Special Issue "Interpretability, Accountability and Robustness in Machine Learning"

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".

Deadline for manuscript submissions: closed (3 October 2021).

Special Issue Editor

Dr. Laurent Risser
E-Mail Website
Guest Editor
CNRS - Toulouse Mathematics Institute - Artificial and Natural Intelligence Toulouse Institute
Interests: Explainable Machine Learning; Fair Machine Learning; Regularization in high-dimensional optimization; Medical image analysis

Special Issue Information

Dear Colleagues, 

Applications based on Machine-Learning algorithms have now become predominant to support decision-making in various fields such as online advertising, credit,  risk assessment or insurance. They are also of high interest for autonomous vehicles or healthcare, among others. These algorithms indeed make it possible to quickly and efficiently take automatic decisions, with unprecedented successes. Their decisions however heavily rely on training data, which are potentially biased. In addition, the decisions rules generally cannot be directly explained to Humans. This is particularly true when using Neural-Networks as well as Forest- or Kernel-based models. The training phase finally relies on high dimensional optimization algorithms which do not generally converge to global minima. As a consequence, a critical question recently arose among the population: Do algorithmic decisions convey any type of discrimination against specific population sub-groups? The same question can be asked in industrial applications, where machine learning algorithms could not be robust in critical situations. This opened a new field of research in Machine Learning dealing with the ‘Interpretability, Accountability and Robustness’ of Machine-Learning algorithms, which are at the heart of this special issue.

We invite you to submit high quality papers to the Special Issue on “Interpretability, Accountability and Robustness in Machine Learning”, with subjects covering the whole range from theory to applications. The following is a (non-exhaustive) list of topics of interests:

  • Interpretable and explainable Machine-Learning
  • Fair Machine-Learning
  • Bias Measurement in complex data
  • Applications

Dr. Laurent Risser
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Machine-Learning
  • Interpretability
  • Explainability
  • Fairness
  • Algorithmic Bias

Published Papers (5 papers)

