The DOI matches its Crossref record; 100 of the 100 references it cites were found in a registry or an index.
Towards Explainable Artificial Intelligence
Open accessAbstract
In recent years, machine learning (ML) has become a key enabling technology for the sciences and industry. Especially through improvements in methodology, the availability of large databases and increased computational power, today’s ML algorithms are able to achieve excellent performance (at times even exceeding the human level) on an increasing number of complex tasks. Deep learning models are at the forefront of this development. However, due to their nested non-linear structure, these powerful models have been generally considered “black boxes”, not providing any information about what exactly makes them arrive at their predictions. Since in many applications, e.g., in the medical domain, such lack of transparency may be not acceptable, the development of methods for visualizing, explaining and interpreting deep learning models has recently attracted increasing attention. This introductory paper presents recent developments and applications in this field and makes a plea for a wider use of explainable learning algorithms in practice.
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Cited by
Explainable AI: Interpreting, Explaining and Visualizing Deep Learning
(2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-28954-6 · Cited by 1,377
Artificial Intelligence and Business Value: a Literature Review
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A Survey on Neural Network Interpretability
Zhang, Y., Tino, P., Leonardis, A., & Tang, K. (2021) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2021.3100641 · Cited by 923
Explainable AI (XAI): A systematic meta-survey of current challenges and future opportunities
Saeed, W., & Omlin, C. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110273 · Cited by 783
Yang, G., Ye, Q., & Xia, J. (2022) · Information Fusion · doi:10.1016/j.inffus.2021.07.016 · Cited by 717
Metadata
- Authors
- Samek, W., & Müller, K.-R.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2019
- Pages
- 5-22
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2972441196
- Cited by
- 580
- References
- 100
- FWCI
- 23.64
- Type
- conference paper
- Language
- en
- Open access
- green
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