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Quantifying Health Inequalities Induced by Data and AI Models

Wu, Honghan; Wang, Minhong; Sylolypavan, Aneeta; Wild, Sarah; (2022) Quantifying Health Inequalities Induced by Data and AI Models. In: Proceedings of the 31st International Joint Conference on Artificial Intelligence. (pp. pp. 5192-5198). IJCA: Vienna, Austria. Green open access

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Abstract

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.

Type: Proceedings paper
Title: Quantifying Health Inequalities Induced by Data and AI Models
Event: The 31st International Joint Conference on Artificial Intelligence
Open access status: An open access version is available from UCL Discovery
Publisher version: https://www.ijcai.org/proceedings/2022/
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
URI: https://discovery.ucl.ac.uk/id/eprint/10148214
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