BiasGuard Pro is a research framework for identifying and auditing gendered stereotypes in professional and career recommendation text. The system is designed to support human interpretation and judgment in fairness-sensitive contexts, rather than to provide automated enforcement or decision-making. The framework integrates a lightweight bias detection model with interpretable explanations and interaction-level design principles drawn from Human-Centered AI (HCAI) and Explainable AI (XAI). BiasGuard Pro explores how fairness auditing can be designed as an interactive human reasoning workflow rather than a static classification output.
Career recommendation systems increasingly mediate access to professional opportunities. While effective at personalization, these systems can reproduce subtle gendered stereotypes embedded in training data and linguistic conventions. Existing bias detection tools often prioritize benchmark performance while offering limited support for:
- Interpretability
- Trust calibration
- Human-in-the-loop reasoning
Most bias detection systems focus on determining whether content is biased. BiasGuard Pro emphasizes how humans reason about bias signals once they are surfaced. For each input, the system:
- Estimates the likelihood of gendered bias
- Identifies influential linguistic features
- Generates counterfactual alternatives illustrating potential mitigation
- Preserves human authority over interpretation and action This design supports reflective analysis rather than categorical judgment.
BiasGuard Pro consists of:
- A lightweight transformer-based bias detector optimized for efficiency and interpretability
- Token-level attribution to support causal reasoning about language
- Counterfactual explanations enabling "what-if" analysis
- An interaction model based on progressive disclosure to reduce cognitive overload and automation bias
- A research-grade evaluation pipeline spanning performance, fairness, and interpretability
- In-domain F1 score: 0.828
- Disparity Gap: 0.041
- Bias Amplification: 1.06
- Average inference latency: ~12 ms (CPU-only) Results indicate that interpretability and fairness robustness can be achieved without sacrificing efficiency.
An interactive demonstration of the auditing interface is available:
🔗 Hugging Face Space
https://huggingface.co/spaces/Dyra1204/BiasGuard-Pro
The full research paper documenting the theoretical foundations, system design, empirical evaluation, and ethical analysis of BiasGuard Pro is available for reading and download:
Readers seeking full technical, methodological, and ethical detail may consult:
- Research overview:
docs/RESEARCH_README.md - System architecture:
docs/ARCHITECTURE.md - Data construction and limitations:
docs/DATA.md - Empirical evaluation:
docs/RESULTS.md - Explainability design:
docs/EXPLAINABILITY.md - Interaction rationale:
docs/INTERFACE.md - Ethical considerations:
docs/ETHICS.md - Reproducibility Guide:
docs/REPRODUCIBILITY.md
- Fairness-aware NLP research
- Human-centered AI studies
- Bias auditing in professional language systems
- Automated moderation or enforcement
- High-stakes decision-making without human oversight
- Direct deployment as a standalone decision system
Project Status: Completed research
Primary Domain: Career recommendation and professional discourse
Design Orientation: Human-centered, interpretability-first fairness auditing