Table 2:

Approaches to eliminate bias in AI/ML

Appropriate research expertise	Involve health equity experts in the conception, development, and deployment of AI/ML.
Diverse study populations	Diversify study populations to adequately represent marginalized populations in training datasets. Convenience samples such as datasets from electronic health records, claims data and so on, may not be adequately representative of marginalized groups.
Diverse study settings	Expand research locations to non-conventional settings where traditionally under-represented and vulnerable populations can be easily reached such as community health centers, faith-based organizations, barbershops, community service organizations, and other settings.
Regulatory measures	Determine fair, clear, specific and quantifiable regulatory measures of inequitable outcomes. Researchers should be required to report descriptive data on study populations by sex, race, ethnicity as long as privacy is protected. Standards should be consistent across regulatory bodies, peer-reviewed scientific journals and gastroenterology/hepatology professional societies.
Pre-deployment auditing	Mandate auditing processes and sensitivity analyses to assess algorithmic performance across subpopulations in the pre-deployment phases.
Post-deployment auditing	Establish auditing processes to assess algorithmic performance across subpopulations in the post-deployment phase and pathways for rapidly mitigating bias if discovered in the post-deployment phase.