Sometimes it is about winning the big contract. Other times it is about challenging your team to develop strategic and novel solutions that can make a big and lasting impact. The OS AI team recently noted that, out of a crowded field that included some of the top minds from across academia and the private sector, two of top Federal health firms – iAdeptive and CVP – have been recognized as winners for the National Institute of Health’s recent Minimizing Bias and Maximizing Long-Term Accuracy, Utility and Generalizability of Predictive Algorithms in Health Care Challenge.
Background
The Minimizing Bias and Maximizing Long-Term Accuracy, Utility and Generalizability of Predictive Algorithms in Health Care Challenge seeks to encourage the development of bias-detection and -correction tools that foster “good algorithmic practice” and mitigate the risk of unwitting bias in clinical decision support.
As AI/ML algorithms are increasingly used in health care systems, accuracy, generalizability and avoidance of bias and drift become more important. Bias primarily surfaces in two forms. Predictive bias is seen in algorithmic inaccuracies that produce estimates that significantly differ from the underlying truth. Social bias reflects systemic inequities in care delivery leading to suboptimal health outcomes for certain populations.
The goal of this Challenge was to identify and minimize inadvertent amplification and perpetuation of systemic biases in AI/ML algorithms used as CDS through the development of predictive and social bias-detection and -correction tools. For this Challenge, participants across academia and the private sector were invited to design a bias-detection and -correction tool.
Winner Spotlight
iAdeptive Challenge Submission: ESRD Bias Detection and Mitigation
Team iAdeptive Members: Sujatha Subramanian, Jo Stigall, Tenzin Jordan Shawa, Jagadish Mohan, Senthil K. Ranganathan
CVP Challenge Submission: Debiaser – AI Bias Detection and Mitigation Tool for Clinical Decision Making
Team CVP Members: Manpreet Khural, Wei Chien, Lauren Winstead, Cal Zemelman
Read more about the challenge, goals, and winners here.
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