# Collaborative Artificial Intelligence Needs Stronger Assurances Driven by Risks

arXiv: https://arxiv.org/abs/2112.00740 · HTML: https://mlsanity.com/paper/2112.00740/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 25 references that are in the map.
- First posted on arXiv 1 Dec 2021.

## Builds on (most cited first, 6 of 6)

- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Machine Learning Testing: Survey, Landscapes and Horizons](https://mlsanity.com/paper/1906.10742/) (2019)
- [The Ethics of AI Ethics -- An Evaluation of Guidelines](https://mlsanity.com/paper/1903.03425/) (2019)
- [Comparing Offline and Online Testing of Deep Neural Networks: An Autonomous Car Case Study](https://mlsanity.com/paper/1912.00805/) (2019)
- [Towards Risk Modeling for Collaborative AI](https://mlsanity.com/paper/2103.07460/) (2021)
- [Simulation-based Testing for Early Safety-Validation of Robot Systems](https://mlsanity.com/paper/2011.10294/) (2020)

## Built on by (most cited first, 2 of 2)

- [Green Resilience of Cyber-Physical Systems](https://mlsanity.com/paper/2311.05201/) (2023)
- [Effort-Optimized, Accuracy-Driven Labelling and Validation of Test Inputs for DL Systems: A Mixed-Integer Linear Programming Approach](https://mlsanity.com/paper/2507.04990/) (2025)

## Abstract (arXiv)

Collaborative AI systems (CAISs) aim at working together with humans in a shared space to achieve a common goal. This critical setting yields hazardous circumstances that could harm human beings. Thus, building such systems with strong assurances of compliance with requirements, domain-specific standards and regulations is of greatest importance. Only few scale impact has been reported so far for such systems since much work remains to manage possible risks. We identify emerging problems in this context and then we report our vision, as well as the progress of our multidisciplinary research team composed of software/systems, and mechatronics engineers to develop a risk-driven assurance process for CAISs.