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AI technologies are being increasingly tested and applied in criticalenvironments including healthcare. Without an effective way to detect andmitigate AI induced inequalities, AI might do more harm than good, potentiallyleading to the widening of underlying inequalities. This paper proposes ageneric allocation-deterioration framework for detecting and quantifying AIinduced inequality. Specifically, AI induced inequalities are quantified as thearea between two allocation-deterioration curves. To assess the framework'sperformance, experiments were conducted on ten synthetic datasets (N>33,000)generated from HiRID - a real-world Intensive Care Unit (ICU) dataset, showingits ability to accurately detect and quantify inequality proportionally tocontrolled inequalities. Extensive analyses were carried out to quantify healthinequalities (a) embedded in two real-world ICU datasets; (b) induced by AImodels trained for two resource allocation scenarios. Results showed thatcompared to men, women had up to 33% poorer deterioration in markers ofprognosis when admitted to HiRID ICUs. All four AI models assessed were shownto induce significant inequalities (2.45% to 43.2%) for non-White compared toWhite patients. The models exacerbated data embedded inequalities significantlyin 3 out of 8 assessments, one of which was >9 times worse.  The codebase is at https://github.com/knowlab/DAindex-Framework.

Quantifying Health Inequalities Induced by Data and AI Models