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Research

Article
An Interaction-Based Convolutional Neural Network (ICNN) Toward a Better Understanding of COVID-19 X-ray Images
Algorithms 2021, 14(11), 337; https://doi.org/10.3390/a14110337 - 19 Nov 2021
Viewed by 356
Abstract
The field of explainable artificial intelligence (XAI) aims to build explainable and interpretable machine learning (or deep learning) methods without sacrificing prediction performance. Convolutional neural networks (CNNs) have been successful in making predictions, especially in image classification. These popular and well-documented successes use [...] Read more.
The field of explainable artificial intelligence (XAI) aims to build explainable and interpretable machine learning (or deep learning) methods without sacrificing prediction performance. Convolutional neural networks (CNNs) have been successful in making predictions, especially in image classification. These popular and well-documented successes use extremely deep CNNs such as VGG16, DenseNet121, and Xception. However, these well-known deep learning models use tens of millions of parameters based on a large number of pretrained filters that have been repurposed from previous data sets. Among these identified filters, a large portion contain no information yet remain as input features. Thus far, there is no effective method to omit these noisy features from a data set, and their existence negatively impacts prediction performance. In this paper, a novel interaction-based convolutional neural network (ICNN) is introduced that does not make assumptions about the relevance of local information. Instead, a model-free influence score (I-score) is proposed to directly extract the influential information from images to form important variable modules. This innovative technique replaces all pretrained filters found by trial-and-error with explainable, influential, and predictive variable sets (modules) determined by the I-score. In other words, future researchers need not rely on pretrained filters; the suggested algorithm identifies only the variables or pixels with high I-score values that are extremely predictive and important. The proposed method and algorithm were tested on real-world data set and a state-of-the-art prediction performance of 99.8% was achieved without sacrificing the explanatory power of the model. This proposed design can efficiently screen patients infected by COVID-19 before human diagnosis and can be a benchmark for addressing future XAI problems in large-scale data sets. Full article
(This article belongs to the Special Issue Interpretability, Accountability and Robustness in Machine Learning)
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Article
A Context-Aware Neural Embedding for Function-Level Vulnerability Detection
Algorithms 2021, 14(11), 335; https://doi.org/10.3390/a14110335 - 17 Nov 2021
Viewed by 271
Abstract
Exploitable vulnerabilities in software systems are major security concerns. To date, machine learning (ML) based solutions have been proposed to automate and accelerate the detection of vulnerabilities. Most ML techniques aim to isolate a unit of source code, be it a line or [...] Read more.
Exploitable vulnerabilities in software systems are major security concerns. To date, machine learning (ML) based solutions have been proposed to automate and accelerate the detection of vulnerabilities. Most ML techniques aim to isolate a unit of source code, be it a line or a function, as being vulnerable. We argue that a code segment is vulnerable if it exists in certain semantic contexts, such as the control flow and data flow; therefore, it is important for the detection to be context aware. In this paper, we evaluate the performance of mainstream word embedding techniques in the scenario of software vulnerability detection. Based on the evaluation, we propose a supervised framework leveraging pre-trained context-aware embeddings from language models (ELMo) to capture deep contextual representations, further summarized by a bidirectional long short-term memory (Bi-LSTM) layer for learning long-range code dependency. The framework takes directly a source code function as an input and produces corresponding function embeddings, which can be treated as feature sets for conventional ML classifiers. Experimental results showed that the proposed framework yielded the best performance in its downstream detection tasks. Using the feature representations generated by our framework, random forest and support vector machine outperformed four baseline systems on our data sets, demonstrating that the framework incorporated with ELMo can effectively capture the vulnerable data flow patterns and facilitate the vulnerability detection task. Full article
(This article belongs to the Special Issue Interpretability, Accountability and Robustness in Machine Learning)
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Article
Local Data Debiasing for Fairness Based on Generative Adversarial Training
Algorithms 2021, 14(3), 87; https://doi.org/10.3390/a14030087 - 14 Mar 2021
Viewed by 876
Abstract
The widespread use of automated decision processes in many areas of our society raises serious ethical issues with respect to the fairness of the process and the possible resulting discrimination. To solve this issue, we propose a novel adversarial training approach called GANSan [...] Read more.
The widespread use of automated decision processes in many areas of our society raises serious ethical issues with respect to the fairness of the process and the possible resulting discrimination. To solve this issue, we propose a novel adversarial training approach called GANSan for learning a sanitizer whose objective is to prevent the possibility of any discrimination (i.e., direct and indirect) based on a sensitive attribute by removing the attribute itself as well as the existing correlations with the remaining attributes. Our method GANSan is partially inspired by the powerful framework of generative adversarial networks (in particular Cycle-GANs), which offers a flexible way to learn a distribution empirically or to translate between two different distributions. In contrast to prior work, one of the strengths of our approach is that the sanitization is performed in the same space as the original data by only modifying the other attributes as little as possible, thus preserving the interpretability of the sanitized data. Consequently, once the sanitizer is trained, it can be applied to new data locally by an individual on their profile before releasing it. Finally, experiments on real datasets demonstrate the effectiveness of the approach as well as the achievable trade-off between fairness and utility. Full article
(This article belongs to the Special Issue Interpretability, Accountability and Robustness in Machine Learning)
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Article
Detection of Representative Variables in Complex Systems with Interpretable Rules Using Core-Clusters
Algorithms 2021, 14(2), 66; https://doi.org/10.3390/a14020066 - 22 Feb 2021
Viewed by 703
Abstract
In this paper, we present a new framework dedicated to the robust detection of representative variables in high dimensional spaces with a potentially limited number of observations. Representative variables are selected by using an original regularization strategy: they are the center of specific [...] Read more.
In this paper, we present a new framework dedicated to the robust detection of representative variables in high dimensional spaces with a potentially limited number of observations. Representative variables are selected by using an original regularization strategy: they are the center of specific variable clusters, denoted CORE-clusters, which respect fully interpretable constraints. Each CORE-cluster indeed contains more than a predefined amount of variables and each pair of its variables has a coherent behavior in the observed data. The key advantage of our regularization strategy is therefore that it only requires to tune two intuitive parameters: the minimal dimension of the CORE-clusters and the minimum level of similarity which gathers their variables. Interpreting the role played by a selected representative variable is additionally obvious as it has a similar observed behaviour as a controlled number of other variables. After introducing and justifying this variable selection formalism, we propose two algorithmic strategies to detect the CORE-clusters, one of them scaling particularly well to high-dimensional data. Results obtained on synthetic as well as real data are finally presented. Full article
(This article belongs to the Special Issue Interpretability, Accountability and Robustness in Machine Learning)
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Article
Groundwater Prediction Using Machine-Learning Tools
Algorithms 2020, 13(11), 300; https://doi.org/10.3390/a13110300 - 17 Nov 2020
Cited by 3 | Viewed by 1560
Abstract
Predicting groundwater availability is important to water sustainability and drought mitigation. Machine-learning tools have the potential to improve groundwater prediction, thus enabling resource planners to: (1) anticipate water quality in unsampled areas or depth zones; (2) design targeted monitoring programs; (3) inform groundwater [...] Read more.
Predicting groundwater availability is important to water sustainability and drought mitigation. Machine-learning tools have the potential to improve groundwater prediction, thus enabling resource planners to: (1) anticipate water quality in unsampled areas or depth zones; (2) design targeted monitoring programs; (3) inform groundwater protection strategies; and (4) evaluate the sustainability of groundwater sources of drinking water. This paper proposes a machine-learning approach to groundwater prediction with the following characteristics: (i) the use of a regression-based approach to predict full groundwater images based on sequences of monthly groundwater maps; (ii) strategic automatic feature selection (both local and global features) using extreme gradient boosting; and (iii) the use of a multiplicity of machine-learning techniques (extreme gradient boosting, multivariate linear regression, random forests, multilayer perceptron and support vector regression). Of these techniques, support vector regression consistently performed best in terms of minimizing root mean square error and mean absolute error. Furthermore, including a global feature obtained from a Gaussian Mixture Model produced models with lower error than the best which could be obtained with local geographical features. Full article
(This article belongs to the Special Issue Interpretability, Accountability and Robustness in Machine Learning)
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