Zhijing Jin (she/her) is a Ph.D. at Max Planck Institute & ETH. Her research focuses on socially responsible NLP by causal inference. Specifically, she works on expanding the impact of NLP by promoting NLP for social good, and developing CausalNLP to improve robustness, fairness, and interpretability of NLP models, as well as analyze the causes of social problems. She has published at many NLP and AI venues (e.g., ACL, EMNLP, NAACL, NeurIPS, AAAI, AISTATS). Her work has been featured in MIT News, ACM TechNews, and Synced. She is actively involved in AI for social good, as the organizer of NLP for Positive Impact Workshops at ACL 2021, EMNLP 2022, and EMNLP 2024, Moral AI Workshop at NeurIPS 2023, and RobustML Workshop at ICLR 2021. To support the NLP research community, she organizes the ACL Year-Round Mentorship Program. To foster the causality research community, she organized the Tutorial on CausalNLP at EMNLP 2022, and served as the Publications Chair for the 1st conference on Causal Learning and Reasoning (CLeaR). More information can be found on her personal website: zhijing-jin.com
Traditionally, research in social science requires a large amount of effort to conduct human studies and accurate data. With the latest advancement in natural language processing (NLP), especially large language models (LLMs), we see a methodology revolution for traditional social science research. In this talk, I will introduce the use of NLP and LLMs on various aspects of computational social science research. First, I will introduce NLP as a tool to distill key information from massive text, such as social media, news articles, and other sources. Based on the data processed by NLP, we can then conduct causal analysis to answer important questions, such as the cause and effect of policies. Furthermore, LLMs can also be a study subject of itself, for which we can probe the moral bias and political bias, as well as understanding how certain decisions are made.
Jan Kinne is a postdoc in the research area “Economics of Innovation and Business Dynamics” at the Center for European Economic Research and a postdoctoral fellow at the Center for Geographic Analysis at Harvard University. He studied geoinformatics at the University of Heidelberg and received his PhD from the University of Salzburg on the topic of “web-based innovation indicators for microgeographic analyses”. He continues to conduct methodological research around the use of (text-based) web data for innovation research. He is also the founder and CEO of ISTARI.AI. The company specializes in AI-based analysis of enterprise web data.