Bias Detection & Mitigation in Transformer Models
Fine-tuning DistilBERT for hate-speech detection with fairness diagnostics
A study of representational fairness in transformer-based text classifiers. As my major contribution to this group project, I fine-tuned DistilBERT on the Jigsaw dataset to detect hate speech, then applied the FHI365 fairness framework to measure and mitigate bias across demographic groups.
The resulting model achieved an F1-score of 0.68 while surfacing a 22% demographic-parity gap, illustrating the tension between predictive performance and group fairness in content-moderation models.
As lead author, I presented these findings on representational fairness at SSaLM: Social Science & Language Models at the Weizenbaum Institute in April 2025.
Stack: Python, PyTorch, DistilBERT, fairness diagnostics.
Code: Colab notebook. Coursework: Deep Learning