# Assessing Trustworthiness of Autonomous Systems

arXiv: https://arxiv.org/abs/2305.03411 · HTML: https://mlsanity.com/paper/2305.03411/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 87 references that are in the map.
- First posted on arXiv 5 May 2023.

## Builds on (most cited first, 18 of 18)

- [Black-box Explanation of Object Detectors via Saliency Maps](https://mlsanity.com/paper/2006.03204/) (2020)
- [Trustworthy AI](https://mlsanity.com/paper/2011.02272/) (2020)
- [The social dilemma of autonomous vehicles](https://mlsanity.com/paper/1510.03346/) (2015)
- [Guidance on the Assurance of Machine Learning in Autonomous Systems (AMLAS)](https://mlsanity.com/paper/2102.01564/) (2021)
- [A Review of Vibration-Based Damage Detection in Civil Structures: From Traditional Methods to Machine Learning and Deep Learning Applications](https://mlsanity.com/paper/2004.04373/) (2020)
- [The Role of Explainability in Assuring Safety of Machine Learning in Healthcare](https://mlsanity.com/paper/2109.00520/) (2021)
- [White Paper Machine Learning in Certified Systems](https://mlsanity.com/paper/2103.10529/) (2021)
- [Towards a Framework for Certification of Reliable Autonomous Systems](https://mlsanity.com/paper/2001.09124/) (2020)
- [Brittle AI, Causal Confusion, and Bad Mental Models: Challenges and Successes in the XAI Program](https://mlsanity.com/paper/2106.05506/) (2021)
- [On Specifying for Trustworthiness](https://mlsanity.com/paper/2206.11421/) (2022)
- [Guidance on the Safety Assurance of Autonomous Systems in Complex Environments (SACE)](https://mlsanity.com/paper/2208.00853/) (2022)
- [A Corroborative Approach to Verification and Validation of Human--Robot Teams](https://mlsanity.com/paper/1608.07403/) (2016)
- [Formalizing and Guaranteeing* Human-Robot Interaction](https://mlsanity.com/paper/2006.16732/) (2020)
- [Quantitative AI Risk Assessments: Opportunities and Challenges](https://mlsanity.com/paper/2209.06317/) (2022)
- [Towards Explainability of Machine Learning Models in Insurance Pricing](https://mlsanity.com/paper/2003.10674/) (2020)
- [CyRes -- Avoiding Catastrophic Failure in Connected and Autonomous Vehicles (Extended Abstract)](https://mlsanity.com/paper/2006.14890/) (2020)
- [Integrating Formal Verification and Simulation-based Assertion Checking in a Corroborative V&V Process](https://mlsanity.com/paper/2208.05273/) (2022)
- [Architecting Safer Autonomous Aviation Systems](https://mlsanity.com/paper/2301.08138/) (2023)

## Built on by (most cited first, 1 of 1)

- [SEED-SET: Scalable Evolving Experimental Design for System-level Ethical Testing](https://mlsanity.com/paper/2603.01630/) (2026)

## 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.