000			01914cam a2200289 4500500
005			20250121115550.0
041			_afre
042			_adc
100	1	0	_aVuarin, Louis _eauthor
700	1	0	_a Steyer, Véronique _eauthor
245	0	0	_aThe principle of explainability of ai and its application in organizations
260			_c2023.
500			_a40
520			_aThe explainability of Artificial Intelligence (AI) is cited in the literature as a pillar of AI ethics, yet few studies explore its organizational reality. This study proposes to remedy this shortcoming, based on interviews with actors in charge of designing and implementing AI in 17 organizations. Our results highlight: the massive substitution of explainability by the emphasis on performance indicators; the substitution of the requirement of understanding by a requirement of accountability; and the ambiguous place of industry experts within design processes, where they are employed to validate the apparent coherence of ‘black-box’ algorithms rather than to open and understand them. In organizational practice, explainability thus appears sufficiently undefined to reconcile contradictory injunctions. Comparing prescriptions in the literature and practices in the field, we discuss the risk of crystallizing these organizational issues via the standardization of management tools used as part of (or instead of) AI explainability.
690			_aaccountability
690			_aAI ethics
690			_aeXplainable AI (XAI)
690			_aexplainability
690			_aArtificial Intelligence
690			_aaccountability
690			_aAI ethics
690			_aeXplainable AI (XAI)
690			_aexplainability
690			_aArtificial Intelligence
786	0		_nRéseaux | o 240 | 4 | 2023-09-21 | p. 179-210 | 0751-7971
856	4	1	_uhttps://shs.cairn.info/journal-reseaux-2023-4-page-179?lang=en&redirect-ssocas=7080
999			_c549366 _d549366