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About 10,000 papers for “explainable machine learning”

Explainable Machine Learning

Jochen Garcke, Ribana Roscher · Machine Learning and Knowledge Extraction · 2023

Machine learning methods are widely used in commercial applications and in many scientific areas [...]

Cited by 10Open access

Explainable Machine learning

paul fred · Figshare · 2026

Financial fraud detection remains a persistent challenge due to extreme class imbalance and the heterogeneity of transaction systems, including mobile payments, cryptocurrency networks, and traditional banking platforms. This note summarizes a unified and explainable machine learning framework that evaluates Logistic Regression, Random Forest, and XGBoost across multiple datasets. Emphasis is placed on model generalization, minority-class detection, and interpretability using SHAP. The findings reinforce the superiority of ensemble methods in fraud detection while highlighting the growing importance of explainable artificial intelligence (XAI) in high-stakes financial environments.

Cited by 0Open access

Explainable Machine learning

paul fred · Figshare · 2026

Financial fraud detection remains a persistent challenge due to extreme class imbalance and the heterogeneity of transaction systems, including mobile payments, cryptocurrency networks, and traditional banking platforms. This note summarizes a unified and explainable machine learning framework that evaluates Logistic Regression, Random Forest, and XGBoost across multiple datasets. Emphasis is placed on model generalization, minority-class detection, and interpretability using SHAP. The findings reinforce the superiority of ensemble methods in fraud detection while highlighting the growing importance of explainable artificial intelligence (XAI) in high-stakes financial environments.

Cited by 0Open access

Explainable Machine Learning

Haase, Robert · Zenodo (CERN European Organization for Nuclear Research) · 2025

In this lecture we will dive into Explainable Machine Learning, covering basic definition of the term and the field, including SHAP-Analysis and Grad-CAM as examples. Related training materials are available online too: https://scads.github.io/ai4medicine-2025/day2.4_explainability/readme.html

Cited by 0Open access

Explainable machine learning and deep learning

Liao, Zhongtian · DR-NTU (Nanyang Technological University) · 2023

The ability of machine learning to improve research, processes and products is significant. Nonetheless, one major challenge hindering its adoption is that computers do not provide explanations for the predictions made in general. This project aims to solve this problem by focusing on methods for making machine learning models and decisions made interpretable to humans.
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\nIn this report, the main concepts related to interpretability are stated first. Next, both global and local model-agnostic methods are explored and implemented to interpret specific models or certain predictions made by the models. Each method implemented is elaborated in detail, including how it functions, its advantages and the negative effects. 
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\nTwo datasets, the bike sharing dataset and the cervical cancer dataset, are used as examples to explain and analyze different methods used in this project on regression and classification levels.

Cited by 0

Efficient and Explainable Machine Learning

Wang, Weijia · eScholarship (California Digital Library) · 2023

In recent years, neural networks have demonstrated remarkable breakthroughs in perceptual tasks such as computer vision and natural language processing, which achieve exceptional classification accuracy and robust generalization capabilities. My research primarily resides within the domain of deep learning, including two major dimensions:Interpretability: Tabular data plays a crucial role as a primary source of structured information, serving as the foundation of decision-making across various fields, ranging from marketing and healthcare to government policy evaluation. In recognition of its significance, researchers have recently turned their attention to applying neural network models to tabular data due to their generally superior performance in comparison to rule-based methods. While neural networks excel in performance, they often act as "black-box" models, posing challenges for applications requiring human interpretability, including medical diagnosis and loan analysis. To address this issue, we propose a series of innovative paradigms aimed at generating human-readable predictions in tabular classification problems through novel neural network training approaches. This allows our interpretable models to leverage the high generalization capacity of neural networks.Efficiency and Scalability: The continuous improvement in neural network performance has come at the cost of increased complexity, including higher storage requirements and computational demands. Despite the substantial processing power of modern hardware for training these models, real-time inference and energy consumption remain significant obstacles, particularly in mobile and wearable applications. To tackle this challenge, we propose to encode deep neural networks using a low-precision number representation, such that the models could achieve accuracy levels comparable to their full-precision counterparts. In addition, we introduce an approach that combines certain steps during the feed-forward phase by pre-computing various intermediate results, allowing the trained neural network to primarily operate in the low-precision domain with fewer floating-point operations.By addressing these two critical aspects, my work contributes to the advancement of neural network applications in both high-stakes interpretability-sensitive domains and resource-constrained deployment scenarios.

Cited by 0Open access

eXplainable Machine Learning -- USI Course

Sokol, Kacper · Zenodo (CERN European Organization for Nuclear Research) · 2023

<strong>Machine Learning Explainability: Exploring Automated Decision-Making Through Transparent Modelling and Peeking Inside Black Boxes</strong> Quarto slides for an eXplainable Machine Learning course delivered at USI Machine learning models require care, attention and a fair amount of tuning to offer accurate, consistent and robust predictive modelling of data. Why should their transparency and explainability be any different? While it is possible to easily generate explanatory insights with methods that are post-hoc and model-agnostic -- LIME and SHAP, for example -- these can be misleading when output by generic tools and viewed out of (technical or domain) context. Explanations should not be taken at their face value; instead their understanding ought to come from interpreting explanatory insights in view of the implicit caveats and limitations under which they were generated. After all, explainability algorithms are complex entities often built from multiple components that are subject to parameterisation choices and operational assumptions, all of which must be accounted for and configured to yield a truthful and useful explainer. Additionally, since any particular method may only provide partial information about the functioning of a predictive model, embracing diverse insights and appreciating their complementarity -- as well as disagreements -- can further enhance understanding of an algorithmic decision-making process. This course takes an adversarial perspective on artificial intelligence explainability and machine learning interpretability. Instead of reviewing popular approaches used to these ends, it breaks them up into core functional blocks, studies the role and configuration thereof, and reassembles them to create bespoke, well-understood explainers suitable for the problem at hand. The course focuses predominantly on tabular data, with some excursions into image and text explainability whenever a method is agnostic of the data type. The tuition is complemented by a series of hands-on materials for self-study, which allow you to experiment with these techniques and appreciate their inherent complexity, capabilities and limitations. The assignment, on the other hand, requires you to develop a tailor-made explainability suite for a data set and predictive model of your choice, or alternatively analyse an explainability algorithm to identify its core algorithmic building blocks and explore how they affect the resulting explanation. (Note that there is a scope for a bespoke project if you have a well-defined idea in mind.)

