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