Malihe Alikhani is an assistant professor of AI and social justice at the Khoury School of Computer Science, Northeastern University. Her research goal is to design inclusive and equitable language technologies. This involves developing systems that communicate effectively with diverse populations, especially those from underserved communities. By integrating insights from cognitive science and social sciences with machine learning, she designs computational models that capture the diversity of interpretation and reflect the success of language as a communicative tool. She aspires to discern and address biases in learning models, especially in applications such as education, health, and social justice. This aspiration has led her to collaborate with educators, healthcare experts, and community leaders to create inclusive technology-enabled experiences.

Talk title: Towards Inclusive and Equitable Language Technologies

Talk abstract: With the increasing deployment of language technologies to users, understanding the impact of natural language processing models on society and behaviors is critical. We aim to design culturally responsible, equitable, and inclusive technologies that serve a diverse population. This talk provides an overview of our projects in democratizing Large Language Models, focusing on making language technologies accessible and equitable for education, health, and social justice. We explore two case studies: 1) developing discourse-aware models for inclusive social media moderation, and 2) creating equitable machine learning frameworks for multimodal communication.

Kevin Leyton-Brown is a professor of Computer Science and a Distinguished University Scholar at the University of British Columbia. He also holds a Canada CIFAR AI Chair at the Alberta Machine Intelligence Institute and is an associate member of the Vancouver School of Economics. He received a PhD and an M.Sc. from Stanford University (2003; 2001) and a B.Sc. from McMaster University (1998). He studies artificial intelligence, mostly at the intersection of machine learning and (1) the design and operation of electronic markets and (2) the design of heuristic algorithms.

Talk title: Rationality Report Cards: Assessing the Economic Rationality of Large Language Models

Talk abstract: There is increasing interest in using LLMs as decision-making ``agents''. Doing so includes many degrees of freedom: which model should be used; how should it be prompted; should it be asked to introspect, conduct chain-of-thought reasoning, etc? Settling these questions---and more broadly, determining whether an LLM agent is reliable enough to be trusted---requires a methodology for assessing such an agent's economic rationality. In this paper, we provide one. We begin by surveying the economic literature on rational decision-making, taxonomizing a large set of fine-grained ``skills'' that an agent should exhibit, along with dependencies between them. We then propose a benchmark distribution that quantitatively scores these skills and, combined with a user-provided rubric, produces ``rationality report card''. Finally, we describe the results of a large-scale empirical experiment with 21 different LLMs, characterizing the both current state of the art and the impact of different model sizes on models' ability to exhibit rational behavior.

Niloufar Salehi is an assistant professor in the School of Information at UC, Berkeley. She studies human-computer interaction, with her research spanning education to healthcare to restorative justice. Her research interests are social computing, participatory and critical design, human-centered AI, and more broadly, human-computer interaction (HCI). Her work has been published and received awards in premier venues including ACM CHI and CSCW and has been covered in Venture Beat, Wired, and the Guardian. She is a W. T. Grant Foundation scholar. She received her PhD in computer science from Stanford University in 2018.

Diyi Yang is an Assistant Professor in Computer Science at Stanford University. Professor Yang's research interests are Computational Social Science and Natural Language Processing. Her research goal is to understand the social aspects of language and then build socially aware NLP systems to better support human-human and human-computer interaction. Professor Yang received her PhD from the School of Computer Science, Carnegie Mellon University, and her bachelor's degree from Shanghai Jiao Tong University, China. Her work has received multiple best paper nominations or awards at ICWSM, EMNLP, SIGCHI, ACL, and CSCW. She is a recipient of Forbes 30 under 30 in Science, IEEE “AI 10 to Watch”, the Intel Rising Star Faculty Award, Microsoft Research Faculty Fellowship, and NSF CAREER Award.