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Human-like machines: Transparency and comprehensibility

  • Piotr M. Patrzyk (a1), Daniela Link (a1) and Julian N. Marewski (a1)

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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