18 citations · 38 across the 4 of their papers we have counts for
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cs.AI2021★ 18 cited
Counterfactuals and Causability in Explainable Artificial Intelligence: Theory, Algorithms, and Applications
Yu-Liang Chou, Catarina Moreira, Peter Bruza +2
There has been a growing interest in model-agnostic methods that can make deep learning models more transparent and explainable to a user. Some researchers recently argued that for…
cs.AI2020★ 1 cited
An Interpretable Probabilistic Approach for Demystifying Black-box Predictive Models
Catarina Moreira, Yu-Liang Chou, Mythreyi Velmurugan +3
The use of sophisticated machine learning models for critical decision making is faced with a challenge that these models are often applied as a "black-box". This has led to an inc…