jordi Nin; Oriol Pujol
Journal:  PLoS One       Date:  2020-11-03       Impact factor: 3.240

9.  Just data? Solidarity and justice in data-driven medicine.

Authors:  Patrik Hummel; Matthias Braun
Journal:  Life Sci Soc Policy       Date:  2020-08-25

10.  Differential Replication for Credit Scoring in Regulated Environments.

Authors:  Irene Unceta; Jordi Nin; Oriol Pujol
Journal:  Entropy (Basel)       Date:  2021-03-30       Impact factor: 2.524

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