Computer Science > Machine Learning
[Submitted on 27 Feb 2024 (v1), last revised 29 Apr 2024 (this version, v3)]
Title:QUCE: The Minimisation and Quantification of Path-Based Uncertainty for Generative Counterfactual Explanations
View PDF HTML (experimental)Abstract:Deep Neural Networks (DNNs) stand out as one of the most prominent approaches within the Machine Learning (ML) domain. The efficacy of DNNs has surged alongside recent increases in computational capacity, allowing these approaches to scale to significant complexities for addressing predictive challenges in big data. However, as the complexity of DNN models rises, interpretability diminishes. In response to this challenge, explainable models such as Adversarial Gradient Integration (AGI) leverage path-based gradients provided by DNNs to elucidate their decisions. Yet the performance of path-based explainers can be compromised when gradients exhibit irregularities during out-of-distribution path traversal. In this context, we introduce Quantified Uncertainty Counterfactual Explanations (QUCE), a method designed to mitigate out-of-distribution traversal by minimizing path uncertainty. QUCE not only quantifies uncertainty when presenting explanations but also generates more certain counterfactual examples. We showcase the performance of the QUCE method by comparing it with competing methods for both path-based explanations and generative counterfactual examples.
Submission history
From: Jamie Duell [view email][v1] Tue, 27 Feb 2024 14:00:08 UTC (181 KB)
[v2] Thu, 14 Mar 2024 00:58:41 UTC (182 KB)
[v3] Mon, 29 Apr 2024 19:57:16 UTC (563 KB)
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