Literature DB >> 29342707

Human-like machines: Transparency and comprehensibility.

Piotr M Patrzyk1, Daniela Link1, Julian N Marewski1.   

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.

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Year:  2017        PMID: 29342707     DOI: 10.1017/S0140525X17000255

Source DB:  PubMed          Journal:  Behav Brain Sci        ISSN: 0140-525X            Impact factor:   12.579


  2 in total

1.  Impact of artificial intelligence on pathologists' decisions: an experiment.

Authors:  Julien Meyer; April Khademi; Bernard Têtu; Wencui Han; Pria Nippak; David Remisch
Journal:  J Am Med Inform Assoc       Date:  2022-09-12       Impact factor: 7.942

2.  Artificial Intelligence Decision-Making Transparency and Employees' Trust: The Parallel Multiple Mediating Effect of Effectiveness and Discomfort.

Authors:  Liangru Yu; Yi Li
Journal:  Behav Sci (Basel)       Date:  2022-04-27
  2 in total

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