# Advancing Trustworthy AI for Sustainable Development: Recommendations for Standardising AI Incident Reporting

arXiv: https://arxiv.org/abs/2501.14778 · HTML: https://mlsanity.com/paper/2501.14778/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 34 references that are in the map.
- First posted on arXiv 1 Jan 2025.

## Builds on (most cited first, 5 of 5)

- [Trustworthy AI: From Principles to Practices](https://mlsanity.com/paper/2110.01167/) (2021)
- [Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims](https://mlsanity.com/paper/2004.07213/) (2020)
- [The role of artificial intelligence in achieving the Sustainable Development Goals](https://mlsanity.com/paper/1905.00501/) (2019)
- [Preventing Repeated Real World AI Failures by Cataloging Incidents: The AI Incident Database](https://mlsanity.com/paper/2011.08512/) (2020)
- [Filling gaps in trustworthy development of AI](https://mlsanity.com/paper/2112.07773/) (2021)

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

- [Standardised schema and taxonomy for AI incident databases in critical digital infrastructure](https://mlsanity.com/paper/2501.17037/) (2025)
- [A five-layer framework for AI governance: integrating regulation, standards, and certification](https://mlsanity.com/paper/2509.11332/) (2025)
- [Incorporating AI incident reporting into telecommunications law and policy: Insights from India](https://mlsanity.com/paper/2509.09508/) (2025)
- [A federated architecture for sector-led AI governance: lessons from India](https://mlsanity.com/paper/2603.26865/) (2026)

## Abstract (arXiv)

The increasing use of AI technologies has led to increasing AI incidents, posing risks and causing harm to individuals, organizations, and society. This study recognizes and addresses the lack of standardized protocols for reliably and comprehensively gathering such incident data crucial for preventing future incidents and developing mitigating strategies. Specifically, this study analyses existing open-access AI-incident databases through a systematic methodology and identifies nine gaps in current AI incident reporting practices. Further, it proposes nine actionable recommendations to enhance standardization efforts to address these gaps. Ensuring the trustworthiness of enabling technologies such as AI is necessary for sustainable digital transformation. Our research promotes the development of standards to prevent future AI incidents and promote trustworthy AI, thus facilitating achieving the UN sustainable development goals. Through international cooperation, stakeholders can unlock the transformative potential of AI, enabling a sustainable and inclusive future for all.