How to Mitigate Algorithmic Bias in Healthcare
Artificial intelligence (AI) has the promise to revolutionize healthcare with machine learning (ML) techniques to predict patient outcomes and personalize patient care, but use of AI carries legal risks, including algorithmic bias, that can affect outcomes and care.
AI seeks to enable computers to imitate intelligent human behavior, and ML, a subset of AI, involves systems that learn from data without relying on rules-based programming. Among others, ML techniques include supervised learning (a method of teaching ML algorithms to "learn" by example) as well as deep learning (a subset of ML that abstracts complex concepts through layers mimicking neural networks of biological systems). Trained ML algorithms can identify causes of diseases by detecting relationships between a set of inputs, such as weight, height, and blood pressure, and an output, such as the likelihood of developing heart disease.
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Before proceeding, please note: If you are not a current client of Perkins Coie, please do not include any information in this e-mail that you or someone else considers to be of a confidential or secret nature. Perkins Coie has no duty to keep confidential any of the information you provide. Neither the transmission nor receipt of your information is considered a request for legal advice, securing or retaining a lawyer. An attorney-client relationship with Perkins Coie or any lawyer at Perkins Coie is not established until and unless Perkins Coie agrees to such a relationship as memorialized in a separate writing.