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20132026
most citedOne Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques

270 citations · 1.1k across the 72 of their papers we have counts for

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Showing 2021Show all

7 papers · 1 filter

stat.ME2021

Scalable Intervention Target Estimation in Linear Models

Burak Varici, Karthikeyan Shanmugam, Prasanna Sattigeri +1

This paper considers the problem of estimating the unknown intervention targets in a causal directed acyclic graph from observational and interventional data. The focus is on soft…

cs.LG2021

Selective Regression Under Fairness Criteria

Abhin Shah, Yuheng Bu, Joshua Ka-Wing Lee +4

Selective regression allows abstention from prediction if the confidence to make an accurate prediction is not sufficient. In general, by allowing a reject option, one expects the…

cs.LG2021★ 1 cited

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.…

cs.AI2021★ 13 cited

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…

cs.LG2021★ 3 cited

Uncertainty Characteristics Curves: A Systematic Assessment of Prediction Intervals

Jiri Navratil, Benjamin Elder, Matthew Arnold +2

Accurate quantification of model uncertainty has long been recognized as a fundamental requirement for trusted AI. In regression tasks, uncertainty is typically quantified using pr…

cs.CV2021

Detector-Free Weakly Supervised Grounding by Separation

Assaf Arbelle, Sivan Doveh, Amit Alfassy +14

Nowadays, there is an abundance of data involving images and surrounding free-form text weakly corresponding to those images. Weakly Supervised phrase-Grounding (WSG) deals with th…

cs.CV2021★ 21 cited

AdaFuse: Adaptive Temporal Fusion Network for Efficient Action Recognition

Yue Meng, Rameswar Panda, Chung-Ching Lin +5

Temporal modelling is the key for efficient video action recognition. While understanding temporal information can improve recognition accuracy for dynamic actions, removing tempor…