000			01847cam a2200277 4500500
005			20250121084821.0
041			_afre
042			_adc
100	1	0	_aBazin, Yoann _eauthor
245	0	0	_aMaking Artificial Intelligence More Sustainable: Three Points of Entry into an Ethical Black Box
260			_c2024.
500			_a15
520			_aThe technological leaps in artificial intelligence (AI) over the past twenty years have profoundly renewed its ability to support, if not replace, humans in many settings, further intensifying rising concerns about decisions made, actions taken, and their potential consequences. A robust conceptual framework to engage with AI ethics is thus more necessary than ever in order to make it more sustainable. To this purpose, this essay advances an approach to AI ethics ‘from within’, defining it as the style of its algorithm(s) in practice. To demonstrate its practical value, I explore three points of entry: (1) value-laden patterns embedded in datasets used in machine learning, (2) the importance of value functions in the training and operating of AI, and (3) the possibility of adjusting some ‘ethics settings’. The example of algorithmic Human Resource Management (HRM) is examined to see how it can be brought closer to sustainable HRM. JEL Codes: O310
690			_aAlgorithmic HRM
690			_aSustainable HRM
690			_aAI Ethics
690			_aArtificial Intelligence
690			_aEthics Settings
690			_aAlgorithmic HRM
690			_aSustainable HRM
690			_aAI Ethics
690			_aArtificial Intelligence
690			_aEthics Settings
786	0		_nJournal of Innovation Economics & Management | o 44 | 2 | 2024-05-17 | p. 119-136
856	4	1	_uhttps://shs.cairn.info/journal-of-innovation-economics-2024-2-page-119?lang=en&redirect-ssocas=7080
999			_c506511 _d506511