Intersectional-Bias-Assessment: Synthetic Clinical Data for Fairness Evaluation

Synthetic demographic and clinical data was created to train a classifier for diagnosing schizophrenia or depression. This dataset supports a tutorial on intersectional bias in model evaluation presented at the ACM FAccT 2022 conference. Author Marta Maslej released it under a Creative Commons license.

Use Cases

• Training classifiers for mental health diagnosis based on synthetic clinical and demographic features.
• Assessing model performance for intersectional bias across demographic subgroups.
• Reproducing or extending the methodology from the ACM FAccT 2022 tutorial on fairness in mental health care.

Strengths

• Dataset is explicitly designed for fairness and bias assessment in a critical domain.
• Associated with a peer-reviewed tutorial presented at a major conference (ACM FAccT 2022).
• Released under a permissive Creative Commons license (CC-4Y).

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment for large-scale training.
• Last update date is unknown; freshness unverified.

Provenance

Source

OpenML platform, author Marta Maslej.

Collection Method

Synthetically generated for model training and bias assessment.

Time Range

Temporal coverage is unknown.

Freshness

Last updated date is unknown.

Geography

Spatial coverage is unknown.