E-LENS: User Requirements-Oriented AI Ethics Assurance

• URL: http://arxiv.org/abs/2503.04747v1
• Date: Thu, 06 Feb 2025 04:37:55 GMT
• Title: E-LENS: User Requirements-Oriented AI Ethics Assurance
• Authors: Jianlong Zhou, Fang Chen,
• Abstract summary: This paper introduces the concept of AI ethics assurance cases into the AI ethics assurance.<n>Three pillars of user requirements, evidence, and validation are proposed as key components and integrated into AI ethics assurance cases.<n>The user requirements-oriented AI ethics assurance case is set up based on three pillars and hazard analysis methods used in the safety assurance of safety-critical systems.
• Score: 7.3246584067312375
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite the much proliferation of AI ethical principles in recent years, there is a challenge of assuring AI ethics with current AI ethics frameworks in real-world applications. While system safety has emerged as a distinct discipline for a long time, originated from safety concerns in early aircraft manufacturing. The safety assurance is now an indispensable component in safety critical domains. Motivated by the assurance approaches for safety-critical systems such as aviation, this paper introduces the concept of AI ethics assurance cases into the AI ethics assurance. Three pillars of user requirements, evidence, and validation are proposed as key components and integrated into AI ethics assurance cases for a new approach of user requirements-oriented AI ethics assurance. The user requirements-oriented AI ethics assurance case is set up based on three pillars and hazard analysis methods used in the safety assurance of safety-critical systems. This paper also proposes a platform named Ethical-Lens (E-LENS) to implement the user requirements-oriented AI ethics assurance approach. The proposed user requirements-based E-LENS platform is then applied to assure AI ethics of an AI-driven human resource shortlisting system as a case study to show the effectiveness of the proposed approach.

Related papers

• Position: A taxonomy for reporting and describing AI security incidents [57.98317583163334]
  We argue that specific are required to describe and report security incidents of AI systems.<n>Existing frameworks for either non-AI security or generic AI safety incident reporting are insufficient to capture the specific properties of AI security.
  arXiv  Detail & Related papers  (2024-12-19T13:50:26Z)
• Landscape of AI safety concerns -- A methodology to support safety assurance for AI-based autonomous systems [0.0]
  AI has emerged as a key technology, driving advancements across a range of applications.<n>The challenge of assuring safety in systems that incorporate AI components is substantial.<n>We propose a novel methodology designed to support the creation of safety assurance cases for AI-based systems.
  arXiv  Detail & Related papers  (2024-12-18T16:38:16Z)
• Technology as uncharted territory: Contextual integrity and the notion of AI as new ethical ground [55.2480439325792]
  I argue that efforts to promote responsible and ethical AI can inadvertently contribute to and seemingly legitimize this disregard for established contextual norms. I question the current narrow prioritization in AI ethics of moral innovation over moral preservation.
  arXiv  Detail & Related papers  (2024-12-06T15:36:13Z)
• Engineering Trustworthy AI: A Developer Guide for Empirical Risk Minimization [53.80919781981027]
  Key requirements for trustworthy AI can be translated into design choices for the components of empirical risk minimization. We hope to provide actionable guidance for building AI systems that meet emerging standards for trustworthiness of AI.
  arXiv  Detail & Related papers  (2024-10-25T07:53:32Z)
• Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems [88.80306881112313]
  We will introduce and define a family of approaches to AI safety, which we will refer to as guaranteed safe (GS) AI. The core feature of these approaches is that they aim to produce AI systems which are equipped with high-assurance quantitative safety guarantees. We outline a number of approaches for creating each of these three core components, describe the main technical challenges, and suggest a number of potential solutions to them.
  arXiv  Detail & Related papers  (2024-05-10T17:38:32Z)
• Testing autonomous vehicles and AI: perspectives and challenges from cybersecurity, transparency, robustness and fairness [53.91018508439669]
  The study explores the complexities of integrating Artificial Intelligence into Autonomous Vehicles (AVs) It examines the challenges introduced by AI components and the impact on testing procedures. The paper identifies significant challenges and suggests future directions for research and development of AI in AV technology.
  arXiv  Detail & Related papers  (2024-02-21T08:29:42Z)
• POLARIS: A framework to guide the development of Trustworthy AI systems [3.02243271391691]
  There is a significant gap between high-level AI ethics principles and low-level concrete practices for AI professionals. We develop a novel holistic framework for Trustworthy AI - designed to bridge the gap between theory and practice. Our goal is to empower AI professionals to confidently navigate the ethical dimensions of Trustworthy AI.
  arXiv  Detail & Related papers  (2024-02-08T01:05:16Z)
• Responsible Design Patterns for Machine Learning Pipelines [10.184056098238765]
  AI ethics involves applying ethical principles to the entire life cycle of AI systems. This is essential to mitigate potential risks and harms associated with AI, such as biases. To achieve this goal, responsible design patterns (RDPs) are critical for Machine Learning (ML) pipelines.
  arXiv  Detail & Related papers  (2023-05-31T15:47:12Z)
• 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)
• 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)

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.