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clinicalfair

Algorithmic Fairness Assessment for Clinical Prediction Models

Overview

clinicalfair is a post-hoc fairness auditing toolkit for clinical prediction models. It evaluates existing models by computing group-wise fairness metrics, visualizing disparities, and performing threshold-based mitigation – motivated by regulatory expectations for transparency in clinical AI.

Metrics: demographic parity, equalized odds, predictive parity, AUC, Brier score – with optional bootstrap confidence intervals
Visualization: disparity plots, ROC by group, calibration by group
Mitigation: group-specific threshold optimization with configurable grid resolution (default 0.01)
Intersectional: cross-tabulated analysis (race x sex x age) with automatic small-group filtering
Reporting: four-fifths rule violation detection

Fairness audit

Figure 1 | Fairness audit with threshold mitigation. (a) Group-wise selection rate, TPR, and FPR before and after equalized-odds threshold optimization. Mitigation reduces cross-group TPR disparity while maintaining acceptable accuracy. (b) ROC curves by racial group showing differential model performance. Data: COMPAS-style simulated recidivism predictions. Methods: Hardt et al. (2016); Obermeyer et al. (2019).

Why clinicalfair?

Existing R packages approach fairness from different angles:

Package	Focus	clinicalfair difference
`fairmodels`	Model-level fairness (wraps mlr3)	clinicalfair is model-agnostic: works with any predicted probabilities
`fairness`	Metric computation	clinicalfair adds threshold mitigation + intersectional analysis
`fairmetrics`	CIs for fairness metrics	clinicalfair also provides bootstrap CIs, plus audit reports with four-fifths rule screening

clinicalfair is designed for the clinical audit use case: a clinician or regulator receives a trained model and needs to answer “is this model fair across patient subgroups?” in one function call, with actionable output.

# One-line audit
fairness_report(fairness_data(predictions, labels, race))

Installation

# From GitHub:
devtools::install_github("CuiweiG/clinicalfair")

# After CRAN acceptance:
install.packages("clinicalfair")

Quick start

library(clinicalfair)
data(compas_sim)

# Create fairness evaluation object
fd <- fairness_data(
    predictions = compas_sim$risk_score,
    labels = compas_sim$recidivism,
    protected_attr = compas_sim$race
)

# Compute metrics
fairness_metrics(fd)

# With bootstrap confidence intervals
fairness_metrics(fd, ci = TRUE, n_boot = 2000)

# Generate audit report
fairness_report(fd)

# Mitigate via threshold optimization
threshold_optimize(fd, objective = "equalized_odds")

Calibration by group

Figure 2 | Calibration curves by racial group. Each point represents a decile bin; point size proportional to sample count. Deviation from the diagonal indicates miscalibration. Differential calibration across groups is a key fairness concern identified by Obermeyer et al. (2019) Science 366:447.

Functions

Function	Description
`fairness_data()`	Bundle predictions, labels, protected attributes
`fairness_metrics()`	Compute group-wise fairness metrics (optional bootstrap CIs)
`fairness_report()`	Generate audit report with four-fifths rule screening
`threshold_optimize()`	Group-specific threshold mitigation (configurable grid resolution)
`intersectional_fairness()`	Cross-tabulated multi-attribute analysis (small-group filtering)
`autoplot()`	Disparity bar plots (S3 method)
`plot_roc()`	ROC curves by protected group
`plot_calibration()`	Calibration curves by group

Key references

Obermeyer Z et al. (2019). Dissecting racial bias in an algorithm. Science 366:447. doi:10.1126/science.aax2342
Hardt M et al. (2016). Equality of Opportunity in Supervised Learning. NeurIPS.
FDA (2021). Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) Action Plan.

License

MIT

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.