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A social evaluation of the perceived goodness of explainability in machine learning
Wanner, Jonas Paul; Herm, Lukas-Valentin; Heinrich, Kai; … - In: Journal of business analytics 5 (2022) 1, pp. 29-50
Persistent link: https://www.econbiz.de/10013270528
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Machine learning explainability in finance : an application to default risk analysis
Bracke, Philippe; Datta, Anupam; Jung, Carsten; Sen, Shayak - 2019
Persistent link: https://www.econbiz.de/10012202304
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Explainable Performance
Hué, Sullivan; Hurlin, Christophe; Pérignon, Christophe; … - 2022
We introduce the XPER (eXplainable PERformance) methodology to measure the specific contribution of the input features to the predictive or economic performance of a model. Our methodology offers several advantages. First, it is both model-agnostic and performance metric-agnostic. Second, XPER...
Persistent link: https://www.econbiz.de/10014236985
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Application of machine learning to a credit rating classification model : techniques for improving the explainability of machine learning
Hashimoto, Ryuichiro; Miura, Kakeru; Yoshizaki, Yasunori - 2023
Persistent link: https://www.econbiz.de/10014305584
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Forecasting accuracy of machine learning and linear regression : evidence from the secondary CAT bond market
Götze, Tobias; Gürtler, Marc; Witowski, Eileen - In: Journal of business economics : JBE 93 (2023) 9, pp. 1629-1660
Persistent link: https://www.econbiz.de/10014422250
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Explainable subgradient tree boosting for prescriptive analytics in operations management
Notz, Pascal Markus; Pibernik, Richard - In: European journal of operational research : EJOR 312 (2024) 3, pp. 1119-1133
Persistent link: https://www.econbiz.de/10014456474
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Artificial Intelligence in travel
Vinod, B. - In: Journal of revenue and pricing management 20 (2021) 3, pp. 368-375
Persistent link: https://www.econbiz.de/10012549352
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When the machine meets the expert : an ethnography of developing AI for hiring
Broek, Elmira van den; Sergeeva, Anastasia; Huysman, Marleen - In: MIS quarterly 45 (2021) 3, pp. 1557-1580
Persistent link: https://www.econbiz.de/10012631445
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Avoiding an oppressive future of machine learning : a design theory for emancipatory assistants
Kane, Gerald C.; Young, Amber G.; Majchrak, Ann; … - In: MIS quarterly 45 (2021) 1, pp. 371-396
Persistent link: https://www.econbiz.de/10012814902
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A note on the interpretability of machine learning algorithms
Guégan, Dominique - 2020
Persistent link: https://www.econbiz.de/10012498600