Ethical Assurance: A practical approach to the responsible design, development, and deployment of data-driven technologies

• URL: http://arxiv.org/abs/2110.05164v1
• Date: Mon, 11 Oct 2021 11:21:49 GMT
• Title: Ethical Assurance: A practical approach to the responsible design, development, and deployment of data-driven technologies
• Authors: Christopher Burr and David Leslie
• Abstract summary: Article offers contributions to the interdisciplinary project of responsible research and innovation in data science and AI. First, it provides a critical analysis of current efforts to establish practical mechanisms for algorithmic assessment. Second, it provides an accessible introduction to the methodology of argument-based assurance. Third, it establishes a novel version of argument-based assurance that we call 'ethical assurance'
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This article offers several contributions to the interdisciplinary project of responsible research and innovation in data science and AI. First, it provides a critical analysis of current efforts to establish practical mechanisms for algorithmic assessment, which are used to operationalise normative principles, such as sustainability, accountability, transparency, fairness, and explainability, in order to identify limitations and gaps with the current approaches. Second, it provides an accessible introduction to the methodology of argument-based assurance, and explores how it is currently being applied in the development of safety cases for autonomous and intelligent systems. Third, it generalises this method to incorporate wider ethical, social, and legal considerations, in turn establishing a novel version of argument-based assurance that we call 'ethical assurance'. Ethical assurance is presented as a structured means for unifying the myriad practical mechanisms that have been proposed, as it is built upon a process-based form of project governance that supports inclusive and participatory ethical deliberation while also remaining grounded in social and technical realities. Finally, it sets an agenda for ethical assurance, by detailing current challenges, open questions, and next steps, which serve as a springboard to build an active (and interdisciplinary) research programme as well as contribute to ongoing discussions in policy and governance.

Related papers

• Fair by design: A sociotechnical approach to justifying the fairness of AI-enabled systems across the lifecycle [0.8164978442203773]
  Fairness is one of the most commonly identified ethical principles in existing AI guidelines. The development of fair AI-enabled systems is required by new and emerging AI regulation.
  arXiv  Detail & Related papers  (2024-06-13T12:03:29Z)
• Towards Responsible AI in Banking: Addressing Bias for Fair Decision-Making [69.44075077934914]
  "Responsible AI" emphasizes the critical nature of addressing biases within the development of a corporate culture. This thesis is structured around three fundamental pillars: understanding bias, mitigating bias, and accounting for bias. In line with open-source principles, we have released Bias On Demand and FairView as accessible Python packages.
  arXiv  Detail & Related papers  (2024-01-13T14:07:09Z)
• Report of the 1st Workshop on Generative AI and Law [78.62063815165968]
  This report presents the takeaways of the inaugural Workshop on Generative AI and Law (GenLaw) A cross-disciplinary group of practitioners and scholars from computer science and law convened to discuss the technical, doctrinal, and policy challenges presented by law for Generative AI.
  arXiv  Detail & Related papers  (2023-11-11T04:13:37Z)
• Predictable Artificial Intelligence [77.1127726638209]
  This paper introduces the ideas and challenges of Predictable AI. It explores the ways in which we can anticipate key validity indicators of present and future AI ecosystems. We argue that achieving predictability is crucial for fostering trust, liability, control, alignment and safety of AI ecosystems.
  arXiv  Detail & Related papers  (2023-10-09T21:36:21Z)
• The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practice [64.29355073494125]
  This article aims to ground what we dub the "participatory turn" in AI design by synthesizing existing theoretical literature on participation. We articulate empirical findings concerning the current state of participatory practice in AI design based on an analysis of recently published research and semi-structured interviews with 12 AI researchers and practitioners.
  arXiv  Detail & Related papers  (2023-10-02T05:30:42Z)
• Ethics in conversation: Building an ethics assurance case for autonomous AI-enabled voice agents in healthcare [1.8964739087256175]
  The principles-based ethics assurance argument pattern is one proposal in the AI ethics landscape. This paper presents the interim findings of a case study applying this ethics assurance framework to the use of Dora, an AI-based telemedicine system.
  arXiv  Detail & Related papers  (2023-05-23T16:04:59Z)
• A multidomain relational framework to guide institutional AI research and adoption [0.0]
  We argue that research efforts aimed at understanding the implications of adopting AI tend to prioritize only a handful of ideas. We propose a simple policy and research design tool in the form of a conceptual framework to organize terms across fields.
  arXiv  Detail & Related papers  (2023-03-17T16:33:01Z)
• Active Inference in Robotics and Artificial Agents: Survey and Challenges [51.29077770446286]
  We review the state-of-the-art theory and implementations of active inference for state-estimation, control, planning and learning. We showcase relevant experiments that illustrate its potential in terms of adaptation, generalization and robustness.
  arXiv  Detail & Related papers  (2021-12-03T12:10:26Z)
• Achieving a Data-driven Risk Assessment Methodology for Ethical AI [3.523208537466128]
  We show that a multidisciplinary research approach is the foundation of a pragmatic definition of ethical and societal risks faced by organizations using AI. We propose a novel data-driven risk assessment methodology, entitled DRESS-eAI.
  arXiv  Detail & Related papers  (2021-11-29T12:55:33Z)
• An interdisciplinary conceptual study of Artificial Intelligence (AI) for helping benefit-risk assessment practices: Towards a comprehensive qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
  This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence. The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
  arXiv  Detail & Related papers  (2021-05-07T12:01:31Z)
• An Ecosystem Approach to Ethical AI and Data Use: Experimental Reflections [0.0]
  This paper offers a methodology to identify the needs of AI practitioners when it comes to confronting and resolving ethical challenges. We offer a grassroots approach to operational ethics based on dialog and mutualised responsibility.
  arXiv  Detail & Related papers  (2020-12-27T07:41:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.