AI pitfalls and what not to do: mitigating bias in AI

Various forms of artificial intelligence (AI) applications are being deployed and used in many healthcare systems. As the use of these applications increases, we are learning the failures of these models and how they can perpetuate bias. With these new lessons, we need to prioritize bias evaluation and mitigation for radiology applications; all the while not ignoring the impact of changes in the larger enterprise AI deployment which may have downstream impact on performance of AI models. In this paper, we provide an updated review of known pitfalls causing AI bias and discuss strategies for mitigating these biases within the context of AI deployment in the larger healthcare enterprise. We describe these…

• NS
  National Science Foundation
• RS
  Radiological Society of North America

  Award: EIHD2204
• NI
  National Institutes of Health

  Awards: R01 EB017205, EB017205, 75N92020C00021, 75N92020C00008
• NI
  National Institute of Biomedical Imaging and Bioengineering

  Awards: 75N92020C00021, EB017205, 75N92020C00008, R01 EB017205
• NI
  National Institute on Minority Health and Health Disparities