705 citations · 1.7k across the 20 of their papers we have counts for
8 papers · 1 filter
Connecting Algorithmic Research and Usage Contexts: A Perspective of Contextualized Evaluation for Explainable AI
Q. Vera Liao, Yunfeng Zhang, Ronny Luss +2
Recent years have seen a surge of interest in the field of explainable AI (XAI), with a plethora of algorithms proposed in the literature. However, a lack of consensus on how to ev…
Human-AI Collaboration via Conditional Delegation: A Case Study of Content Moderation
Vivian Lai, Samuel Carton, Rajat Bhatnagar +3
Despite impressive performance in many benchmark datasets, AI models can still make mistakes, especially among out-of-distribution examples. It remains an open question how such im…
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…
Joint Optimization of AI Fairness and Utility: A Human-Centered Approach
Yunfeng Zhang, Rachel K. E. Bellamy, Kush R. Varshney
Today, AI is increasingly being used in many high-stakes decision-making applications in which fairness is an important concern. Already, there are many examples of AI being biased…
Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making
Yunfeng Zhang, Q. Vera Liao, Rachel K. E. Bellamy
Today, AI is being increasingly used to help human experts make decisions in high-stakes scenarios. In these scenarios, full automation is often undesirable, not only due to the si…
One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques
Vijay Arya, Rachel K. E. Bellamy, Pin-Yu Chen +17
As artificial intelligence and machine learning algorithms make further inroads into society, calls are increasing from multiple stakeholders for these algorithms to explain their…
Bootstrapping Conversational Agents With Weak Supervision
Neil Mallinar, Abhishek Shah, Rajendra Ugrani +9
Many conversational agents in the market today follow a standard bot development framework which requires training intent classifiers to recognize user input. The need to create a…
AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias
Rachel K. E. Bellamy, Kuntal Dey, Michael Hind +15
Fairness is an increasingly important concern as machine learning models are used to support decision making in high-stakes applications such as mortgage lending, hiring, and priso…