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Why not both? Complementing explanations with uncertainty, and the role of self-confidence in Human-AI collaboration
AI and ML models have already found many applications in critical domains, such as healthcare and criminal justice.
Explainability in Machine Learning: a Pedagogical Perspective
We provide a pedagogical perspective on how to structure the learning process to better impart knowledge to students and researchers in machine learning, when and how to implement various explainability techniques as well as how to interpret the results.
Principled Diverse Counterfactuals in Multilinear Models
Machine learning (ML) applications have automated numerous real-life tasks, improving both private and public life.
Principles and Practice of Explainable Machine Learning
In this report, we focus specifically on data-driven methods -- machine learning (ML) and pattern recognition models in particular -- so as to survey and distill the results and observations from the literature.
Interventions and Counterfactuals in Tractable Probabilistic Models: Limitations of Contemporary Transformations
We show that when transforming SPNs to a causal graph interventional reasoning reduces to computing marginal distributions; in other words, only trivial causal reasoning is possible.
On Constraint Definability in Tractable Probabilistic Models
Incorporating constraints is a major concern in probabilistic machine learning.
