On the Cruelty of Computational Reasoning

We seek, firstly, to demonstrate the cruelty of current computational reasoning artefacts when applied to decision making in human social systems. We see this cruelty as unintended and not a direct expression of the motives and values of those creating or using algorithms. But in our view, a certain consequence nevertheless. Secondly, we seek to identify the key aspects of some exemplars of AI products and services that demonstrate these properties and consequences and relate these to the form of reasoning that they embody. Third we show how the reasoning strategies developed and now increasingly deployed by computer and data science have necessary, special and damaging qualities in the social world. Briefly noting how the narrative underpinning the creation and use of AI and other tools provides them with power in neoliberal economies. Creating a disempowered data ‘subject’ in an inferior, economically and politically supine position from which they must defend themselves if they can.

Content

Author and article information

Contributors

Alexander Hogan

Kevin Hogan

Christian Tilt

Conference

Publication date: May 2018

Publication date (Print): May 2018

Pages: 1-6

Affiliations

[1 ] ETIC Lab

Newtown, UK

[2 ] ETIC Lab

Milton Keynes, UK

Article

DOI: 10.14236/ewic/EVAC18.3

SO-VID: a134d476-2c1c-45e3-9e94-64384f8cef38

License:

This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

Conference name: Politics of the Machines - Art and After

Conference acronym: EVA Copenhagen

Conference number: 7

Conference location: Aalborg University, Copenhagen, Denmark

Conference date: 15 - 17 May 2018

Conference sponsor: Electronic Workshops in Computing (eWiC)

Conference theme: Digital arts and culture

History

Product

1477-9358 BCS Learning & Development

Self URI (article page): https://www.scienceopen.com/hosted-document?doi=10.14236/ewic/EVAC18.3

Self URI (journal page): https://ewic.bcs.org/

References

J Angwin J Larson S Mattu L Kirchner 2016 Machine bias: There’s software used across the country to predict future criminals and it’s biased against blacks A vailable online at www.propublica.org/article/machine-bias-risk-SSRN : https://ssrn.com/abstract=3020259
L Markus 2017 Datification, Organizational Strategy, and IS Research: What’s the Score? The Journal of Strategic Information Systems 26 3 233 241
C O’Neil 2016 Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy Crown Publishing Group New York, NY
M OswaldJ. GraceS Urwin G. Barnes 2017 Algorithmic Risk Assessment Policing Models: Lessons from the Durham HART Model and ‘Experimental’ Proportionality. Information & Communications Technology Law, Forthcoming Available at SSRN: https://ssrn.com/abstract=3029345 or http://dx.doi.org/10.2139/ssrn.3029345
R. ParasuramanD.H. Manzey 2010 ”Complacency and Bias in Human Use of Automation: An Attentional Integration”. Human Factors 52 3 381 410
A Chouldechova 2017 Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments Big Data 5 2
W Dieterich W Oliver T Brennan 2013 Predictive Validity of the COMPAS Reentry Risk Scales Northpointe https://epic.org/algorithmic-transparency/crim-justice/EPIC-16-06-23-WI- FOIA-201600805-MDOC_ReentryStudy082213.pdf May 2018
V Eubanks 2018 Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor New York, NY: St. Martin’s Press
AW FloresK Bechtel CT Lowenkamp 2016 False positives, false negatives, and false analyses: A rejoinder to ‘‘machine bias: There’s software used across the country to predict future criminals And it’s biased against blacks Available at: http://www.crj.org/page/-/publications/ rejoinder7.11.pdf
A Howard J Borenstein 2017 The Ugly Truth About Ourselves and Our Robot Creations: The Problem of Bias and Social Inequity Sci Eng Ethics 2017 Sep 21 10.1007/s11948-017-9975-2 [Epub ahead of print]
E Joh 2017 Feeding the Machine: Policing, Crime Data, & Algorithms William & Mary Bill ofdebtors They have none of the resources
J Rights 2017 Forthcoming Available at personal, intellectual or financial that may be required to understand or militate the circumstances they find themselves in Not least because no other single actor in this ecosystem has the information to fully comprehend what it is that is happening to these people.

Comments

Comment on this article

[1] J Angwin J Larson S Mattu L Kirchner 2016 Machine bias: There’s software used across the country to predict future criminals and it’s biased against blacks A vailable online at www.propublica.org/article/machine-bias-risk-SSRN : https://ssrn.com/abstract=3020259

[2] L Markus 2017 Datification, Organizational Strategy, and IS Research: What’s the Score? The Journal of Strategic Information Systems 26 3 233 241

[3] C O’Neil 2016 Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy Crown Publishing Group New York, NY

[4] M OswaldJ. GraceS Urwin G. Barnes 2017 Algorithmic Risk Assessment Policing Models: Lessons from the Durham HART Model and ‘Experimental’ Proportionality. Information & Communications Technology Law, Forthcoming Available at SSRN: https://ssrn.com/abstract=3029345 or http://dx.doi.org/10.2139/ssrn.3029345

[5] R. ParasuramanD.H. Manzey 2010 ”Complacency and Bias in Human Use of Automation: An Attentional Integration”. Human Factors 52 3 381 410

[6] A Chouldechova 2017 Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments Big Data 5 2

[7] W Dieterich W Oliver T Brennan 2013 Predictive Validity of the COMPAS Reentry Risk Scales Northpointe https://epic.org/algorithmic-transparency/crim-justice/EPIC-16-06-23-WI- FOIA-201600805-MDOC_ReentryStudy082213.pdf May 2018

[8] V Eubanks 2018 Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor New York, NY: St. Martin’s Press

[9] AW FloresK Bechtel CT Lowenkamp 2016 False positives, false negatives, and false analyses: A rejoinder to ‘‘machine bias: There’s software used across the country to predict future criminals And it’s biased against blacks Available at: http://www.crj.org/page/-/publications/ rejoinder7.11.pdf

[10] A Howard J Borenstein 2017 The Ugly Truth About Ourselves and Our Robot Creations: The Problem of Bias and Social Inequity Sci Eng Ethics 2017 Sep 21 10.1007/s11948-017-9975-2 [Epub ahead of print]

[11] E Joh 2017 Feeding the Machine: Policing, Crime Data, & Algorithms William & Mary Bill ofdebtors They have none of the resources

[12] J Rights 2017 Forthcoming Available at personal, intellectual or financial that may be required to understand or militate the circumstances they find themselves in Not least because no other single actor in this ecosystem has the information to fully comprehend what it is that is happening to these people.

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