# Identifying Roles, Requirements and Responsibilities in Trustworthy AI Systems

arXiv: https://arxiv.org/abs/2106.08258 · HTML: https://mlsanity.com/paper/2106.08258/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jun 2021.

## Built on by (most cited first, 4 of 4)

- [Requirements Engineering for Machine Learning: A Review and Reflection](https://mlsanity.com/paper/2210.00859/) (2022)
- [How Mature is Requirements Engineering for AI-based Systems? A Systematic Mapping Study on Practices, Challenges, and Future Research Directions](https://mlsanity.com/paper/2409.07192/) (2024)
- [A Design Framework for operationalizing Trustworthy Artificial Intelligence in Healthcare: Requirements, Tradeoffs and Challenges for its Clinical Adoption](https://mlsanity.com/paper/2504.19179/) (2025)
- [Algorithmic Transparency and Manipulation](https://mlsanity.com/paper/2311.13286/) (2023)

## Abstract (arXiv)

Artificial Intelligence (AI) systems are being deployed around the globe in critical fields such as healthcare and education. In some cases, expert practitioners in these domains are being tasked with introducing or using such systems, but have little or no insight into what data these complex systems are based on, or how they are put together. In this paper, we consider an AI system from the domain practitioner's perspective and identify key roles that are involved in system deployment. We consider the differing requirements and responsibilities of each role, and identify a tension between transparency and privacy that needs to be addressed so that domain practitioners are able to intelligently assess whether a particular AI system is appropriate for use in their domain.