| 2026 | AAAI | Bias Association Discovery Framework for Open-Ended LLM Generations. | Jinhao Pan, Chahat Raj, Ziwei Zhu |
| 2026 | ACL | Talent or Luck? Evaluating Attribution Bias in Large Language Models. | Chahat Raj, Mahika Banerjee, Jinhao Pan, Aylin Caliskan, Antonios Anastasopoulos, Ziwei Zhu |
| 2026 | ACL | VIGNETTE: Socially Grounded Bias Evaluation for Vision-Language Models. | Chahat Raj, Bowen Wei, Aylin Caliskan, Antonios Anastasopoulos, Ziwei Zhu |
| 2025 | EMNLP | Toward Inclusive Language Models: Sparsity-Driven Calibration for Systematic and Interpretable Mitigation of Social Biases in LLMs. | Prommy Sultana Hossain, Chahat Raj, Ziwei Zhu, Jessica Lin, Emanuela Marasco |
| 2025 | EMNLP | What's Not Said Still Hurts: A Description-Based Evaluation Framework for Measuring Social Bias in LLMs. | Jinhao Pan, Chahat Raj, Ziyu Yao, Ziwei Zhu |
| 2024 | AIES | Breaking Bias, Building Bridges: Evaluation and Mitigation of Social Biases in LLMs via Contact Hypothesis. | Chahat Raj, Anjishnu Mukherjee, Aylin Caliskan, Antonios Anastasopoulos, Ziwei Zhu |
| 2024 | ECIR | SALSA: Salience-Based Switching Attack for Adversarial Perturbations in Fake News Detection Models. | Chahat Raj, Anjishnu Mukherjee, Hemant Purohit, Antonios Anastasopoulos, Ziwei Zhu |
| 2024 | EMNLP | BiasDora: Exploring Hidden Biased Associations in Vision-Language Models. | Chahat Raj, Anjishnu Mukherjee, Aylin Caliskan, Antonios Anastasopoulos, Ziwei Zhu |
| 2023 | AIES | True and Fair: Robust and Unbiased Fake News Detection via Interpretable Machine Learning. | Chahat Raj, Anjishnu Mukherjee, Ziwei Zhu |
| 2023 | EMNLP | Global Voices, Local Biases: Socio-Cultural Prejudices across Languages. | Anjishnu Mukherjee, Chahat Raj, Ziwei Zhu, Antonios Anastasopoulos |