14 citations · 124 across the 36 of their papers we have counts for
41 papers
A Reliable Common-Sense Reasoning Socialbot Built Using LLMs and Goal-Directed ASP
Yankai Zeng, Abhiramon Rajashekharan, Kinjal Basu +3
The development of large language models (LLMs), such as GPT, has enabled the construction of several socialbots, like ChatGPT, that are receiving a lot of attention for their abil…
Disentangling and Operationalizing AI Fairness at LinkedIn
Joaquin Quiñonero-Candela, Yuwen Wu, Brian Hsu +5
Operationalizing AI fairness at LinkedIn's scale is challenging not only because there are multiple mutually incompatible definitions of fairness but also because determining what…
An Operational Perspective to Fairness Interventions: Where and How to Intervene
Brian Hsu, Xiaotong Chen, Ying Han +2
As AI-based decision systems proliferate, their successful operationalization requires balancing multiple desiderata: predictive performance, disparity across groups, safeguarding…
Automated Interactive Domain-Specific Conversational Agents that Understand Human Dialogs
Yankai Zeng, Abhiramon Rajasekharan, Parth Padalkar +3
Achieving human-like communication with machines remains a classic, challenging topic in the field of Knowledge Representation and Reasoning and Natural Language Processing. These…
A Light-speed Linear Program Solver for Personalized Recommendation with Diversity Constraints
Haoyue Wang, Miao Cheng, Kinjal Basu +3
We study a structured linear program (LP) that emerges in the need of ranking candidates or items in personalized recommender systems. Since the candidate set is only known in real…
Pushing the limits of fairness impossibility: Who's the fairest of them all?
Brian Hsu, Rahul Mazumder, Preetam Nandy +1
The impossibility theorem of fairness is a foundational result in the algorithmic fairness literature. It states that outside of special cases, one cannot exactly and simultaneousl…