AI Governance by Human Rights-Centred Design, Deliberation and Oversight: An End to Ethics Washing

Yeung, Karen; Howes, Andrew; Pogrebna, Ganna

doi:10.2139/ssrn.3435011

Cited by 47 publications

(54 citation statements)

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“…For Actionable Principles this could take the form of significant agile engagement with multiple cross-sectoral stakeholders outside of the group, consulting them on their expertise, insights and unique point of view. Elsewhere, such an approach has also been proposed for 'human rights-centred design, deliberation and oversight' of AI (Yeung et al 2019 ). It ensures that a much larger diversity of voices is captured that otherwise would be missing in the process, regardless of the composition or size of the group.…”

Section: Multi-stakeholder Participation and Cross-sectoral Feedbackmentioning

confidence: 99%

“…Guidance in the form of a toolbox, or method to operationalise the recommendations can be a crucial step to move from AI Ethics Principles towards Actionable Principles (Morley et al 2019 ). The benefit of providing existing and desired tools is equally taken up by proponents of a human rights frameworks based approach with the expectation that these can be adapted to ensure an integration of human rights norms within the AI lifecycle (Yeung et al 2019 ).…”

Section: Multi-stakeholder Participation and Cross-sectoral Feedbackmentioning

confidence: 99%

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Actionable Principles for Artificial Intelligence Policy: Three Pathways

Stix

2021

Sci Eng Ethics

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In the development of governmental policy for artificial intelligence (AI) that is informed by ethics, one avenue currently pursued is that of drawing on “AI Ethics Principles”. However, these AI Ethics Principles often fail to be actioned in governmental policy. This paper proposes a novel framework for the development of ‘Actionable Principles for AI’. The approach acknowledges the relevance of AI Ethics Principles and homes in on methodological elements to increase their practical implementability in policy processes. As a case study, elements are extracted from the development process of the Ethics Guidelines for Trustworthy AI of the European Commission’s “High Level Expert Group on AI”. Subsequently, these elements are expanded on and evaluated in light of their ability to contribute to a prototype framework for the development of 'Actionable Principles for AI'. The paper proposes the following three propositions for the formation of such a prototype framework: (1) preliminary landscape assessments; (2) multi-stakeholder participation and cross-sectoral feedback; and, (3) mechanisms to support implementation and operationalizability.

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Section: Multi-stakeholder Participation and Cross-sectoral Feedbackmentioning

confidence: 99%

Section: Multi-stakeholder Participation and Cross-sectoral Feedbackmentioning

confidence: 99%

Actionable Principles for Artificial Intelligence Policy: Three Pathways

Stix

2021

Sci Eng Ethics

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“…One governance approach is to require an ethical impact assessment for any AI system that is considered risky. 33 Yeung et al (2019) propose an approach modeled after the 'safety case' framework used for regulating safety of offshore oil rigs. This approach, which is similar to the risk management framework imposed on financial institutions under the Solvency II Directive, 34 consists of developing a comprehensive risk assessment, listing all the possible things that could go wrong for the AI system -including societal and environmental harms -and assessing the severity of harm for each possible bad event, and the probability of each bad event occurring.…”

Section: Ai Impact Assessments and An Independent Review Committeementioning

confidence: 99%

Flexible and Context-Specific AI Explainability: A Multidisciplinary Approach

et al. 2020

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The recent enthusiasm for artificial intelligence (AI) is due principally to advances in deep learning. Deep learning methods are remarkably accurate, but also opaque, which limits their potential use in safety-critical applications. To achieve trust and accountability, designers and operators of machine learning algorithms must be able to explain the inner workings, the results and the causes of failures of algorithms to users, regulators, and citizens. The originality of this paper is to combine technical, legal and economic aspects of explainability to develop a framework for defining the "right" level of explainability in a given context. We propose three logical steps: First, define the main contextual factors, such as who the audience of the explanation is, the operational context, the level of harm that the system could cause, and the legal/regulatory framework. This step will help characterize the operational and legal needs for explanation, and the corresponding social benefits. Second, examine the technical tools available, including post hoc approaches (input perturbation, saliency maps...) and hybrid AI approaches. Third, as function of the first two steps, choose the right levels of global and local explanation outputs, taking into the account the costs involved. We identify seven kinds of costs and emphasize that explanations are socially useful only when total social benefits exceed costs. arXiv:2003.07703v1 [cs.CY]

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“…As the total quantification of human and social experience remains a long-term dream, the labour of those required to develop AI -and those affected by its application -will remain central to the discussion of its ethics, governance, and policy. In this context, and because of the lack of enforcement of ethical principles of AI, many of these principles currently operate under the immense influence of corporate entities in their development (Yeung et al 2020). Through the International Labour Organization, United Nations has issued several conventions on human rights in working contexts relating to labour issues.…”

Section: Towards Human Rights-based Ai Labour Principlesmentioning

confidence: 99%

From Development to Deployment: For a Comprehensive Approach to Ethics of Ai and Labour

Posada

2020

SPIR

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In recent years, government and policy organizations, private companies, and research agencies have been discussing the potential disruption caused by the deployment of AI systems in working environments. This paper traces contemporary discourse on the relationship between artificial intelligence and labour and discusses how these principles must be comprehensive in their approach to labour and AI. First, the paper asserts that ethical frameworks in AI alone are not enough to guarantee the rights of workers since they lack enforcement mechanisms and the participation of worker organizations. Secondly, it argues that current discussions on AI and labour focus on the deployment of these technologies in the workplace but ignore the essential role of human labour in their development, particularly in the different cases of outsourced labour around the world. Finally, the paper recommends the use of already existing human rights frameworks on working conditions – notably the International Labour Organization conventions on the right of collective bargaining, the abolition of discrimination at work, and the right to equal remuneration – as a basis for a more comprehensive ethical framework on AI labour. It concludes by arguing that the central question regarding the future of work will not be whether intelligent machines will replace humans, but who will own and have a say on the systems that will ultimately work alongside humans.

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AI Governance by Human Rights-Centred Design, Deliberation and Oversight: An End to Ethics Washing

Cited by 47 publications

References 0 publications

Actionable Principles for Artificial Intelligence Policy: Three Pathways

Actionable Principles for Artificial Intelligence Policy: Three Pathways

Flexible and Context-Specific AI Explainability: A Multidisciplinary Approach

From Development to Deployment: For a Comprehensive Approach to Ethics of Ai and Labour

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