Towards Explainable AI Using Similarity: An Analogues Visualization System

Segura, Vinícius C. V. B.; Brandão, Bruna; Fucs, Ana; Brazil, Emilio Vital

doi:10.1007/978-3-030-23541-3_28

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…Some of the works considered diverse forms of data altogether, such as the works of Ribeiro et al [96], Alonso et al [52] and Lundberg et al [59], who proposed methods that can deal with the input types, images, texts, and vectors. Another proposed method was developed to learn on graphs and vectors containing numbers [175]. In addition to the mentioned forms of input data, a specialised form of input data was observed, namely the logic scoring preference (LSP) criteria [103], which was later counted as numbers due to apparent similarity.…”

Section: Input Datamentioning

confidence: 99%

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

et al. 2022

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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.

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