Galstyan, Aram

A Survey on Bias and Fairness in Machine Learning

Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2019) · arXiv

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 5,812

A Survey on Bias and Fairness in Machine Learning

Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2022) · ACM Computing Surveys

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 5,812

Opinion Dynamics with Confirmation Bias

Allahverdyan, A. E., & Galstyan, A. (2014) · PLoS ONE

Opinion Dynamics and Social InfluenceDecision-Making and Behavioral Economics

Cited by 90

“I’m fully who I am”: Towards Centering Transgender and Non-Binary Voices to Measure Biases in Open Language Generation

Ovalle, A., Goyal, P., Dhamala, J., Jaggers, Z., Chang, K.-W., Galstyan, A., Zemel, R., & Gupta, R. (2023) · 2023 ACM Conference on Fairness Accountability and Transparency

Ethics and Social Impacts of AILGBTQ Health, Identity, and Policy

Cited by 43

Context Length Alone Hurts LLM Performance Despite Perfect Retrieval

Du, Y., Tian, M., Ronanki, S., Rongali, S., Bodapati, S. B., Galstyan, A., Wells, A., Schwartz, R., Huerta, E. A., & Peng, H. (2025) · Findings of the Association for Computational Linguistics: EMNLP 2025

Topic ModelingInformation Retrieval and Search Behavior

Cited by 39

Quantifying machine influence over human forecasters

Abeliuk, A., Benjamin, D. M., Morstatter, F., & Galstyan, A. (2020) · Scientific Reports

Forecasting Techniques and ApplicationsHuman-Automation Interaction and Safety

Cited by 18

The First Workshop on Personalized Generative AI @ CIKM 2023: Personalization Meets Large Language Models

Chen, Z., Jiang, Z., Yang, F., He, Z., Hou, Y., Cho, E., McAuley, J., Galstyan, A., Hu, X., & Yang, J. (2023) · Proceedings of the 32nd ACM International Conference on Information and Knowledge Management

Recommender Systems and TechniquesTopic Modeling

Cited by 5