Lessons for Editors of AI Incidents from the AI Incident Database

TL;DR

This paper analyzes the AI Incident Database to identify challenges in incident classification and reporting, proposing mitigations to improve robustness and inform future AI incident reporting practices.

Contribution

It provides an in-depth review of the AIID dataset, identifies structural ambiguities and uncertainties in incident reporting, and suggests improvements for incident classification and documentation.

Findings

Structural ambiguities challenge incident classification

Epistemic uncertainty in reporting is unavoidable

Proposed mitigations enhance robustness of incident processes

Abstract

As artificial intelligence (AI) systems become increasingly deployed across the world, they are also increasingly implicated in AI incidents - harm events to individuals and society. As a result, industry, civil society, and governments worldwide are developing best practices and regulations for monitoring and analyzing AI incidents. The AI Incident Database (AIID) is a project that catalogs AI incidents and supports further research by providing a platform to classify incidents for different operational and research-oriented goals. This study reviews the AIID's dataset of 750+ AI incidents and two independent taxonomies applied to these incidents to identify common challenges to indexing and analyzing AI incidents. We find that certain patterns of AI incidents present structural ambiguities that challenge incident databasing and explore how epistemic uncertainty in AI incident reporting is unavoidable. We therefore report mitigations to make incident processes more robust to uncertainty related to cause, extent of harm, severity, or technical details of implicated systems. With these findings, we discuss how to develop future AI incident reporting practices.