Standardised schema and taxonomy for AI incident databases in critical digital infrastructure

TL;DR

This paper introduces a standardized schema and taxonomy for AI incident databases in critical digital infrastructure to improve incident data collection, analysis, and response, thereby enhancing safety, transparency, and accountability.

Contribution

It develops a unified schema and taxonomy for AI incident data, including new fields like severity, causes, and harms, to enable consistent and detailed incident documentation.

Findings

Facilitates more effective incident data collection and analysis

Supports evidence-based policymaking and safety measures

Lays foundation for global AI incident response coordination

Abstract

The rapid deployment of Artificial Intelligence (AI) in critical digital infrastructure introduces significant risks, necessitating a robust framework for systematically collecting AI incident data to prevent future incidents. Existing databases lack the granularity as well as the standardized structure required for consistent data collection and analysis, impeding effective incident management. This work proposes a standardized schema and taxonomy for AI incident databases, addressing these challenges by enabling detailed and structured documentation of AI incidents across sectors. Key contributions include developing a unified schema, introducing new fields such as incident severity, causes, and harms caused, and proposing a taxonomy for classifying AI incidents in critical digital infrastructure. The proposed solution facilitates more effective incident data collection and analysis, thus supporting evidence-based policymaking, enhancing industry safety measures, and promoting transparency. This work lays the foundation for a coordinated global response to AI incidents, ensuring trust, safety, and accountability in using AI across regions.