Determining the scope of AI incident reporting involves navigating complex ethical and legal considerations, especially when addressing aggregate or societal-level harms. One primary ethical concern is the need for transparency and accountability in AI systems. Reporting frameworks must ensure that incidents causing societal harm, even if not directly linked to technical failures, are documented and analyzed to foster public trust and promote responsible AI governance.
Legally, the definitions of harm in various jurisdictions, such as those outlined in the EU AI Act and OECD guidelines, must be considered. These definitions often emphasize the need to identify harm to individuals, rights, property, or the environment. However, societal-level harms, such as those resulting from algorithmic bias or misinformation, may not fit neatly into these categories. This raises questions about how to classify and report such incidents without undermining the legal frameworks in place.
Furthermore, there is a need to balance the rights of individuals with the collective interests of society. Reporting systems must be designed to capture the nuances of aggregate harms while ensuring that individual privacy and data protection laws are upheld. This may involve anonymizing data or aggregating information to prevent the identification of specific individuals affected by broader societal issues.
Ultimately, the ethical and legal considerations in AI incident reporting necessitate a comprehensive approach that recognizes the interconnectedness of individual and societal harms, ensuring that all relevant incidents are captured and addressed in a manner that promotes accountability and ethical AI practices.