FAIRS — A Framework for Evaluating the Inclusion of Sex in Clinical Algorithms

The FAIRS framework (Framework for Algorithm Inclusion Review for Sex) is designed to evaluate the ethical, legal, and clinical appropriateness of including sex as a variable in clinical algorithms. Clinical algorithms significantly influence patient care decisions, including eligibility for critical medical resources. While race-based variables have recently been excluded from many algorithms due to concerns of discrimination, sex remains widely used, raising questions about fairness and equity. FAIRS consists of three key evaluative questions: whether sex is prognostically necessary, the mechanisms behind its predictive value, and whether its inclusion leads to inequitable treatment. The framework emphasizes that sex should only be incorporated when its presence substantially improves algorithm performance and is biologically driven rather than influenced by biases or disparities in care. FAIRS aims to provide guidance for regulators, clinicians, and health organizations to create fair, unbiased, and legally sound algorithmic tools in medicine.

The FAIRS framework (Framework for Algorithm Inclusion Review for Sex) is designed to evaluate the ethical, legal, and clinical appropriateness of including sex as a variable in clinical algorithms. Clinical algorithms significantly influence patient care decisions, including eligibility for critical medical resources. While race-based variables have recently been excluded from many algorithms due to concerns of discrimination, sex remains widely used, raising questions about fairness and equity. FAIRS consists of three key evaluative questions: whether sex is prognostically necessary, the mechanisms behind its predictive value, and whether its inclusion leads to inequitable treatment. The framework emphasizes that sex should only be incorporated when its presence substantially improves algorithm performance and is biologically driven rather than influenced by biases or disparities in care. FAIRS aims to provide guidance for regulators, clinicians, and health organizations to create fair, unbiased, and legally sound algorithmic tools in medicine.