From Biased Data Inputs To Your Discriminatory Diagnosis Outputs: A Review of Legal Liability For Artificial Intelligence in Healthcare

Authors

Amber Bolden, Notre Dame Law School

Abstract

While health disparities in America occur due to non-medical circumstances, certain protected classes experience healthcare disparities due to the biases of medical professionals. Biased diagnoses, both intentional or unintentional, have existed throughout the history of the medical profession. That those biases are becoming data for training algorithms raises concerns as the medical field increasingly incorporates and standardizes artificial and augmented intelligence in patient diagnosis and treatment. Currently unregulated but with lifedetermining potential, artificial intelligence (AI) when used in patient treatment leads to important questions: should the doctor, the provider, or the AI developers be liable, and for what? Section II discusses how bias manifests in healthcare and the legal avenues available for negligent, non-AI diagnosis and treatments. Section III then addresses how AI outputs can perpetuate discriminatory care and how negligent AI fits into the current legal framework. Finally, Section IV proposes how resulting biased AI diagnosis and treatments should be legally handlined moving forward.

Recommended Citation

Amber Bolden, From Biased Data Inputs To Your Discriminatory Diagnosis Outputs: A Review of Legal Liability For Artificial Intelligence in Healthcare, 30 Mich. Tech. L. Rev. (2024).
Available at: https://repository.law.umich.edu/mtlr/vol30/iss2/3