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Human-like machines: Transparency and comprehensibility
Piotr M Patrzyk1, Daniela Link1, Julian N Marewski1
1Faculty 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.
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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