Tackling algorithmic bias in health care

Artificial intelligence (AI) holds great promise for improving health care and public health. By leveraging and processing large amounts of data at far greater speeds than humans, AI can generate predictions that can inform policy or treatment decisions. But as predictive algorithms in medicine and public health increase and the fields rely on them more, policymakers, data scientists, ethicists, and industry leaders must work together to develop best practices for addressing algorithmic bias. Algorithmic bias occurs when AI tools systemically make predictions that are discriminatory against groups of people. The potential problems stemming from algorithmic bias are well documented. For instance, as a result of algorithmic bias, a former hiring algorithm that Amazon used taught itself that male applicants were preferable to women. This was because the data used to develop the algorithms showed that most people who submitted resumes for software developer jobs were males. To inform discussions about how to address algorithmic bias, we are sharing lessons we learned from our participation in a national AI competition. (author abstract)