69 citations · 190 across the 8 of their papers we have counts for
8 papers
Human-centered NLP Fact-checking: Co-Designing with Fact-checkers using Matchmaking for AI
Houjiang Liu, Anubrata Das, Alexander Boltz +4
While many Natural Language Processing (NLP) techniques have been proposed for fact-checking, both academic research and fact-checking organizations report limited adoption of such…
Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes
Angie Zhang, Alexander Boltz, Jonathan Lynn +2
AI technologies continue to advance from digital assistants to assisted decision-making. However, designing AI remains a challenge given its unknown outcomes and uses. One way to e…
Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation
Angie Zhang, Olympia Walker, Kaci Nguyen +3
Research exploring how to support decision-making has often used machine learning to automate or assist human decisions. We take an alternative approach for improving decision-maki…
Learning Complementary Policies for Human-AI Teams
Ruijiang Gao, Maytal Saar-Tsechansky, Maria De-Arteaga
This paper tackles the critical challenge of human-AI complementarity in decision-making. Departing from the traditional focus on algorithmic performance in favor of performance of…
Policy Optimization with Advantage Regularization for Long-Term Fairness in Decision Systems
Eric Yang Yu, Zhizhen Qin, Min Kyung Lee +1
Long-term fairness is an important factor of consideration in designing and deploying learning-based decision systems in high-stake decision-making contexts. Recent work has propos…
Understanding Effects of Algorithmic vs. Community Label on Perceived Accuracy of Hyper-partisan Misinformation
Chenyan Jia, Alexander Boltz, Angie Zhang +2
Hyper-partisan misinformation has become a major public concern. In order to examine what type of misinformation label can mitigate hyper-partisan misinformation sharing on social…
IF-City: Intelligible Fair City Planning to Measure, Explain and Mitigate Inequality
Yan Lyu, Hangxin Lu, Min Kyung Lee +2
With the increasing pervasiveness of Artificial Intelligence (AI), many visual analytics tools have been proposed to examine fairness, but they mostly focus on data scientist users…
Human-AI Collaboration with Bandit Feedback
Ruijiang Gao, Maytal Saar-Tsechansky, Maria De-Arteaga +3
Human-machine complementarity is important when neither the algorithm nor the human yield dominant performance across all instances in a given domain. Most research on algorithmic…