Executive Summary
The fourth white paper in the Beyond Point Solutions: A Win–Win–Win Framework for Embedding AI into Oncology series examines one of the most critical dimensions of responsible AI adoption — bias across the AI pipeline.
Bias is not a single failure point; it is a pipeline-wide vulnerability. It begins with incomplete or unrepresentative data, deepens through flawed labeling and model optimization, and reemerges post-deployment through how AI recommendations are presented to clinicians.
Each stage introduces distinct risks — from invisible language and imaging proxies that encode systemic disparities to thresholding decisions that favor aggregate accuracy over subgroup equity.
For healthcare organizations, addressing bias is no longer a compliance exercise; it is a patient-safety imperative.
This paper builds upon the evaluation framework introduced in White Paper 3, showing how sensitivity, specificity, calibration, and discrimination must be stratified across demographic subgroups to reveal hidden inequities. It also introduces “fair-use auditing” and human-AI interaction design as practical tools to mitigate bias amplification in real-world workflows.
Authored By: Padmasri Bhetanabhotla
