Explainable AI improves task performance in human-AI collaboration

Autor: Senoner, Julian, Schallmoser, Simon, Kratzwald, Bernhard, Feuerriegel, Stefan, Netland, Torbjørn

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 remain

Externí odkaz: http://arxiv.org/abs/2406.08271

Machine Learning for Predicting Micro- and Macrovascular Complications in Individuals With Prediabetes or Diabetes: Retrospective Cohort Study.

Autor: Schallmoser, Simon^1,2 (AUTHOR) schallmoser@lmu.de, Zueger, Thomas^3,4 (AUTHOR), Kraus, Mathias⁵ (AUTHOR), Saar-Tsechansky, Maytal⁶ (AUTHOR), Stettler, Christoph³ (AUTHOR), Feuerriegel, Stefan^1,2 (AUTHOR)

Publikováno v: Journal of Medical Internet Research. 2023, Vol. 25 Issue 1, p1-14. 14p.

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Explainable AI improves task performance in human-AI collaboration.

Autor: Senoner J; ETH Zurich, Zurich, Switzerland.; EthonAI, Zurich, Switzerland., Schallmoser S; LMU Munich, Munich, Germany.; Munich Center for Machine Learning (MCML), Munich, Germany., Kratzwald B; ETH Zurich, Zurich, Switzerland.; EthonAI, Zurich, Switzerland., Feuerriegel S; LMU Munich, Munich, Germany.; Munich Center for Machine Learning (MCML), Munich, Germany., Netland T; ETH Zurich, Zurich, Switzerland. tnetland@ethz.ch.

Publikováno v: Scientific reports [Sci Rep] 2024 Dec 28; Vol. 14 (1), pp. 31150. Date of Electronic Publication: 2024 Dec 28.