Literature DB >> 34083752

Bad machines corrupt good morals.

Nils Köbis1, Jean-François Bonnefon2, Iyad Rahwan3.   

Abstract

As machines powered by artificial intelligence (AI) influence humans' behaviour in ways that are both like and unlike the ways humans influence each other, worry emerges about the corrupting power of AI agents. To estimate the empirical validity of these fears, we review the available evidence from behavioural science, human-computer interaction and AI research. We propose four main social roles through which both humans and machines can influence ethical behaviour. These are: role model, advisor, partner and delegate. When AI agents become influencers (role models or advisors), their corrupting power may not exceed the corrupting power of humans (yet). However, AI agents acting as enablers of unethical behaviour (partners or delegates) have many characteristics that may let people reap unethical benefits while feeling good about themselves, a potentially perilous interaction. On the basis of these insights, we outline a research agenda to gain behavioural insights for better AI oversight.

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Year:  2021        PMID: 34083752     DOI: 10.1038/s41562-021-01128-2

Source DB:  PubMed          Journal:  Nat Hum Behav        ISSN: 2397-3374


  39 in total

1.  Business culture and dishonesty in the banking industry.

Authors:  Alain Cohn; Ernst Fehr; Michel André Maréchal
Journal:  Nature       Date:  2014-11-19       Impact factor: 49.962

2.  Civic honesty around the globe.

Authors:  Alain Cohn; Michel André Maréchal; Christian Lukas Zünd; David Tannenbaum
Journal:  Science       Date:  2019-06-20       Impact factor: 47.728

Review 3.  Machine behaviour.

Authors:  Iyad Rahwan; Manuel Cebrian; Nick Obradovich; Josh Bongard; Jean-François Bonnefon; Cynthia Breazeal; Jacob W Crandall; Nicholas A Christakis; Iain D Couzin; Matthew O Jackson; Nicholas R Jennings; Ece Kamar; Isabel M Kloumann; Hugo Larochelle; David Lazer; Richard McElreath; Alan Mislove; David C Parkes; Alex 'Sandy' Pentland; Margaret E Roberts; Azim Shariff; Joshua B Tenenbaum; Michael Wellman
Journal:  Nature       Date:  2019-04-24       Impact factor: 49.962

Review 4.  The grand challenges of Science Robotics.

Authors:  Guang-Zhong Yang; Jim Bellingham; Pierre E Dupont; Peer Fischer; Luciano Floridi; Robert Full; Neil Jacobstein; Vijay Kumar; Marcia McNutt; Robert Merrifield; Bradley J Nelson; Brian Scassellati; Mariarosaria Taddeo; Russell Taylor; Manuela Veloso; Zhong Lin Wang; Robert Wood
Journal:  Sci Robot       Date:  2018-01-31

5.  The truth about lies: A meta-analysis on dishonest behavior.

Authors:  Philipp Gerlach; Kinneret Teodorescu; Ralph Hertwig
Journal:  Psychol Bull       Date:  2019-01       Impact factor: 17.737

6.  Heterogeneity in banker culture and its influence on dishonesty.

Authors:  Zoe Rahwan; Erez Yoeli; Barbara Fasolo
Journal:  Nature       Date:  2019-11-13       Impact factor: 49.962

7.  Ethical Free Riding: When Honest People Find Dishonest Partners.

Authors:  Jörg Gross; Margarita Leib; Theo Offerman; Shaul Shalvi
Journal:  Psychol Sci       Date:  2018-10-16

8.  Rule Following Mitigates Collaborative Cheating and Facilitates the Spreading of Honesty Within Groups.

Authors:  Jörg Gross; Carsten K W De Dreu
Journal:  Pers Soc Psychol Bull       Date:  2020-06-17

9.  The collaborative roots of corruption.

Authors:  Ori Weisel; Shaul Shalvi
Journal:  Proc Natl Acad Sci U S A       Date:  2015-08-10       Impact factor: 11.205

10.  Intrinsic honesty and the prevalence of rule violations across societies.

Authors:  Simon Gächter; Jonathan F Schulz
Journal:  Nature       Date:  2016-03-09       Impact factor: 49.962

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  4 in total

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Journal:  J Am Med Inform Assoc       Date:  2022-03-15       Impact factor: 4.497

2.  Delegation to artificial agents fosters prosocial behaviors in the collective risk dilemma.

Authors:  Elias Fernández Domingos; Inês Terrucha; Rémi Suchon; Jelena Grujić; Juan C Burguillo; Francisco C Santos; Tom Lenaerts
Journal:  Sci Rep       Date:  2022-05-19       Impact factor: 4.996

3.  Linking Human And Machine Behavior: A New Approach to Evaluate Training Data Quality for Beneficial Machine Learning.

Authors:  Thilo Hagendorff
Journal:  Minds Mach (Dordr)       Date:  2021-09-26       Impact factor: 5.339

4.  Conformity Assessments and Post-market Monitoring: A Guide to the Role of Auditing in the Proposed European AI Regulation.

Authors:  Jakob Mökander; Maria Axente; Federico Casolari; Luciano Floridi
Journal:  Minds Mach (Dordr)       Date:  2021-11-05       Impact factor: 5.339

  4 in total

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