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001			00005620
003			ES-MaONT
005			20220719152924.0
008			200122s2020 lu a|||| |||| 00| 0 eng d
020			_a978-92-76-14660-5 (online)
022			_a1831-9424 (online)
024			_d. _2doi _a10.2760/57493
040			_aES-MaONT
100	1		_aHamon, Ronan _93915
245	1	0	_aRobustness and explainability of Artificial Intelligence _b: from technical to policy solutions _c/ Ronan Hamon, Henrik Junklewitz, Ignacio Sanchez
260			_aLuxembourg _b: Publications Office of the European Union, _c2020
300			_a34 p. : _bil. _c; 1 documento PDF
336			_atexto (visual) _2isbdcontent
337			_aelectrónico _2isbdmedia
338			_arecurso en línea _2rdacarrier
490			_aJRC Technical Reports _v; 30040
504			_aBibliografía: p. 26-31
520			_aIn the light of the recent advances in artificial intelligence (AI), the serious negative consequences of its use for EU citizens and organisations have led to multiple initiatives from the European Commission to set up the principles of a trustworthy and secure AI. Among the identified requirements, the concepts of robustness and explainability of AI systems have emerged as key elements for a future regulation of this technology. This Technical Report by the European Commission Joint Research Centre (JRC) aims to contribute to this movement for the establishment of a sound regulatory framework for AI, by making the connection between the principles embodied in current regulations regarding to the cybersecurity of digital systems and the protection of data, the policy activities concerning AI, and the technical discussions within the scientific community of AI, in particular in the field of machine learning, that is largely at the origin of the recent advancements of this technology. The individual objectives of this report are to provide a policy-oriented description of the current perspectives of AI and its implications in society, an objective view on the current landscape of AI, focusing of the aspects of robustness and explainability. This also include a technical discussion of the current risks associated with AI in terms of security, safety, and data protection, and a presentation of the scientific solutions that are currently under active development in the AI community to mitigate these risks. This report puts forward several policy-related considerations for the attention of policy makers to establish a set of standardisation and certification tools for AI. First, the development of methodologies to evaluate the impacts of AI on society, built on the model of the Data Protection Impact Assessments (DPIA) introduced in the General Data Protection Regulation (GDPR), is discussed. Secondly, a focus is made on the establishment of methodologies to assess the robustness of systems that would be adapted to the context of use. This would come along with the identification of known vulnerabilities of AI systems, and the technical solutions that have been proposed in the scientific community to address them. Finally, the aspects of transparency and explainability of AI are discussed, including the explainability-by-design approaches for AI models.
540			_aAll the authorized content except: Figure 17 on page 80, used under CC0 licence _bEuropean Union
650		0	_aTecnologías habilitadoras digitales _918
653			_aartificial intelligence
653			_adata protection
653			_ainformation security
653			_aregulation of telecommunications
700	1		_aJunklewitz, Henrik _93916
700	1		_aSánchez, Ignacio _93917
710	2		_aComisión Europea _9999
830			_pJRC Technical Reports _92965
856	4		_uhttps://op.europa.eu/en/publication-detail/-/publication/38750ca4-36a5-11ea-ba6e-01aa75ed71a1/language-en _x0 _yAcceso al documento
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999			_c5620 _d5620