On Explainable Fuzzy Recommenders and their Performance Evaluation

Rutkowski, Tomasz M.; Łapa, Krystian; Nielek, Radosław

doi:10.2478/amcs-2019-0044

Cited by 25 publications

(6 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The existing literature generally classifies the XAI taxonomy as follows [ [155] , [156] , [157] ]: Stage : ante-hoc [ 158 , 159 ] and post-hoc, where post-hoc methods are further classified into model-agnostic and model-specific. Scope : local [ 160 , 161 ] and global [ 162 , 163 ]. Output format : numeric [ 164 ], visualization [ 165 ], etc.…”

Section: Future Directionsmentioning

confidence: 99%

A review of the application of artificial intelligence to nuclear reactors: Where we are and what's next

Huang

Peng

Deng

et al. 2023

Heliyon

View full text Add to dashboard Cite

Section: Future Directionsmentioning

confidence: 99%

A review of the application of artificial intelligence to nuclear reactors: Where we are and what's next

Huang

Peng

Deng

et al. 2023

Heliyon

View full text Add to dashboard Cite

“…De et al proposed the existing TREPAN decision tree as a surrogate model with an FFNN to generate rules depicting the flow of information within the neural network [89]. Rutkowski et al used the Wang-Mendal (WM) algorithm to generate fuzzy rules to support recommendations with explanations [157]. A novel neuro-fuzzy system, ALMMo-0*, was proposed by Soares et al [116].…”

Section: Rule-based Explanationsmentioning

confidence: 99%

A Systematic Review of Explainable Artificial Intelligence in Terms of Different Application Domains and Tasks

et al. 2022

View full text Add to dashboard Cite

Artificial intelligence (AI) and machine learning (ML) have recently been radically improved and are now being employed in almost every application domain to develop automated or semi-automated systems. To facilitate greater human acceptability of these systems, explainable artificial intelligence (XAI) has experienced significant growth over the last couple of years with the development of highly accurate models but with a paucity of explainability and interpretability. The literature shows evidence from numerous studies on the philosophy and methodologies of XAI. Nonetheless, there is an evident scarcity of secondary studies in connection with the application domains and tasks, let alone review studies following prescribed guidelines, that can enable researchers’ understanding of the current trends in XAI, which could lead to future research for domain- and application-specific method development. Therefore, this paper presents a systematic literature review (SLR) on the recent developments of XAI methods and evaluation metrics concerning different application domains and tasks. This study considers 137 articles published in recent years and identified through the prominent bibliographic databases. This systematic synthesis of research articles resulted in several analytical findings: XAI methods are mostly developed for safety-critical domains worldwide, deep learning and ensemble models are being exploited more than other types of AI/ML models, visual explanations are more acceptable to end-users and robust evaluation metrics are being developed to assess the quality of explanations. Research studies have been performed on the addition of explanations to widely used AI/ML models for expert users. However, more attention is required to generate explanations for general users from sensitive domains such as finance and the judicial system.

show abstract

“…Support for XAI in academia for evaluation tasks are found in [58], [59], and [60]. Lastly, in the entertainment industry XAI for recommender systems is found in the works of [61], [62], and [63].…”

Section: Explainable Artificial Intelligence (Xai)mentioning

confidence: 99%

Explainable Intrusion Detection Systems (X-IDS): A Survey of Current Methods, Challenges, and Opportunities

et al. 2022

View full text Add to dashboard Cite

The application of Artificial Intelligence (AI) and Machine Learning (ML) to cybersecurity challenges has gained traction in industry and academia, partially as a result of widespread malware attacks on critical systems such as cloud infrastructures and government institutions. Intrusion Detection Systems (IDS), using some forms of AI, have received widespread adoption due to their ability to handle vast amounts of data with a high prediction accuracy. These systems are hosted in the organizational Cyber Security Operation Center (CSoC) as a defense tool to monitor and detect malicious network flow that would otherwise impact the Confidentiality, Integrity, and Availability (CIA). CSoC analysts rely on these systems to make decisions about the detected threats. However, IDSs designed using Deep Learning (DL) techniques are often treated as black box models and do not provide a justification for their predictions. This creates a barrier for CSoC analysts, as they are unable to improve their decisions based on the model's predictions. One solution to this problem is to design explainable IDS (X-IDS). This survey reviews the state-of-the-art in explainable AI (XAI) for IDS, its current challenges, and discusses how these challenges span to the design of an X-IDS. In particular, we discuss black box and white box approaches comprehensively. We also present the tradeoff between these approaches in terms of their performance and ability to produce explanations. Furthermore, we propose a generic architecture that considers human-in-the-loop which can be used as a guideline when designing an X-IDS. Research recommendations are given from three critical viewpoints: the need to define explainability for IDS, the need to create explanations tailored to various stakeholders, and the need to design metrics to evaluate explanations.INDEX TERMS Explainable intrusion detection systems, explainable artificial intelligence, machine learning, deep learning, white box, black box, explainability, cybersecurity.

show abstract

On Explainable Fuzzy Recommenders and their Performance Evaluation

Cited by 25 publications

References 22 publications

A review of the application of artificial intelligence to nuclear reactors: Where we are and what's next

A review of the application of artificial intelligence to nuclear reactors: Where we are and what's next

A Systematic Review of Explainable Artificial Intelligence in Terms of Different Application Domains and Tasks

Explainable Intrusion Detection Systems (X-IDS): A Survey of Current Methods, Challenges, and Opportunities

Contact Info

Product

Resources

About