A Review of Taxonomies of Explainable Artificial Intelligence (XAI) Methods

Speith, Timo

doi:10.1145/3531146.3534639

Cited by 140 publications

(92 citation statements)

References 65 publications

(122 reference statements)

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“…Among these terms, interpretability is defined as a concept similar to explainability [41]. However, in recent years, the terminology for the term "interpretability" has shifted to information extraction rather than providing explanations [42], meaning that the terms of interpretability and explainability are becoming more diverse while still intersecting with each other. Therefore, in this study, we focus on the side of "explainability" in XAI whereas the reviewed papers focusing on "intelligibility", "transparency", and "intelligibility" parts would be extracted and excluded according to their clutters with the concept of "explainability".…”

Section: Xai Backgroundmentioning

confidence: 99%

Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research

et al. 2022

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This survey presents a comprehensive review of current literature on Explainable Artificial Intelligence (XAI) methods for cyber security applications. Due to the rapid development of Internetconnected systems and Artificial Intelligence in recent years, Artificial Intelligence including Machine Learning (ML) and Deep Learning (DL) has been widely utilized in the fields of cyber security including intrusion detection, malware detection, and spam filtering. However, although Artificial Intelligence-based approaches for the detection and defense of cyber attacks and threats are more advanced and efficient compared to the conventional signature-based and rule-based cyber security strategies, most ML-based techniques and DL-based techniques are deployed in the ''black-box'' manner, meaning that security experts and customers are unable to explain how such procedures reach particular conclusions. The deficiencies of transparencies and interpretability of existing Artificial Intelligence techniques would decrease human users' confidence in the models utilized for the defense against cyber attacks, especially in current situations where cyber attacks become increasingly diverse and complicated. Therefore, it is essential to apply XAI in the establishment of cyber security models to create more explainable models while maintaining high accuracy and allowing human users to comprehend, trust, and manage the next generation of cyber defense mechanisms. Although there are papers reviewing Artificial Intelligence applications in cyber security areas and the vast literature on applying XAI in many fields including healthcare, financial services, and criminal justice, the surprising fact is that there are currently no survey research articles that concentrate on XAI applications in cyber security. Therefore, the motivation behind the survey is to bridge the research gap by presenting a detailed and up-to-date survey of XAI approaches applicable to issues in the cyber security field. Our work is the first to propose a clear roadmap for navigating the XAI literature in the context of applications in cyber security.

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Section: Xai Backgroundmentioning

confidence: 99%

Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research

et al. 2022

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“…Authors in [103] presents a generalized taxonomy of EXAI based on current challenges and future directions. The proposed taxonomy incorporates the EXAI database models, reviewed taxonomies, and a decision tree approach to select the best taxonomy for desired applications.…”

Section: State-of-the-artmentioning

confidence: 99%

Explainable AI for Healthcare 5.0: Opportunities and Challenges

et al. 2022

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In the healthcare domain, a transformative shift is envisioned towards Healthcare 5.0. It expands the operational boundaries of Healthcare 4.0 and leverages patient-centric digital wellness. Healthcare 5.0 focuses on real-time patient monitoring, ambient control and wellness, and privacy compliance through assisted technologies like artificial intelligence (AI), Internet-of-Things (IoT), big data, and assisted networking channels. However, healthcare operational procedures, verifiability of prediction models, resilience, and lack of ethical and regulatory frameworks are potential hindrances to the realization of Healthcare 5.0. Recently, explainable AI (EXAI) has been a disruptive trend in AI that focuses on the explainability of traditional AI models by leveraging the decision-making of the models and prediction outputs. The explainability factor opens new opportunities to the black-box models and brings confidence in healthcare stakeholders to interpret the machine learning (ML) and deep learning (DL) models. EXAI is focused on improving clinical health practices and brings transparency to the predictive analysis, which is crucial in the healthcare domain. Recent surveys on EXAI in healthcare have not significantly focused on the data analysis and interpretation of models, which lowers its practical deployment opportunities. Owing to the gap, the proposed survey explicitly details the requirements of EXAI in Healthcare 5.0, the operational and data collection process. Based on the review method and presented research questions, systematically, the article unfolds a proposed architecture that presents an EXAI ensemble on the computerized tomography (CT) image classification and segmentation process. A solution taxonomy of EXAI in Healthcare 5.0 is proposed, and operational challenges are presented. A supported case study on electrocardiogram (ECG) monitoring is presented that preserves the privacy of local models via federated learning (FL) and EXAI for metric validation. The case-study is supported through experimental validation. The analysis proves the efficacy of EXAI in health setups that envisions real-life model deployments in a wide range of clinical applications.

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“…Since explainability has rapidly expanded as a research field in the last years, publications about this topic have become quite numerous, and it is hard to keep track of the terms, methods, and results that came up [46]. For this reason, there have been numerous literature reviews presenting overviews concerning certain aspects (e.g., methods or definitions) of explainability research.…”

Section: Explainabilitymentioning

confidence: 99%

“…For instance, [47] focuses on explainability of recommender systems, [48] on explainability of robots and human-robot interaction, [49] on the humancomputer interaction (HCI) domain, and [50] on biomedical and malware classification. Another focus of these reviews is to demarcate different, but related terms often used in explainability research (see, e.g., [4,46,51]). For instance, the terms "explainablilty" and "interpretability" are sometimes used as synonyms and sometimes not [52,53].…”

Section: Explainabilitymentioning

confidence: 99%

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Explainable software systems: from requirements analysis to system evaluation

2022

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The growing complexity of software systems and the influence of software-supported decisions in our society sparked the need for software that is transparent, accountable, and trustworthy. Explainability has been identified as a means to achieve these qualities. It is recognized as an emerging non-functional requirement (NFR) that has a significant impact on system quality. Accordingly, software engineers need means to assist them in incorporating this NFR into systems. This requires an early analysis of the benefits and possible design issues that arise from interrelationships between different quality aspects. However, explainability is currently under-researched in the domain of requirements engineering, and there is a lack of artifacts that support the requirements engineering process and system design. In this work, we remedy this deficit by proposing four artifacts: a definition of explainability, a conceptual model, a knowledge catalogue, and a reference model for explainable systems. These artifacts should support software and requirements engineers in understanding the definition of explainability and how it interacts with other quality aspects. Besides that, they may be considered a starting point to provide practical value in the refinement of explainability from high-level requirements to concrete design choices, as well as on the identification of methods and metrics for the evaluation of the implemented requirements.

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A Review of Taxonomies of Explainable Artificial Intelligence (XAI) Methods

Cited by 140 publications

References 65 publications

Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research

Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research

Explainable AI for Healthcare 5.0: Opportunities and Challenges

Explainable software systems: from requirements analysis to system evaluation

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