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Human-like machines: Transparency and comprehensibility

Published online by Cambridge University Press:  10 November 2017

Piotr M. Patrzyk ,
Daniela Link  and
Julian N. Marewski
Piotr M. Patrzyk
Affiliation:
Faculty of Business and Economics, University of Lausanne, Quartier UNIL-Dorigny, Internef, CH-1015 Lausanne, Switzerland. piotr.patrzyk@unil.chdaniela.link@unil.chjulian.marewski@unil.ch
Daniela Link
Affiliation:
Faculty of Business and Economics, University of Lausanne, Quartier UNIL-Dorigny, Internef, CH-1015 Lausanne, Switzerland. piotr.patrzyk@unil.chdaniela.link@unil.chjulian.marewski@unil.ch
Julian N. Marewski
Affiliation:
Faculty of Business and Economics, University of Lausanne, Quartier UNIL-Dorigny, Internef, CH-1015 Lausanne, Switzerland. piotr.patrzyk@unil.chdaniela.link@unil.chjulian.marewski@unil.ch
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Abstract

Artificial intelligence algorithms seek inspiration from human cognitive systems in areas where humans outperform machines. But on what level should algorithms try to approximate human cognition? We argue that human-like machines should be designed to make decisions in transparent and comprehensible ways, which can be achieved by accurately mirroring human cognitive processes.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 40 , 2017 , e276
Copyright
Copyright © Cambridge University Press 2017 

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