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Emerging Perspectives on Medical Decision Support: Co-Designing XAI

Paper presented at Fair & Responsible AI Workshop @ CHI 2020, April 25-30, 2020, Honolulu, Hawaii
: Mucha, Henrik; Robert, Sebastian

Volltext urn:nbn:de:0011-n-5905130 (310 KByte PDF)
MD5 Fingerprint: 41106a47d59d241624cf0839157b8405
Erstellt am: 6.6.2020

2020, 4 S.
Fair & Responsible AI Workshop <2020, Honolulu/Hawaii>
Conference on Human Factors in Computing Systems (CHI) <2020, Honolulu/Hawaii>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()

Artificial Intelligence (AI) and Machine Learning (ML) promise to significantly enhance the capabilities of decision support systems in medicine. Yet, if these systems fail to providean understandable rationale of the decision making process the adoption of this powerful technology will be difficult. Hence, there is growing interest in Explainable Artificial Intelligence (XAI). Explanations that speak the language of the user are a cornerstone for Fair AI. In this position paper, we reflect on research about XAI and designing decision support in medicine. From there, we lay out an approach to use co-design methodology to explore how users perceive and interact with explanations of decision support systems.