497 citations · 1.9k across the 38 of their papers we have counts for
10 papers · 1 filter
Towards a Science of Human-AI Decision Making: A Survey of Empirical Studies
Vivian Lai, Chacha Chen, Q. Vera Liao +2
As AI systems demonstrate increasingly strong predictive performance, their adoption has grown in numerous domains. However, in high-stakes domains such as criminal justice and hea…
Human-Centered Explainable AI (XAI): From Algorithms to User Experiences
Q. Vera Liao, Kush R. Varshney
In recent years, the field of explainable AI (XAI) has produced a vast collection of algorithms, providing a useful toolbox for researchers and practitioners to build XAI applicati…
AI Explainability 360: Impact and Design
Vijay Arya, Rachel K. E. Bellamy, Pin-Yu Chen +17
As artificial intelligence and machine learning algorithms become increasingly prevalent in society, multiple stakeholders are calling for these algorithms to provide explanations.…
The Who in XAI: How AI Background Shapes Perceptions of AI Explanations
Upol Ehsan, Samir Passi, Q. Vera Liao +4
Explainability of AI systems is critical for users to take informed actions. Understanding "who" opens the black-box of AI is just as important as opening it. We conduct a mixed-me…
Uncertainty Quantification 360: A Holistic Toolkit for Quantifying and Communicating the Uncertainty of AI
Soumya Ghosh, Q. Vera Liao, Karthikeyan Natesan Ramamurthy +4
In this paper, we describe an open source Python toolkit named Uncertainty Quantification 360 (UQ360) for the uncertainty quantification of AI models. The goal of this toolkit is t…
Model LineUpper: Supporting Interactive Model Comparison at Multiple Levels for AutoML
Shweta Narkar, Yunfeng Zhang, Q. Vera Liao +2
Automated Machine Learning (AutoML) is a rapidly growing set of technologies that automate the model development pipeline by searching model space and generating candidate models.…
Question-Driven Design Process for Explainable AI User Experiences
Q. Vera Liao, Milena Pribić, Jaesik Han +2
A pervasive design issue of AI systems is their explainability--how to provide appropriate information to help users understand the AI. The technical field of explainable AI (XAI)…
Facilitating Knowledge Sharing from Domain Experts to Data Scientists for Building NLP Models
Soya Park, April Wang, Ban Kawas +3
Data scientists face a steep learning curve in understanding a new domain for which they want to build machine learning (ML) models. While input from domain experts could offer val…
Expanding Explainability: Towards Social Transparency in AI systems
Upol Ehsan, Q. Vera Liao, Michael Muller +2
As AI-powered systems increasingly mediate consequential decision-making, their explainability is critical for end-users to take informed and accountable actions. Explanations in h…
How Much Automation Does a Data Scientist Want?
Dakuo Wang, Q. Vera Liao, Yunfeng Zhang +5
Data science and machine learning (DS/ML) are at the heart of the recent advancements of many Artificial Intelligence (AI) applications. There is an active research thread in AI, \…