Скачать или смотреть Explainable AI improves task performance in human–AI collaboration

Video summary of the research article 'Explainable AI improves task performance in human–AI collaboration' by Julian Senoner, Simon Schallmoser, Bernhard Kratzwald, Stefan Feuerriegel, and Torbjørn Netland. Nature | Scientific Reports.

Link to paper: https://www.nature.com/articles/s4159...

Abstract: Artificial intelligence (AI) provides considerable opportunities to assist human work. However, one crucial challenge of human-AI collaboration is that many AI algorithms operate
in a black-box manner where the way how the AI makes predictions remains opaque. This
makes it difficult for humans to validate a prediction made by AI against their own domain knowledge. For this reason, we hypothesize that augmenting humans with explainable
AI improves task performance in human–AI collaboration. To test this hypothesis, we implement explainable AI in the form of visual heat maps in inspection tasks conducted by
domain experts. The results of our experiments show that task performance significantly improves when participants are supported by explainable AI with heat maps instead of black-box AI.