AI Mismatches: Identifying Potential Algorithmic Harms Before AI Development
This addresses the challenge of preventing unintended harms in AI development for practitioners and stakeholders, though it appears incremental as it builds on existing risk management concepts.
The paper tackles the problem of AI systems failing to deliver expected performance and causing harm, known as 'AI Mismatches', by proposing an approach to anticipate and mitigate risks early in development, based on an analysis of 774 AI cases and seven matrices to map critical factors.
AI systems are often introduced with high expectations, yet many fail to deliver, resulting in unintended harm and missed opportunities for benefit. We frequently observe significant "AI Mismatches", where the system's actual performance falls short of what is needed to ensure safety and co-create value. These mismatches are particularly difficult to address once development is underway, highlighting the need for early-stage intervention. Navigating complex, multi-dimensional risk factors that contribute to AI Mismatches is a persistent challenge. To address it, we propose an AI Mismatch approach to anticipate and mitigate risks early on, focusing on the gap between realistic model performance and required task performance. Through an analysis of 774 AI cases, we extracted a set of critical factors, which informed the development of seven matrices that map the relationships between these factors and highlight high-risk areas. Through case studies, we demonstrate how our approach can help reduce risks in AI development.