首页|A scoping review of fair machine learning techniques when using real-world data
A scoping review of fair machine learning techniques when using real-world data
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – According to news reporting based on a preprint abstract, our journalists obtained the following quote sourced from me drxiv.org: “Objective: The integration of artificial intelligence (AI) and machine learning (ML) in health care to aid clinical decisions is widespread. “However, as AI and ML take important roles in health care, there are concerns a bout AI and ML associated fairness and bias. “That is, an AI tool may have a disparate impact, with its benefits and drawback s unevenly distributed across societal strata and subpopulations, potentially ex acerbating existing health inequities. Thus, the objectives of this scoping revi ew were to summarize existing literature and identify gaps in the topic of tackl ing algorithmic bias and optimizing fairness in AI/ML models using real-world da ta (RWD) in health care domains. Methods: We conducted a thorough review of tech niques for assessing and optimizing AI/ML model fairness in health care when usi ng RWD in health care domains. The focus lies on appraising different quantifica tion metrics for accessing fairness, publicly accessible datasets for ML fairnes s research, and bias mitigation approaches.
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NSTL
