Artificial Intelligence, Governance and Ethics: Global Perspectives

Daly, Angela; Hagendorff, Thilo; Li, Hui; Mann, Monique; Marda, Vidushi; Wagner, Ben; Wang, Wayne Wei; Witteborn, Saskia

doi:10.2139/ssrn.3414805

Cited by 48 publications

(30 citation statements)

References 30 publications

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“…An international European initiative is the multi-stakeholder European Union High-Level Expert Group on Artificial Intelligence, which is composed by 52 experts from academia, civil society, and industry. The group produced a deliverable on the required criteria for AI trustworthiness (Daly, 2019). Even articles 21 and 22 of the recent European Union General Data Protection Regulation include passages functional to AI governance, although further action has been recently demanded from the European Parliament ( De Sutter, 2019).…”

Section: Guidelines and Secondary Literature On Ai Ethics Its Dimensmentioning

confidence: 99%

Ethical principles in machine learning and artificial intelligence: cases from the field and possible ways forward

Piano

2020

Humanit Soc Sci Commun

147

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Decision-making on numerous aspects of our daily lives is being outsourced to machinelearning (ML) algorithms and artificial intelligence (AI), motivated by speed and efficiency in the decision process. ML approaches-one of the typologies of algorithms underpinning artificial intelligence-are typically developed as black boxes. The implication is that ML code scripts are rarely scrutinised; interpretability is usually sacrificed in favour of usability and effectiveness. Room for improvement in practices associated with programme development have also been flagged along other dimensions, including inter alia fairness, accuracy, accountability, and transparency. In this contribution, the production of guidelines and dedicated documents around these themes is discussed. The following applications of AIdriven decision-making are outlined: (a) risk assessment in the criminal justice system, and (b) autonomous vehicles, highlighting points of friction across ethical principles. Possible ways forward towards the implementation of governance on AI are finally examined.

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Section: Guidelines and Secondary Literature On Ai Ethics Its Dimensmentioning

confidence: 99%

Ethical principles in machine learning and artificial intelligence: cases from the field and possible ways forward

Piano

2020

Humanit Soc Sci Commun

147

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“…53 National Governance Committee for the New Generation Artificial Intelligence (2019) Governance principles of the new generation artificial intelligence -developing responsible artificial intelligence. 54 Daly et al (2019). 55 Kozuka (2019), p. 322.…”

Section: Asian Comparisonmentioning

confidence: 99%

On the Governance of Artificial Intelligence through Ethics Guidelines

Larsson

2020

AsianJLS

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This article uses a socio-legal perspective to analyze the use of ethics guidelines as a governance tool in the development and use of artificial intelligence (AI). This has become a central policy area in several large jurisdictions, including China and Japan, as well as the EU, focused on here. Particular emphasis in this article is placed on the Ethics Guidelines for Trustworthy AI published by the EU Commission’s High-Level Expert Group on Artificial Intelligence in April 2019, as well as the White Paper on AI, published by the EU Commission in February 2020. The guidelines are reflected against partially overlapping and already-existing legislation as well as the ephemeral concept construct surrounding AI as such. The article concludes by pointing to (1) the challenges of a temporal discrepancy between technological and legal change, (2) the need for moving from principle to process in the governance of AI, and (3) the multidisciplinary needs in the study of contemporary applications of data-dependent AI.

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“…Following Jonathan Tucker (2012b), one can distinguish between more or less stringent governance measures, ranging from statutory regulations or reporting requirements to security guidelines or pre-publication reviews up to codes of conduct, transparency measures or risk education and general awareness raising with regard to the dual-use nature of machine learning techniques (Minehata and Sture 2010). Although a bunch of international and national governance approaches (Daly et al 2019) as well as legal norms already exist, regulating complexes like privacy, data protection, security, confidentiality, environmental protection, armament, labeling and many more, numerous areas of machine learning research and development are unregulated and in need of legal enactment (Calo 2017). At the same time, one also has to keep in mind that the enactment of laws which are supposed to deal with complex dynamics in technology development, where potential harms can only be foreseen through vague technology assessment (Collingridge 1980), bears the risk of stifling innovations or smothering promising technologies in an early stage.…”

Section: Governing Forbidden Knowledgementioning

confidence: 99%

Forbidden knowledge in machine learning reflections on the limits of research and publication

Hagendorff

2020

AI & Soc

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Certain research strands can yield "forbidden knowledge". This term refers to knowledge that is considered too sensitive, dangerous or taboo to be produced or shared. Discourses about such publication restrictions are already entrenched in scientific fields like IT security, synthetic biology or nuclear physics research. This paper makes the case for transferring this discourse to machine learning research. Some machine learning applications can very easily be misused and unfold harmful consequences, for instance, with regard to generative video or text synthesis, personality analysis, behavior manipulation, software vulnerability detection and the like. Up till now, the machine learning research community embraces the idea of open access. However, this is opposed to precautionary efforts to prevent the malicious use of machine learning applications. Information about or from such applications may, if improperly disclosed, cause harm to people, organizations or whole societies. Hence, the goal of this work is to outline deliberations on how to deal with questions concerning the dissemination of such information. It proposes a tentative ethical framework for the machine learning community on how to deal with forbidden knowledge and dual-use applications.

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Artificial Intelligence, Governance and Ethics: Global Perspectives

Cited by 48 publications

References 30 publications

Ethical principles in machine learning and artificial intelligence: cases from the field and possible ways forward

Ethical principles in machine learning and artificial intelligence: cases from the field and possible ways forward

On the Governance of Artificial Intelligence through Ethics Guidelines

Forbidden knowledge in machine learning reflections on the limits of research and publication

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