Cited by 0Open access

Explainable Machine Learning in Cybersecurity

Yan, Feixue · Figshare · 2024

This study addresses the need for transparency in machine learning models within cybersecurity, emphasizing the importance of comprehensible explanations to support trust and informed decision-making. It proposes a framework to enhance explanation in two critical areas: identifying suspicious cryptocurrency transactions to improve information-level security and examining Android software for malicious behavior to strengthen system-level security. By clarifying the decision-making processes of machine learning, this research aims to mitigate risks associated with opaque algorithms, fostering greater security, user trust, and reliability in digital threat detection and prevention.

Cited by 0Open access

Towards explainable automated machine learning

Moosbauer, Julia · 2023

Automated machine learning (AutoML) aims at increasing the efficiency and accessibility of machine learning (ML) techniques by automating complex tasks like model and hyperparameter selection through optimization. While AutoML systems and hyperparameter optimization (HPO) frameworks have demonstrated efficiency gains compared to manual expert-driven processes, the requirement for explainability is currently hardly satisfied. The work connects the two emerging fields of AutoML and explainable AI in a systematic manner. The thesis includes diverse methodological approaches towards increased explainability in the context of AutoML. Throughout this thesis, three levels of explainability requirements are distinguished: (1) explainability of the final model, (2) explainability of the leraning algorithm, that found the model, and (3) explainability of the AutoML system, that configured the learning algorithm, that found the model. The first part of this thesis addresses the requirement of explainability of models returned by AutoML systems. The first contributing article reviews multi-objective hyperparameter optimization in general and motivates interpretability and sparsity as further objectives of HPO. In the second contributing article, we develop an efficient multi-objective HPO method that optimizes for both predictive performance and model sparsity as one criterion of model explainability. The second part of the thesis deals with the explainability of the learning algorithms or inducing mechanisms experimented with during an AutoML process. A third contributing article presents a novel method that extends partial dependence plots to generate insights on the effects of hyperparameters of learning algorithms on performance. The technique accounts in a post-hoc manner for sampling bias typically present in experimental data generated by AutoML systems. The fourth contributing article introduces a method to address the problem of a sampling bias ex-ante by adapting the search strategy of an optimizer to account for the reliability of explanations in terms of hyperparameter effects. The final part of the thesis deals with the explainability of the mechanisms and components of AutoML systems. The aim is to explain why specific optimizers and their components used within AutoML systems work better than others. A fifth contributing article presents a thorough evaluation of multi-fidelity HPO algorithms. An expressive and flexible framework of multi-fidelity HPO algorithms is introduced and automatically configured through optimization, and the outcomes are analyzed through an ablation analysis. This work has been facilitated by an efficient multi-fidelity benchmarking suite (YAHPO gym), which is the sixth contribution of this thesis.

Cited by 0Open access

Visualization for explainable machine learning

Yao Ming · 2019

With the recent advancements of machine learning, especially deep learning, we have seen fast-growing applications of these intelligent systems in various domains. However, the increasing complexity of these systems makes it very challenging to explain or interpret their reasoning process, which limits their adoption in critical decision-making scenarios. In the meantime, visualization has been effectively applied to support the understanding and analyzing of complex systems and large data collections. In this thesis, we study how to make machine learning systems explainable for human users using visualizations. We first propose a user-model interaction framework for describing and categorizing the explainable machine learning problem. Then we discuss the role of visualization in explainable machine learning, including How, Where, and Why visualization could be used to help explain What parts of the machine learning pipeline to Whom. We also summarize the recent research advances in this field. We then grounded our study of different aspects of the explainable problem on specific applications: 1) how can visualization help explain the inner working mechanisms of deep learning models for model developers and researchers? 2) how can we explain the behavior of a model for non-expert users with little knowledge in machine learning? 3) how can explainability help expert users in various application domains to incorporate domain knowledge into the model? We experiment with these ideas under a human-in-the-loop setting and include preliminary evaluation results in this thesis. At last, we discuss our ongoing and future research as well as open questions in visualization for explainable machine learning.

Cited by 1

Explainable Machine Learning in Cybersecurity

Yan, Feixue · Swinburne Research Bank (Swinburne University of Technology) · 2024

This study addresses the need for transparency in machine learning models within cybersecurity, emphasizing the importance of comprehensible explanations to support trust and informed decision-making. It proposes a framework to enhance explanation in two critical areas: identifying suspicious cryptocurrency transactions to improve information-level security and examining Android software for malicious behavior to strengthen system-level security. By clarifying the decision-making processes of machine learning, this research aims to mitigate risks associated with opaque algorithms, fostering greater security, user trust, and reliability in digital threat detection and prevention.

Cited by 0Open access