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Comparing Strategies For Post-Hoc Explanations In Machine Learning Models

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Indeed, it assigns a score to an explanation model by measuring the ability of a human predictor to replicate machine learning decisions when exposed to the provided explanations. To the best of our knowledge, this is the first attempt to apply forward simulatability to the medical domain. Creating three explanation models for the ML GI (right) scores for a six-layer ANN. On average, across four datasets and three predictive models, ICL strategies demonstrate non-trivial post hoc explanation capabilities: E-ICL explanations (with in-context examples selected from LIME) match the faithfulness of gradient-based/LIME methods; P-ICL and PG-ICL explanations achieve more faithful

An Empirical Comparison of Interpretable Models to Post-Hoc Explanations

We develop a methodology for the analysis of machine learning (ML) models to detect and understand biased decisions and apply it to two specific scenarios. In particular, we show how analyzing model predictions across the dataset, comparing models trained on different subsets of the original data, and applying model-agnostic post-hoc explanation tools can help A.5 Post hoc attribution explanation methods prompting with respect to context size. Evaluation is made with Mistral and Zephyr and the Causal Captum library. Post hoc attribution has been Judgment dataset. Most context sizes result in computed using the Captum (Miglani et al., 2023) better Self-AMPLIFY result as compared to IO. library.

Deciphering Model Decisions: A Comparative Analysis of SHAP

Explanations may be intrinsic, generated during the prediction process, or post-hoc, created after the model has been trained (Munn & Pitman, 2022). Both methods are essential for enhancing transparency and interpretability in machine learning models. Nowadays Predictive Maintenance (PdM) is widely used to maximize machine availability and minimize unnecessary maintenance activities. However, PdM systems often rely on complex machine learning models that cannot explain their actions, distancing users from the decision-making process. Accordingly, the purpose of the present paper is to explore and

We refer to this process as “post-hoc modification” of a model and demonstrate how it can be used to achieve precise control over which aspects of the model are fitted to the data through machine learning and which are determined through domain information.

Post-hoc Explanation. The other explanation position has been terminologically cast as justification or explainable machine learning. The key idea here is that one can explain/justify how a model reached some decision with reference to other information (e.g., “the model did this because it used such-and-such data”). Lipton [27] has further divided post-hoc By combining embedding-based and semantic based models for post-hoc explanations in recommender systems, the framework we defined aims at producing meaningful and easy-to-understand explanations, enhancing user trust and satisfaction, and potentially promoting the adoption of recom-mender systems across the e-commerce sector.

  • Post-Hoc Explanation Options for XAI in Deep Learning:
  • Techniques for Interpretable Machine Learning
  • The coming of age of interpretable and explainable machine learning models
  • Post-hoc Explanation Options for XAI in Deep Learning: The

To ensure that a machine learning model has learned the intended features, it can be useful to have an explanation of why a specific output was given. Slack et al. have created a conversational

Post-hoc Explanation Options for XAI in Deep Learning: The

Context: In artificial intelligence, particularly machine learning, understanding and interpreting model decisions is crucial. Techniques like SHAP (Shapley Additive explanations) and LIME (Local Model-agnostic approaches, on the other hand, focus on post-hoc interpretability by generating explanations irrespective of the underlying model architecture.

In this paper, we discuss explainable and interpretable machine learning as post hoc and ante-hoc strategies to address regulatory restrictions and highlight several aspects related to them, including their evaluation and assessment and the legal boundaries of

In recent times, the progress of machine learning has facilitated the development of decision support systems that exhibit predictive accuracy, surpassing human capabilities in certain scenarios. However, this improvement has come at the cost of increased model complexity, rendering them black-box models that obscure their internal logic from users. We introduce metrics to analyse these properties along the main dimensions of process data: the event, case, and control flow attributes. This allows for comparing explanations produced by transparent models with explanations generated by (post-hoc) explainability techniques on top of opaque black box models. On the amplification of security and privacy risks by post-hoc explanations in machine learning models Pengrui Quan , Supriyo Chakrabortyy, Jeya Vikranth Jeyakumar , and Mani Srivastava University of California, Los Angeles yIBM Thomas J. Watson Research Center

Interpretable machine learning techniques can generally be grouped into two categories: intrinsic interpretability and post-hoc interpretability, depending on the time when the interpretability is obtained [23]. Intrinsic interpretability is achieved by constructing self-explanatory models which in-corporate interpretability directly to their structures. The family of this category includes

(PDF) On the amplification of security and privacy risks by post-hoc ...

