Explainable artificial intelligence: A survey

By Filip Karlo Dosilovic, Mario Brcic, N. Hlupić. 2018

964 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Safety, Robustness & Interpretability

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Citations in the corpus

A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI (2019, methodology): “We extend the so-called integrated interpretability [16] by including considerations for subject-content-dependent models.”
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI) (2018, background): “[64] proposed a general overview of the topic in their conference paper.”
Offensive Language Detection Explained (2020, background): “The former refers to locally comprehending individual decisions, while the latter refers to globally comprehending the decision function (Došilović et al., 2018; Monroe, 2018; Montavon et al., 2017).”
Methods for Segmentation and Classification of Digital Microscopy Tissue Images (2018, background): “This relatively new topic in machine/deep learning in biomedicine is rapidly gaining popularity; there are an increasing number of recent works on defining explainable AI and seeking approaches to realize it (Došilović et al., 2018; Holzinger, 2018; Sadeghi et al., 2018).”
Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence (2023, background): “Dosilovic et al. [70]. provided a general review of XAI in their paper’s closing remarks.”
Explainable AI (XAI): Core Ideas, Techniques, and Solutions (2022, background): “[13], general interpretability and explainability have been discussed but limited to supervised ML models.”
Explainable AI (XAI): A Systematic Meta-Survey of Current Challenges and Future Opportunities (2021, background): “Furthermore, it has been found that similar or identical concepts are called by different names and different concepts are called by the same names [1, 31].”
Globally-Consistent Rule-Based Summary-Explanations for Machine Learning Models: Application to Credit-Risk Evaluation (2019, neutral): “…focus on this research area and refer the reader to survey papers on interpretable and explainable machine learning to other bodies of work (Rudin et al. 2022, Došilović et al. 2018, Guidotti et al. 2018b, Arya et al. 2020, Tjoa and Guan 2020, Roscher et al. 2020, Burkart and Huber 2021).”
A Review on Language Models as Knowledge Bases (2022, background): “While literature in NLP and computer science combine explainability and interpretability as similar, if not, identical aspects (Došilović et al., 2018),”
A comprehensive taxonomy for explainable artificial intelligence: a systematic survey of surveys on methods and concepts (2021, background): “2018 [40] B, R XAI for supervised learning 1”
Explainable Artificial Intelligence: a Systematic Review (2020, methodology): “This opacity has created the need for XAI architectures that is motivated mainly by three reasons, as suggested by [12, 13]: i) the demand to produce more transparent models; ii) the need of techniques that enable humans to interact with them; iii) the requirement of trustworthiness of their inferen”
Explainable Reinforcement Learning: A Survey (2020, neutral): “readability-performance trade-off [12], accuracy-comprehensibility trade-off [16], or accuracy-interpretability trade-off [50].”
Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey (2020, background): “There are many prior publications debating the nuances in defining Explainability and Interpretability of neural networks [44, 45].”
Explaining Toxic Text via Knowledge Enhanced Text Generation (2022, background): “As mentioned earlier, explanations of toxicity can help with downstream tasks such as debiasing or decision making by humans, thus there has been increasing demand for explainable machine learning classifiers (Ribeiro et al., 2016; Došilovi´c et al., 2018).”
Explainability in Graph Neural Networks: A Taxonomic Survey (2020, background): “Recent survey works [23], [24], [25], [26] provides good systematic reviews and taxonomies of these methods.”