Learning Confound

The learning confound is the second of Klein's three diagnostic categories for evaluating claims of AI superiority over experts. The confound operates at the structural level of study design: the AI system is designed to learn from the data, given access to large datasets and computational resources to identify patterns no human could detect through unaided cognition. The human experts, by contrast, are typically evaluated on their existing knowledge without comparable learning opportunities. They are not shown the data the algorithm learned from. They are not given time to study the patterns the algorithm identified. They are tested cold, on their clinical judgment as it stands at the moment of evaluation, against a system optimized specifically for the task at hand. The comparison's unfairness is so stark Klein argues it should disqualify the conclusions drawn from it — but such studies are regularly published, cited, and used to justify organizational decisions about AI deployment and reduction of human expert involvement.