Fairness

The Fairness principle states that medical AI tools should maintain the same performance across individuals and groups of individuals (including under-represented and disadvantaged groups). AI-driven medical care should be provided equally for all citizens, independently of their sex, gender, ethnicity, age, socio-economic status and (dis)abilities, among other attributes. Fair medical AI tools should be developed such that potential AI biases are minimised as much as possible, or identified and reported.

To this end, three recommendations for Fairness are defined in the FUTURE-AI framework. First, AI developers together with domain experts should define fairness for their specific use case and make an inventory of potential sources of bias (Fairness 1). Accordingly, to facilitate verification of AI fairness and non-discrimination, information on the subjects’ relevant attributes should be included in the datasets (Fairness 2). Finally, whenever this data is available, the development team should apply bias detection and correction methods, to obtain the best possible trade-off between fairness and accuracy (Fairness 3).

Recommendation	Practical steps	Examples of approaches and methods	Stage
Fairness 1. Define sources of bias	Identify potential biases Analyse impact of biases Prioritise biases to address	Group attributes (e.g. sex, gender, age, ethnicity) Medical profiles (e.g. comorbidities, disabilities) Human and technical biases (e.g. data acquisition, labelling, curation)	Design
Fairness 2. Collect information on individual and data attributes	Define relevant attributes Implement data collection Ensure ethical compliance Handle missing data	Demographic information Medical profile data Data provenance information Imputation techniques	Development
Fairness 3. Evaluate fairness	Apply bias detection methods Calculate fairness metrics Test bias mitigation measures Document remaining biases	True Positive Rates Statistical Parity Group Fairness Equalised Odds	Evaluation