mlsanityData to 7 Oct 2026

Assessing Trustworthiness of Autonomous Systems

arXiv 2305.03411 ↗First posted 5 May 2023cs.AI cs.CY cs.SEcited by 1

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Abstract (arXiv)

As Autonomous Systems (AS) become more ubiquitous in society, more responsible for our safety and our interaction with them more frequent, it is essential that they are trustworthy. Assessing the trustworthiness of AS is a mandatory challenge for the verification and development community. This will require appropriate standards and suitable metrics that may serve to objectively and comparatively judge trustworthiness of AS across the broad range of current and future applications. The meta-expression `trustworthiness' is examined in the context of AS capturing the relevant qualities that comprise this term in the literature. Recent developments in standards and frameworks that support assurance of autonomous systems are reviewed. A list of key challenges are identified for the community and we present an outline of a process that can be used as a trustworthiness assessment framework for AS.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 18 of its 87 references that are in the map. First posted on arXiv 5 May 2023.

Builds on

18 of 87 references.

References · 18

  1. 2020Black-box Explanation of Object Detectors via Saliency Mapscited by 74
  2. 2020Trustworthy AIcited by 62
  3. 2015The social dilemma of autonomous vehiclescited by 47
  4. 2021Guidance on the Assurance of Machine Learning in Autonomous Systems (AMLAS)cited by 40
  5. 2020A Review of Vibration-Based Damage Detection in Civil Structures: From Traditional Methods to Machine Learning and Deep Learning Applicationscited by 33
  6. 2021The Role of Explainability in Assuring Safety of Machine Learning in Healthcarecited by 26
  7. 2021White Paper Machine Learning in Certified Systemscited by 15
  8. 2020Towards a Framework for Certification of Reliable Autonomous Systemscited by 12
  9. 2021Brittle AI, Causal Confusion, and Bad Mental Models: Challenges and Successes in the XAI Programcited by 11
  10. 2022On Specifying for Trustworthinesscited by 8
  11. 2022Guidance on the Safety Assurance of Autonomous Systems in Complex Environments (SACE)cited by 7
  12. 2016A Corroborative Approach to Verification and Validation of Human--Robot Teamscited by 6
6 more
  1. 2020Formalizing and Guaranteeing* Human-Robot Interactioncited by 6
  2. 2022Quantitative AI Risk Assessments: Opportunities and Challengescited by 4
  3. 2020Towards Explainability of Machine Learning Models in Insurance Pricingcited by 3
  4. 2020CyRes -- Avoiding Catastrophic Failure in Connected and Autonomous Vehicles (Extended Abstract)cited by 1
  5. 2022Integrating Formal Verification and Simulation-based Assertion Checking in a Corroborative V&V Processcited by 1
  6. 2023Architecting Safer Autonomous Aviation Systemscited by 1

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Citing papers per year: 2026: 1.

1 paper.