mlsanityData to 7 Oct 2026

E-LENS: User Requirements-Oriented AI Ethics Assurance

arXiv 2503.04747 ↗First posted 6 Feb 2025cs.CY cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 49 references that are in the map. First posted on arXiv 6 Feb 2025.

Builds on

9 of 49 references.

References · 9

  1. 2019Advances and Open Problems in Federated Learningcited by 3,021
  2. 2018Datasheets for Datasetscited by 1,371
  3. 2018Model Cards for Model Reportingcited by 1,068
  4. 2018AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Biascited by 319
  5. 2019The role of artificial intelligence in achieving the Sustainable Development Goalscited by 100
  6. 2021Question-Driven Design Process for Explainable AI User Experiencescited by 31
  7. 2021Exploring the Assessment List for Trustworthy AI in the Context of Advanced Driver-Assistance Systemscited by 6
  8. 2020Security Assurance Cases -- State of the Art of an Emerging Approachcited by 4
  9. 2023A PRISMA-driven systematic mapping study on system assurance weakenerscited by 3

Built on by

Not cited yet.