These tools and metrics are designed to help AI actors develop and use trustworthy AI systems and applications that respect human rights and are fair, transparent, explainable, robust, secure and safe.
Equal outcomes
In the field of health, equal patient outcomes refers to the assurance that protected groups have equal benefit in terms of patient outcomes from the deployment of machine-learning models. A weak form of equal outcomes is ensuring that both the protected and non-protected groups benefit similarly from a model (equal benefit); a stronger form is making sure that both groups benefit and any outcome disparity is lessened (equalized outcomes). Ensuring equal outcomes is the most critical aspect of fairness and can be advanced by interventions proactively designed to reduce disparities (34, 35). It may be hard to know in advance, though, if any well-intentioned general, non-tailored intervention, whether a quality improvement project or a machine-learning system, might disproportionately harm or benefit a protected group. However, besides equal outcomes, other options that might advance health equity can be analyzed and addressed prospectively.
About the metric
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