AI incident reporting systems can effectively manage the multiplicity of incidents by implementing a structured approach that categorizes and clusters similar incidents while preserving the unique details of each event. One effective strategy is the introduction of "AI incident variants," which allows for the identification and cataloging of incidents that share common causative factors and produce similar harms. This approach enables the system to group related incidents under a single umbrella while still maintaining detailed records of individual harm events.
To achieve this, reporting systems should utilize robust metadata frameworks that capture essential attributes of each incident, such as the specific AI system involved, the context of deployment, and the nature of the harm experienced. By employing taxonomies like the CSET AI Harm Taxonomy and the Goals, Methods, and Failures (GMF) Taxonomy, incident reporting systems can provide a nuanced understanding of the incidents, distinguishing between tangible and intangible harms, and identifying the underlying causes of failures.
Moreover, leveraging advanced data analytics and machine learning techniques can enhance the ability to detect patterns across incidents, allowing for more efficient categorization and analysis. This dual approach of clustering similar incidents while maintaining detailed records of individual cases ensures that the reporting system remains comprehensive and informative, ultimately contributing to the prevention of future AI-related harms.