Synthetic Multi-Scenario Dataset for Fairness Evaluation in E-Recruitment

A synthetic dataset simulating candidate profiles for technology jobs in a Brazilian context, designed for benchmarking fairness-aware algorithms. It contains nine partitions combining three sizes (1k, 5k, 10k instances) and three bias conditions (debiased, biased, extreme bias). The dataset was authored by Carvalho and last updated on 2026-05-11.

Use Cases

• Algorithmic fairness auditing based on parametric control over bias injection
• Scalability benchmarking of bias mitigation techniques across dataset sizes (1k, 5k, 10k)
• Multi-objective re-ranking evaluation using technical features and demographic attributes
• Explainability (XAI) evaluation in recruitment systems based on suitability scores and bias penalties

Strengths

• Explicit parametric control over bias injection across multiple demographic dimensions
• Nine distinct partitions combining three dataset sizes (1k, 5k, 10k) and three bias conditions
• Includes three sensitive attributes (gender, race, location) relevant to the Brazilian context

Limitations

• Column-level documentation is absent; field semantics must be inferred after download
• Row count is unknown for each specific partition, which may limit suitability assessment
• Data is synthetic and may not fully capture real-world recruitment dynamics

Provenance

Source

figshare

Collection Method

Synthetically generated for research purposes.

Freshness

Last updated 2026-05-11 06:02:48; freshness should be verified

Geography

Brazil (demographic attributes follow IBGE classification)