• Yongfeng Zhang

Congratulations to Prof. Yongfeng Zhang for receiving the Faculty Early Career Development (CAREER) Award from the National Science Foundation (NSF) for his project titled "CAREER: Towards Conversational Recommendation Systems: Explainability, Fairness, and Human-in-the-Loop Learning". The award is from October 2021 to September 2026 with the total awarded amount $550,000.

The Faculty Early Career Development (CAREER) Program is an NSF foundation-wide activity that offers the National Science Foundation's most prestigious awards in support of early-career faculty who have the potential to serve as academic role models in research and education and to lead advances in the mission of their department or organization.

The project aims to develop explainable, fairness-aware, trustworthy and responsible AI systems. Recent advances in Artificial Intelligence AI have accumulated a rich toolbox of models for information retrieval, natural language processing and personalized recommendation. By optimizing over benchmark datasets, many of the models were developed with an algorithmic consideration instead of putting human at the central consideration. However, the ultimate goal of AI is to serve humans, collaborate with humans, and, ultimately, benefit humans. As a result, algorithmic approaches to AI must put humans in the loop for model design, implementation and validation. This project focuses on conversational AI, a promising approach towards putting humans in the loop, which enables direct conversation between human and AI for model learning. In particular, the project explores conversational recommender systems to help users in information seeking and decision making. It will develop explainable and fairness-aware algorithms for conversational recommendation. Presentation of the work and demos will help to engage with wider audiences that are interested in computational research. By integrating transparency and fairness principles into computer science courses on areas such as Information Retrieval, Data Mining and Artificial Intelligence, results from the project will educate students to understand how AI can be not only useful but also socially responsible.

This project will develop a general framework for conversational recommendation that bridges natural language understanding and dialog state management. With the framework, the project will explore three directions. The first direction aims at developing explainable conversation strategies based on human-machine collaborative reasoning, which brings cognitive ease to users and helps to build trust between human and AI. The second direction explores fairness-aware conversation strategies based on short-term and long-term fairness learning, which helps to achieve fair recommendation experiences between advantages and disadvantaged users. The third direction aims at developing a learning to evaluate protocol for conversational recommendation, which unifies the advantages of online crowd-sourcing and offline model learning for evaluation. The project will also develop a prototype conversational recommendation platform as a class project to support the education of responsible AI. The project will result in the dissemination of shared data and evaluation platforms to the Information Retrieval, Data Mining, Recommender System, and broader AI communities.

More information about the project can be found on the NSF webpage: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2046457