A survey on XAI and natural language explanations
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Language is the universal means of communication among humans and machines, and thus AI models using natural language processing and/or counterfactual and natural language explanations are relevant to help humans supervise and take appropriate decisions based on AI outputs.
“In terms of technical tools to reach different audiences, language appears as the universal means of communication among humans and machines, and thus, AI models using natural language processing and/or counterfactual and natural language explanations [27] will be relevant to help humans supervise and take the most appropriate decision based on the output of AI systems.”
From Connecting the dots in trustworthy Artificial Intelligence: From AI principles, ethics, and key requirements to responsible AI systems and regulation (2023) · cites this paper as [27]
Natural language explanations, quantitative measures of explanation quality, and models with formal symbolic basis (neural-symbolic learning and reasoning) are key for explanations to be understood by non-expert audience.
“Natural language explanations [27,67], quantitative measures of the quality of explanations [68,69], and models that support their learning process with formal symbolic basis such as language, rules, compositional relationships or knowledge graphs (neural-symbolic learning and reasoning [66]) are key for explanations to be understood by non-expert audience.”
From Connecting the dots in trustworthy Artificial Intelligence: From AI principles, ethics, and key requirements to responsible AI systems and regulation (2023) · cites this paper as [27]
Cites
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
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Explanation in artificial intelligence: Insights from the social sciences
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Anchors: High-Precision Model-Agnostic Explanations
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Letham, B., Rudin, C., McCormick, T. H., & Madigan, D. (2015) · The Annals of Applied Statistics · doi:10.1214/15-aoas848 · Cited by 766 · see citing works
Notions of explainability and evaluation approaches for explainable artificial intelligence
Vilone, G., & Longo, L. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.05.009 · Cited by 598 · cited 3× · see citing works
“According to Vilone and Longo (2021), 250 relevant methods were proposed only between 2016 and 2020, more than four times the ones proposed in the previous five years.”
BackgroundMethodChaves, A. P., & Gerosa, M. A. (2021) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2020.1841438 · Cited by 572 · see citing works
Intelligible models for classification and regression
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Metrics for explainable AI: challenges and prospects
Hoffman, R. R., Mueller, S. T., Klein, G., & Litman, J. (2018) · arXiv · doi:10.48550/arxiv.1812.04608 · Cited by 518 · see citing works
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Counterfactuals in Explainable Artificial Intelligence (XAI): Evidence from Human Reasoning
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Effectiveness of an Empathic Chatbot in Combating Adverse Effects of Social Exclusion on Mood
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Natural Language Generation enhances human decision-making with uncertain information
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Ali, S., Abuhmed, T., El-Sappagh, S., Muhammad, K., Alonso-Moral, J. M., Confalonieri, R., Guidotti, R., Del Ser, J., Díaz-Rodríguez, N., & Herrera, F. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101805 · Cited by 1,762 · see citing works
Díaz-Rodríguez, N., Del Ser, J., Coeckelbergh, M., López de Prado, M., Herrera-Viedma, E., & Herrera, F. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101896 · Cited by 800 · see citing works
- Authors
- Cambria, E., Malandri, L., Mercorio, F., Mezzanzanica, M., & Nobani, N.
- Affiliations
- Nanyang Technological University (SG)
- Venue
- Information Processing & Management
- Publisher
- Elsevier BV
- Published
- 2023-01
- Volume
- 60
- Issue
- 1
- Pages
- 103111
- ISSN
- 0020-0271 (ISSN-L)
- 0306-4573
- OpenAlex
- W4307212054
- Cited by
- 131
- References
- 14
- Citations by year
- 2026: 26 · 2025: 34 · 2024: 48 · 2023: 22 · 2022: 1
- FWCI
- 8.64
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Information Processing & Management (publishedVersion)
- BOA (University of Milano-Bicocca) (submittedVersion)
- DR-NTU (Nanyang Technological University) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Software › Software Testing and Debugging Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Text Readability and Simplification
- Keywords
- explainable AI, natural language explanations, automated explanation generation
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