Explainable Artificial Intelligence: a Systematic Review

By Giulia Vilone, Longo Luca. arXiv.org, 2020

322 citations recorded by the source corpus.

Explainable Artificial Intelligence (XAI) has experienced a significant growth over the last few years. This is due to the widespread application of machine learning, particularly deep learning, that has led to the development of highly accurate models but lack explainability and interpretability. A plethora of methods to tackle this problem have been proposed, developed and tested. This systematic review contributes to the body of knowledge by clustering these methods with a hierarchical classification system with four main clusters: review articles, theories and notions, methods and their evaluation. It also summarises the state-of-the-art in XAI and recommends future research directions.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/1fb5cd122affeb0385e2a087d14be4bac103460e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xinyi Zhou
2. Gesina Schwalbe
3. Christian Walter Peter Omlin
4. Waddah Saeed
5. Xinwei Wu
6. Tianhao Shen
7. Weilong Dong
8. Chuang Liu

Citations in the corpus

• A comprehensive taxonomy for explainable artificial intelligence: a systematic survey of surveys on methods and concepts (2021, background): “Finally and a little earlier, Vilone and Longo (2020) published in 2020 an equally extensive systematic literature survey on XAI in general.”
• Explainable AI (XAI): A Systematic Meta-Survey of Current Challenges and Future Opportunities (2021, background): “General Towards more formalism [1, 31, 10, 32, 9, 33, 26, 2, 34, 8, 35, 36, 37, 18, 38, 39, 40], [41, 42, 43, 44, 45, 46] ∗ Multidisciplinary research collaborations [47, 35, 18, 48, 49, 50], [45, 51, 42] * Explanations and the nature of user experience and expertise [28, 24, 52, 26, 39, 2], [43, 41”
• Large Language Model Alignment: A Survey (2023, background): “Mechanistic interpretability (Vilone and Longo, 2020) refers to elucidating the internal mechanisms by which a machine learning model transforms inputs into outputs, providing causal and functional explanations for how and why certain predictions are made (Nanda, 2022; Lipton, 2017).”
• A Survey of Fake News (2020, methodology): “Facilitating interpretability has been of great interest in artificial intelligence [Vilone and Longo 2020] and ML [Du et al. 2018] research.”
• Interpretable Machine Learning - A Brief History, State-of-the-Art and Challenges (2020, unknown): “Research-wise, the field is maturing in terms of methods surveys [75,41,120,96,1,6,23,15], further consolidation of terms and knowledge [42,22,82,97,88,17], and work about defining interpretability or evaluation of IML methods [74,73,95,49].”
• A Fuzzy Shell for Developing an Interpretable BCI Based on the Spatiotemporal Dynamics of the Evoked Oscillations (2021, background): “)erefore, although the RL is intuitive and does not need an extra XAI, the paradigm of RL requires abstraction and instantiation of the agent, environment, state, action, and reward according to the specific of the learning problem.”