On the Relationship Between Interpretability and Explainability in Machine Learning
- URL: http://arxiv.org/abs/2311.11491v2
- Date: Thu, 25 Apr 2024 12:06:39 GMT
- Title: On the Relationship Between Interpretability and Explainability in Machine Learning
- Authors: Benjamin Leblanc, Pascal Germain,
- Abstract summary: Interpretability and explainability have gained more and more attention in the field of machine learning.
Since both provide information about predictors and their decision process, they are often seen as two independent means for one single end.
This view has led to a dichotomous literature: explainability techniques designed for complex black-box models, or interpretable approaches ignoring the many explainability tools.
- Score: 2.828173677501078
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Interpretability and explainability have gained more and more attention in the field of machine learning as they are crucial when it comes to high-stakes decisions and troubleshooting. Since both provide information about predictors and their decision process, they are often seen as two independent means for one single end. This view has led to a dichotomous literature: explainability techniques designed for complex black-box models, or interpretable approaches ignoring the many explainability tools. In this position paper, we challenge the common idea that interpretability and explainability are substitutes for one another by listing their principal shortcomings and discussing how both of them mitigate the drawbacks of the other. In doing so, we call for a new perspective on interpretability and explainability, and works targeting both topics simultaneously, leveraging each of their respective assets.
Related papers
- Diffexplainer: Towards Cross-modal Global Explanations with Diffusion Models [51.21351775178525]
DiffExplainer is a novel framework that, leveraging language-vision models, enables multimodal global explainability.
It employs diffusion models conditioned on optimized text prompts, synthesizing images that maximize class outputs.
The analysis of generated visual descriptions allows for automatic identification of biases and spurious features.
arXiv Detail & Related papers (2024-04-03T10:11:22Z) - Explaining Explainability: Towards Deeper Actionable Insights into Deep
Learning through Second-order Explainability [70.60433013657693]
Second-order explainable AI (SOXAI) was recently proposed to extend explainable AI (XAI) from the instance level to the dataset level.
We demonstrate for the first time, via example classification and segmentation cases, that eliminating irrelevant concepts from the training set based on actionable insights from SOXAI can enhance a model's performance.
arXiv Detail & Related papers (2023-06-14T23:24:01Z) - REX: Reasoning-aware and Grounded Explanation [30.392986232906107]
We develop a new type of multi-modal explanations that explain the decisions by traversing the reasoning process and grounding keywords in the images.
Second, we identify the critical need to tightly couple important components across the visual and textual modalities for explaining the decisions.
Third, we propose a novel explanation generation method that explicitly models the pairwise correspondence between words and regions of interest.
arXiv Detail & Related papers (2022-03-11T17:28:42Z) - Human Interpretation of Saliency-based Explanation Over Text [65.29015910991261]
We study saliency-based explanations over textual data.
We find that people often mis-interpret the explanations.
We propose a method to adjust saliencies based on model estimates of over- and under-perception.
arXiv Detail & Related papers (2022-01-27T15:20:32Z) - Interpretable Deep Learning: Interpretations, Interpretability,
Trustworthiness, and Beyond [49.93153180169685]
We introduce and clarify two basic concepts-interpretations and interpretability-that people usually get confused.
We elaborate the design of several recent interpretation algorithms, from different perspectives, through proposing a new taxonomy.
We summarize the existing work in evaluating models' interpretability using "trustworthy" interpretation algorithms.
arXiv Detail & Related papers (2021-03-19T08:40:30Z) - Fairness and Robustness of Contrasting Explanations [9.104557591459283]
We study individual fairness and robustness of contrasting explanations.
We propose to use plausible counterfactuals instead of closest counterfactuals for improving the individual fairness of counterfactual explanations.
arXiv Detail & Related papers (2021-03-03T12:16:06Z) - Contrastive Explanations for Model Interpretability [77.92370750072831]
We propose a methodology to produce contrastive explanations for classification models.
Our method is based on projecting model representation to a latent space.
Our findings shed light on the ability of label-contrastive explanations to provide a more accurate and finer-grained interpretability of a model's decision.
arXiv Detail & Related papers (2021-03-02T00:36:45Z) - Interpretability and Explainability: A Machine Learning Zoo Mini-tour [4.56877715768796]
Interpretability and explainability lie at the core of many machine learning and statistical applications in medicine, economics, law, and natural sciences.
We emphasise the divide between interpretability and explainability and illustrate these two different research directions with concrete examples of the state-of-the-art.
arXiv Detail & Related papers (2020-12-03T10:11:52Z) - Abduction and Argumentation for Explainable Machine Learning: A Position
Survey [2.28438857884398]
This paper presents Abduction and Argumentation as two principled forms for reasoning.
It fleshes out the fundamental role that they can play within Machine Learning.
arXiv Detail & Related papers (2020-10-24T13:23:44Z) - The Struggles of Feature-Based Explanations: Shapley Values vs. Minimal
Sufficient Subsets [61.66584140190247]
We show that feature-based explanations pose problems even for explaining trivial models.
We show that two popular classes of explainers, Shapley explainers and minimal sufficient subsets explainers, target fundamentally different types of ground-truth explanations.
arXiv Detail & Related papers (2020-09-23T09:45:23Z) - One Explanation Does Not Fit All: The Promise of Interactive
Explanations for Machine Learning Transparency [21.58324172085553]
We discuss the promises of Interactive Machine Learning for improved transparency of black-box systems.
We show how to personalise counterfactual explanations by interactively adjusting their conditional statements.
We argue that adjusting the explanation itself and its content is more important.
arXiv Detail & Related papers (2020-01-27T13:10:12Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.