Model Agnostic Local Explanations of Reject

• URL: http://arxiv.org/abs/2205.07623v1
• Date: Mon, 16 May 2022 12:42:34 GMT
• Title: Model Agnostic Local Explanations of Reject
• Authors: Andr\'e Artelt, Roel Visser, Barbara Hammer
• Abstract summary: The application of machine learning based decision making systems in safety critical areas requires reliable high certainty predictions. Reject options are a common way of ensuring a sufficiently high certainty of predictions made by the system. We propose a model agnostic method for locally explaining arbitrary reject options by means of interpretable models and counterfactual explanations.
• Score: 6.883906273999368
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The application of machine learning based decision making systems in safety critical areas requires reliable high certainty predictions. Reject options are a common way of ensuring a sufficiently high certainty of predictions made by the system. While being able to reject uncertain samples is important, it is also of importance to be able to explain why a particular sample was rejected. However, explaining general reject options is still an open problem. We propose a model agnostic method for locally explaining arbitrary reject options by means of interpretable models and counterfactual explanations.

Related papers

• Local Feature Selection without Label or Feature Leakage for Interpretable Machine Learning Predictions [15.811319240038603]
  We attribute the problem of misleading selections by formalizing the concepts of label and feature leakage. We propose the first local feature selection method that is proven to have no leakage called SUWR. Our experimental results indicate that SUWR is less prone to overfitting and combines state-of-the-art predictive performance with high feature-selection sparsity.
  arXiv  Detail & Related papers  (2024-07-16T14:36:30Z)
• Logic-based Explanations for Linear Support Vector Classifiers with Reject Option [0.0]
  Support Vector (SVC) is a well-known Machine Learning (ML) model for linear classification problems. We propose a logic-based approach with formal guarantees on the correctness and minimality of explanations for linear SVCs with reject option.
  arXiv  Detail & Related papers  (2024-03-24T15:14:44Z)
• Identifying Drivers of Predictive Aleatoric Uncertainty [2.5311562666866494]
  We present a simple approach to explain predictive aleatoric uncertainties. We estimate uncertainty as predictive variance by adapting a neural network with a Gaussian output distribution. We quantify our findings with a nuanced benchmark analysis that includes real-world datasets.
  arXiv  Detail & Related papers  (2023-12-12T13:28:53Z)
• Decomposing Uncertainty for Large Language Models through Input Clarification Ensembling [69.83976050879318]
  In large language models (LLMs), identifying sources of uncertainty is an important step toward improving reliability, trustworthiness, and interpretability. In this paper, we introduce an uncertainty decomposition framework for LLMs, called input clarification ensembling. Our approach generates a set of clarifications for the input, feeds them into an LLM, and ensembles the corresponding predictions.
  arXiv  Detail & Related papers  (2023-11-15T05:58:35Z)
• Shortcomings of Top-Down Randomization-Based Sanity Checks for Evaluations of Deep Neural Network Explanations [67.40641255908443]
  We identify limitations of model-randomization-based sanity checks for the purpose of evaluating explanations. Top-down model randomization preserves scales of forward pass activations with high probability.
  arXiv  Detail & Related papers  (2022-11-22T18:52:38Z)
• "Even if ..." -- Diverse Semifactual Explanations of Reject [8.132423340684568]
  We propose a conceptual modeling of semifactual explanations for arbitrary reject options. We empirically evaluate a specific implementation on a conformal prediction based reject option.
  arXiv  Detail & Related papers  (2022-07-05T08:53:08Z)
• Logical Satisfiability of Counterfactuals for Faithful Explanations in NLI [60.142926537264714]
  We introduce the methodology of Faithfulness-through-Counterfactuals. It generates a counterfactual hypothesis based on the logical predicates expressed in the explanation. It then evaluates if the model's prediction on the counterfactual is consistent with that expressed logic.
  arXiv  Detail & Related papers  (2022-05-25T03:40:59Z)
• Explaining Reject Options of Learning Vector Quantization Classifiers [6.125017875330933]
  We propose to use counterfactual explanations for explaining rejects in machine learning models. We investigate how to efficiently compute counterfactual explanations of different reject options for an important class of models.
  arXiv  Detail & Related papers  (2022-02-15T08:16:10Z)
• Dense Uncertainty Estimation via an Ensemble-based Conditional Latent Variable Model [68.34559610536614]
  We argue that the aleatoric uncertainty is an inherent attribute of the data and can only be correctly estimated with an unbiased oracle model. We propose a new sampling and selection strategy at train time to approximate the oracle model for aleatoric uncertainty estimation. Our results show that our solution achieves both accurate deterministic results and reliable uncertainty estimation.
  arXiv  Detail & Related papers  (2021-11-22T08:54:10Z)
• Learning Probabilistic Ordinal Embeddings for Uncertainty-Aware Regression [91.3373131262391]
  Uncertainty is the only certainty there is. Traditionally, the direct regression formulation is considered and the uncertainty is modeled by modifying the output space to a certain family of probabilistic distributions. How to model the uncertainty within the present-day technologies for regression remains an open issue.
  arXiv  Detail & Related papers  (2021-03-25T06:56:09Z)
• Evaluations and Methods for Explanation through Robustness Analysis [117.7235152610957]
  We establish a novel set of evaluation criteria for such feature based explanations by analysis. We obtain new explanations that are loosely necessary and sufficient for a prediction. We extend the explanation to extract the set of features that would move the current prediction to a target class.
  arXiv  Detail & Related papers  (2020-05-31T05:52:05Z)

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.