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pro vyhledávání: '"Satzger, P."'
The integration of artificial intelligence into business processes has significantly enhanced decision-making capabilities across various industries such as finance, healthcare, and retail. However, explaining the decisions made by these AI systems p
Externí odkaz:
http://arxiv.org/abs/2410.20873
Autor:
Spitzer, Philipp, Holstein, Joshua, Morrison, Katelyn, Holstein, Kenneth, Satzger, Gerhard, Kühl, Niklas
Across various applications, humans increasingly use black-box artificial intelligence (AI) systems without insight into these systems' reasoning. To counter this opacity, explainable AI (XAI) methods promise enhanced transparency and interpretabilit
Externí odkaz:
http://arxiv.org/abs/2409.12809
In numerous high-stakes domains, training novices via conventional learning systems does not suffice. To impart tacit knowledge, experts' hands-on guidance is imperative. However, training novices by experts is costly and time-consuming, increasing t
Externí odkaz:
http://arxiv.org/abs/2406.01329
Identifying and handling label errors can significantly enhance the accuracy of supervised machine learning models. Recent approaches for identifying label errors demonstrate that a low self-confidence of models with respect to a certain label repres
Externí odkaz:
http://arxiv.org/abs/2405.09602
Artificial intelligence (AI) has the potential to significantly enhance human performance across various domains. Ideally, collaboration between humans and AI should result in complementary team performance (CTP) -- a level of performance that neithe
Externí odkaz:
http://arxiv.org/abs/2404.00029
Autor:
Spitzer, Philipp, Holstein, Joshua, Hemmer, Patrick, Vössing, Michael, Kühl, Niklas, Martin, Dominik, Satzger, Gerhard
The constantly increasing capabilities of artificial intelligence (AI) open new possibilities for human-AI collaboration. One promising approach to leverage existing complementary capabilities is allowing humans to delegate individual instances to th
Externí odkaz:
http://arxiv.org/abs/2401.04729
Autor:
Schemmer, Max, Bartos, Andrea, Spitzer, Philipp, Hemmer, Patrick, Kühl, Niklas, Liebschner, Jonas, Satzger, Gerhard
Publikováno v:
International Conference on Information Systems (ICIS 2023)
The true potential of human-AI collaboration lies in exploiting the complementary capabilities of humans and AI to achieve a joint performance superior to that of the individual AI or human, i.e., to achieve complementary team performance (CTP). To r
Externí odkaz:
http://arxiv.org/abs/2310.02108
Information systems increasingly leverage artificial intelligence (AI) and machine learning (ML) to generate value from vast amounts of data. However, ML models are imperfect and can generate incorrect classifications. Hence, human-in-the-loop (HITL)
Externí odkaz:
http://arxiv.org/abs/2307.03003
As the shortage of skilled workers continues to be a pressing issue, exacerbated by demographic change, it is becoming a critical challenge for organizations to preserve the knowledge of retiring experts and to pass it on to novices. While this knowl
Externí odkaz:
http://arxiv.org/abs/2305.07681
In AI-assisted decision-making, a central promise of having a human-in-the-loop is that they should be able to complement the AI system by overriding its wrong recommendations. In practice, however, we often see that humans cannot assess the correctn
Externí odkaz:
http://arxiv.org/abs/2304.08804