mlsanityData to 7 Oct 2026

On Specifying for Trustworthiness

arXiv 2206.11421 ↗First posted 22 Jun 2022cs.AI cs.ROcited by 8

Not on the map. Why?

Abstract (arXiv)

As autonomous systems (AS) increasingly become part of our daily lives, ensuring their trustworthiness is crucial. In order to demonstrate the trustworthiness of an AS, we first need to specify what is required for an AS to be considered trustworthy. This roadmap paper identifies key challenges for specifying for trustworthiness in AS, as identified during the "Specifying for Trustworthiness" workshop held as part of the UK Research and Innovation (UKRI) Trustworthy Autonomous Systems (TAS) programme. We look across a range of AS domains with consideration of the resilience, trust, functionality, verifiability, security, and governance and regulation of AS and identify some of the key specification challenges in these domains. We then highlight the intellectual challenges that are involved with specifying for trustworthiness in AS that cut across domains and are exacerbated by the inherent uncertainty involved with the environments in which AS need to operate.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 5 of its 54 references that are in the map. First posted on arXiv 22 Jun 2022.

Builds on

5 of 54 references.

References · 5

  1. 2020Underspecification Presents Challenges for Credibility in Modern Machine Learningcited by 488
  2. 2021The Role of Explainability in Assuring Safety of Machine Learning in Healthcarecited by 26
  3. 2020Towards a Framework for Certification of Reliable Autonomous Systemscited by 12
  4. 2020Formalizing and Guaranteeing* Human-Robot Interactioncited by 6
  5. 2022Trustworthy Autonomous Systems (TAS): Engaging TAS experts in curriculum designcited by 2

Built on by

Cited by year

Citing papers per year: 2023: 4; 2024: 2; 2025: 2.

8 papers.