The pervasive application of algorithmic decision-making is raising concerns on the risk of unintended bias in AI systems deployed in critical settings such as healthcare. The detection and mitigation of biased models is a very delicate task which should be tackled with care and involving domain experts in the loop. In this paper we introduce FairLens, a methodology for discovering and explaining biases. We show how our tool can be used to audit a fictional commercial black-box model acting as a clinical decision support system. In this scenario, the healthcare facility experts can use FairLens on their own historical data to discover the model's biases before incorporating it into the clinical decision flow. FairLens first stratifies the available patient data according to attributes such as age, ethnicity, gender and insurance; it then assesses the model performance on such subgroups of patients identifying those in need of expert evaluation. Finally, building on recent state-of-the-art XAI (eXplainable Artificial Intelligence) techniques, FairLens explains which elements in patients' clinical history drive the model error in the selected subgroup. Therefore, FairLens allows experts to investigate whether to trust the model and to spotlight group-specific biases that might constitute potential fairness issues.
From Statistical Fidelity to Clinical Consistency: Scalable Generation and Auditing of Synthetic Patient Trajectories
The University of Queensland（昆士兰大学）；The University of New South Wales（新南威尔士大学）；Shahid Sadoughi University of Medical Sciences（沙希德·萨多格伊医学科学与健康服务大学）；The University of Auckland（奥克兰大学）
University of California, Los Angeles（加利福尼亚大学洛杉矶分校）
Auditing machine learning-based (ML) healthcare tools for bias is critical to preventing patient harm, especially in communities that disproportionately face health inequities. General frameworks are becoming increasingly available to measure ML fairness gaps between groups. However, ML for health (ML4H) auditing principles call for a contextual, patient-centered approach to model assessment. Therefore, ML auditing tools must be (1) better aligned with ML4H auditing principles and (2) able to illuminate and characterize communities vulnerable to the most harm. To address this gap, we propose s…
Explaining Deep Learning for ECG Analysis: Building Blocks for Auditing and Knowledge Discovery
Fraunhofer Heinrich Hertz Institute（弗劳恩霍夫海因里希·赫兹研究所）；Physikalisch-Technische Bundesanstalt（德国联邦物理技术研究院）；Charité Universitätsmedizin Berlin（柏林夏里特医学院）；Carl von Ossietzky Universität Oldenburg（奥尔登堡卡尔·冯·奥西茨基大学）
MedRLM: Recursive Multimodal Health Intelligence for Long-Context Clinical Reasoning, Sensor-Guided Screening, Evidence-Grounded Decision Support, and Community-to-Tertiary Referral Optimization
School of Information, Computer；Communication Technology Sirindhorn International Institute of Technology, Thammasat University Pathum Thani, Thailand 1
