000 02439nam a22003497ac4500
999 _c6026
_d6026
001 00006026
003 ES-MaONT
005 20220728103336.0
008 200625t2020 ||||fo||||i00| 0 eng d
020 _a9789284658558
024 _2doi
_a10.2861/247
040 _aES-MaONT
245 1 0 _aArtificial intelligence: From ethics to policy
260 _aBrussels
_bEuropan Union
_c2020
300 _a37 pág.
336 _2isbdcontent
_atexto
337 _2isbdmedia
_ainformático
338 _2rdacarrier
_arecurso en línea
520 _aThere is little doubt that artificial intelligence (AI) and ma ch inelearning (ML) will revolutionise public services. However, th e p ower for positive change that AI provides simultaneously holds the potential for negative impacts on society. AI ethics work to uncover the variety of ethical issues resulting from the design, development, and deployment of AI. The question at the centre of all current work in AI ethics is: How can we move from AI ethics to specific policy and legislation for governing AI? Based on a framing of 'AI as a social experiment', this study arrives at policy options for public administrations and governmental organisations who are looking to deploy AI/ML solutions, as well as the private companies who are creating AI/ML solutions for use in the public arena. The reasons for targeting this application sector concern: the need for a high standard of transparency, respect for democratic values, and legitimacy. The policy options presented here chart a path towards accountability; procedures and decisions of an ethical nature are systematically logged prior to the deployment of an AI system. This logging is the first step in allowing ethics to play a crucial role in the implementation of AI for the public good
650 0 _94348
_aInteligencia Artificial
653 _aAI
653 _amachine learning
653 _aML
653 _apublic services
653 _apolicies
653 _agoverning AI
700 _a van Wynsberghe, Aimee
_94499
710 _aParlamento Europeo
_95217
856 4 2 _uhttps://www.europarl.europa.eu/RegData/etudes/STUD/2020/641507/EPRS_STU(2020)641507_EN.pdf
_x0
_yacceso al documento
856 4 2 _uhttps://www.europarl.europa.eu/thinktank/en/document.html?reference=EPRS_STU(2020)641507
_x0
_yMás información
942 _2z
_cINF