The black box of machine learning models is essential for building trust and understanding their predictions. In this comprehensive guide, we delve into the

ABSTRACT The growing complexity of machine learning and deep learning models has led to an increased reliance on opaque „black box“ systems, making it dificult to understand the rationale behind predictions. This lack of transparency is particularly challenging in high-stakes applications where interpretability is as important as accuracy. Post-hoc explanation To this end, we leverage post hoc explanation methods which output attribution scores (explanations) capturing the influence of each of the input features on model predictions. More specifically, we construct automated natural language rationales that embed insights from post hoc explanations to provide corrective signals to LLMs.

arXiv:2310.05797v4 [cs.CL] 11 Jul 2024

Additionally, post-hoc methods provide explanations after a model is learned and are generally model-agnostic. This survey provides a

This work concentrates on post-hoc ex-planation-by-example solutions to XAI as one approach to explaining black box deep-learning systems. Three different methods of post-hoc explanation are out-lined for image and time-series datasets: that is, factual, counterfactual, and semi-factual methods). The future landscape for XAI solutions is discussed. Therefore, post hoc explainers that provide explanations for machine learning models by, for example, estimating numerical importance of the input features, have been gaining wide usage. Self-explaining deep models are designed to learn the latent concept-based explanations implicitly during training, which eliminates the requirement of any post-hoc explanation generation technique. In this work, we propose one such model that appends an explanation generation module on top of any basic network and jointly trains the whole module

These techniques focus on providing either model-specific or model-agnostic post-hoc explanations, enhancing the clarity of the adopted machine learning models.

Explainable artificial intelligence is an emerging research direction helping the user or developer of machine learning models understand why models behave the way they do. The most popular explanation technique is feature importance. However, there are several different approaches how feature importances are being measured, most notably global and local. In Previous work has highlighted that existing post-hoc explanation methods exhibit disparities in explanation fidelity (across “race” and “gender” as sensitive attributes), and while a large body of work focuses on mitigating these issues at the explanation metric level, the role of the data generating process and black box model in relation to explanation disparities remains

Unlike black-box deep learning models, NSAI does not rely solely on post-hoc explanations, reducing the risk of misleading or opaque justifications. Besides overseeing model training to ensure it is conducted in a trustworthy and ethical manner, practitioners and learning science researchers can also gain insights into the actual logic behind

Nowadays, most studies comparing machine learning methods and logit models mainly focus on predictive accuracy, while others – to a lesser extent – focus on post-hoc explanation analysis. In this paper, we compare the predictive performance of five machine learning classifiers and the MNL and MMNL models. Various post-hoc interpretability methods exist to evaluate the results of machine learning classification and prediction tasks. To better understand the performance and reliability of such Analyzing and Evaluating Post hoc Explanation Methods for Black Box Machine Learning Abstract Over the past decade, complex tools such as deep learning models have been increasingly employed in high-stakes domains such as healthcare and criminal justice. Furthermore, these models achieve state-of-the-art accuracy at the expense of interpretability.

Unlocking Machine Learning Model Decisions: A Comparative Analysis of LIME and SHAP for Enhanced Interpretability Abstract The widespread adoption of machine learning in scientific research has created a fundamental tension between model opacity and scientific understanding. Whilst some advocate for intrinsically interpretable models, we introduce Computational Interpretabilism (CI) as a philosophical framework for post-hoc interpretability in scientific AI. Drawing parallels with The two-stage explanation strategy endows post-hoc graph explanations with the applicability to pretrained GNNs where downstream tasks are inaccessible and the capacity to explain the transferable knowledge in the pretrained GNNs.

Machine learning methods are being increasingly applied in sensitive societal contexts, where decisions impact human lives. Hence it has become necessary to build capabilities for providing easily-interpretable explanations of models’ predictions. Recently in academic literature, a vast number of explanations methods have been proposed. Unfortunately, to our knowledge, little Unfortunately, there is little agreement about what constitutes a „good“ explanation. Moreover, current methods of explanation evaluation are derived from either subjective or proxy means. In this work, we propose a framework for the evaluation of post hoc explainers on ground truth that is directly derived from the additive structure of a model.