An Algorithm Independent Case-Based Explanation Approach for Recommender Systems Using Interaction Graphs

Caro-Martínez, Marta; Recio-Garcí­a, Juan A.; Jiménez-Díaz, Guillermo

doi:10.1007/978-3-030-29249-2_2

Cited by 8 publications

(3 citation statements)

References 27 publications

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“…The case-based approach has been used to predict running-paces for different stages in ultra races, based on cases from similar runners in past cases [16]. CBR has also been used to provide explanatory cases for black-box recommender systems to achieve justification [4,10]. Explanations for such systems can also be created through relations between features (concepts) [11].…”

Section: Related Workmentioning

confidence: 99%

“…The most important measure to prevent poor working conditions is regulations. Regulations are usually enforced through labour inspections, which make them a vital part of the strategy employed by many countries to ensure good health, safety, decent work conditions and well-being for workers (see UN's SDGs 3, 8 and 16 4 ). Hence it is important to carry out labour inspections efficiently at large scale.…”

Section: Introductionmentioning

confidence: 99%

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Bayesian Feature Construction for Case-Based Reasoning: Generating Good Checklists

Flogard

Mengshoel

Bach

2021

Case-Based Reasoning Research and Development

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Checklists are used to aid the fulfillment of safety critical activities in a variety of different applications, such as aviation, health care or labour inspections. However, optimizing a checklist for a specific purpose can be challenging. Checklists also need to be trustworthy and user friendly to promote user compliance. With labour inspections as a starting point, we introduce the Checklist Construction Problem. To address the problem, we seek to optimize the content of labour inspection checklists in order to improve the working conditions in every organisation targeted for inspections. To do so, we introduce a hybrid framework called BCBR to construct trustworthy checklists. BCBR is based on case-based reasoning (CBR) and Bayesian inference (BI) and constructs new checklists based on past cases. A key novelty of BCBR is the use of BI for constructing new features in past cases. The augmented past cases are retrieved via CBR to construct new checklists, which ensures justification for the content of the checklists and promotes trust. Experiments suggest that BCBR is more effective than any other baseline we tested, in terms of constructing trustworthy checklists.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bayesian Feature Construction for Case-Based Reasoning: Generating Good Checklists

Flogard

Mengshoel

Bach

2021

Case-Based Reasoning Research and Development

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“…As CBR is considered a transparent technique, it is being applied for the explanation of opaque machine-learning techniques such as neural networks [33], [49], [50]. There are several proposals to create NN-CBR twins, where the CBR system is not used to solve the problem, but to find explanatory cases for the user.…”

Section: Explanation Of the Neural Network Predictions Using Case-mentioning

confidence: 99%

Explainable Prediction of Chronic Renal Disease in the Colombian Population Using Neural Networks and Case-Based Reasoning

et al. 2019

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This paper presents a neural network-based classifier to predict whether a person is at risk of developing chronic kidney disease (CKD). The model is trained with the demographic data and medical care information of two population groups: on the one hand, people diagnosed with CKD in Colombia during 2018, and on the other, a sample of people without a diagnosis of this disease. Once the model is trained and evaluation metrics for classification algorithms are applied, the model achieves 95% accuracy in the test data set, making its application for disease prognosis feasible. However, despite the demonstrated efficiency of the neural networks to predict CKD, this machine-learning paradigm is opaque to the expert regarding the explanation of the outcome. Current research on eXplainable AI proposes the use of twin systems, where a black-box machine-learning method is complemented by another white-box method that provides explanations about the predicted values. Case-Based Reasoning (CBR) has proved to be an ideal complement as this paradigm is able to find explanatory cases for an explanation-by-example justification of a neural network's prediction. In this paper, we apply and validate a NN-CBR twin system for the explanation of CKD predictions. As a result of this research, 3,494,516 people were identified as being at risk of developing CKD in Colombia, or 7% of the total population. INDEX TERMS Chronic kidney disease prediction, neural networks, case-based reasoning, twin systems, explainable AI, support vector machines, random forest.

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A User-Centric Evaluation to Generate Case-Based Explanations Using Formal Concept Analysis

Jorro-Aragoneses

Caro-Martínez

Díaz-Agudo

et al. 2020

Case-Based Reasoning Research and Development

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An Algorithm Independent Case-Based Explanation Approach for Recommender Systems Using Interaction Graphs

Cited by 8 publications

References 27 publications

Bayesian Feature Construction for Case-Based Reasoning: Generating Good Checklists

Bayesian Feature Construction for Case-Based Reasoning: Generating Good Checklists

Explainable Prediction of Chronic Renal Disease in the Colombian Population Using Neural Networks and Case-Based Reasoning

A User-Centric Evaluation to Generate Case-Based Explanations Using Formal Concept Analysis

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