Human-like machines: Transparency and comprehensibility.
- Patrzyk PM 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.
- Link D 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.
- Marewski JN 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.
- 2018-01-19
Published in:
- The Behavioral and brain sciences. - 2017
English
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.
- Language
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- English
- Open access status
- green
- Identifiers
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- DOI 10.1017/S0140525X17000255
- PMID 29342707
- Persistent URL
- https://sonar.ch/global/documents/148323
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