Meeting KI-Postdocs: 'Negativity bias in research' "Why comparisons between the transparency of artificial intelligence and human cognition are problematic" (Dr. Uwe Peters) Artificial intelligence (AI) algorithms used in high-stakes decision-making contexts often lack transparency in that the internal factors that lead them to their decisions remain unknown. While this is commonly thought to be a problem with these systems, many AI researchers respond that we shouldn’t be overly concerned because empirical evidence shows that human decision-making is equally opaque and isn’t usually required to be more transparent. I argue that the empirical data on human cognition that are claimed to support this equal opacity view don’t sufficiently support it. In fact, the equal opacity view rests on a narrow, selective, and uncritical survey of relevant psychological studies. https://www.uni-bonn.de/en/research-and-teaching/research-profile/transdisciplinary-research-areas/tra-4-individuals/events-1/negativity-bias-in-research-part-of-the-ai-research-group https://www.uni-bonn.de/++resource++plone-logo.svg

"Why comparisons between the transparency of artificial intelligence and human cognition are problematic"
(Dr. Uwe Peters)

Artificial intelligence (AI) algorithms used in high-stakes decision-making contexts often lack transparency in that the internal factors that lead them to their decisions remain
unknown. While this is commonly thought to be a problem with these systems, many AI researchers respond that we shouldn’t be overly concerned because empirical evidence
shows that human decision-making is equally opaque and isn’t usually required to be more transparent. I argue that the empirical data on human cognition that are claimed to support this equal opacity view don’t sufficiently support it. In fact, the equal opacity view rests on a narrow, selective, and uncritical survey of relevant psychological studies.