Advancing Trustworthy AI for Sustainable Development: Recommendations for Standardising AI Incident Reporting
Avinash Agarwal, Manisha J Nene
TL;DR
This paper addresses the absence of standardized AI incident reporting and data sharing, which hampers learning from past failures and undermines trust in AI for sustainable development. It systematically analyzes open-access AI incident databases (AIID and AIAAIC) to identify nine gaps and proposes nine concrete recommendations to standardize definitions, data fields, and sharing mechanisms. The contributions include a taxonomy-powered framework for incident categorization, interoperability-oriented data structures, and inclusive reporting practices supported by international cooperation. The work aims to accelerate trustworthy AI deployment and SDG achievement by enabling transparent, cross-border incident analysis and mitigation strategies.
Abstract
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.