Browsing by Author "Baesens, Bart"

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    (33, 33) De Bock, Koen W.; Coussement, Kristof; Caigny, Arno De; S?owi?ski, Roman; Baesens, Bart; Boute, Robert N.; Choi, Tsan Ming; Delen, Dursun; Kraus, Mathias; Lessmann, Stefan; Maldonado, Sebastián; Martens, David; Óskarsdóttir, María; Vairetti, Carla; Verbeke, Wouter; Weber, Richard; 33
    The ability to understand and explain the outcomes of data analysis methods, with regard to aiding decision-making, has become a critical requirement for many applications. For example, in operational research domains, data analytics have long been promoted as a way to enhance decision-making. This study proposes a comprehensive, normative framework to define explainable artificial intelligence (XAI) for operational research (XAIOR) as a reconciliation of three subdimensions that constitute its requirements: performance, attributable, and responsible analytics. In turn, this article offers in-depth overviews of how XAIOR can be deployed through various methods with respect to distinct domains and applications. Finally, an agenda for future XAIOR research is defined.