Including race in clinical algorithms can both reduce and increase health inequities – it depends on what doctors use them for

• Health practitioners are increasingly concerned that because race is a social construct, and the biological mechanisms of how race affects clinical outcomes are often unknown, including race in predictive algorithms for clinical decision-making may worsen inequities.
• A higher eGFR value means better kidney health.
• My recently published research suggests that excluding race from certain diagnostic algorithms could worsen health inequities.

Different approaches to fairness

• Researchers use different economic frameworks to understand how society allocates resources.
• This approach allocates resources to those with the most opportunities to generate positive outcomes or mitigate negative ones.
• Although utilitarian approaches do not take fairness into account, an approach that does would ask two questions: How do we define fairness?
• Are there conditions when maximizing an algorithm’s prediction power and accuracy would not conflict with fairness?

Equality of opportunity

• There are two fundamental principles in equality of opportunity.
• However, differences in individual effort that occur because of circumstances, such as living in an area with limited access to healthy food, are not addressed under equality of opportunity.
• Equality of opportunity implies that if algorithms were to be used for clinical decision-making, then it is necessary to understand what causes variation in the predictions they make.

Evaluating clinical algorithms for fairness

• To hold machine learning and other artificial intelligence algorithms accountable to a standard of equity, I applied the principles of equality of opportunity to
  evaluate whether race should be included in clinical algorithms.
• The first, diagnostic algorithms, makes predictions based on outcomes that have already occurred at the time of decision-making.
• The second, prognostic algorithms, predicts future outcomes that have not yet occurred at the time of decision-making.
• For example, prognostic algorithms are used to predict whether a patient will live if they do or do not obtain a kidney transplant.

Unanswered questions and future work

• My colleagues and I are exploring many of these unanswered questions to reduce algorithmic discrimination.
• We believe our work will readily extend to other areas outside of health, including education, crime and labor markets.