The DOI matches its Crossref record; 51 of the 51 references it cites were found in a registry or an index.
Solving the Black Box Problem: A Normative Framework for Explainable Artificial Intelligence
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“Moreover, the technical complexity and opacity of algorithmic systems187–189 deprives workers of the ability to understand and master the system that governs their work, which limits their voice and enpowerment172,185,190.”
From Understanding and shaping the future of work with self-determination theory (2022) · cites this paper as [188]
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“In this data-rich environment, tax administrations enjoy conditions that are very favourable to the use of AI technologies: 31 (i) the volume of data is so great that its direct examination is not feasible through human labour; 32 and (ii) the large amounts of data mean that any”
From Towards eXplainable Artificial Intelligence (XAI) in Tax Law: The Need for a Minimum Legal Standard (2022) · cites this paper as [32]
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“Information detailing which biomarkers and molecular interactions primarily result in the disease fingerprint is unknown and largely cannot be determined by current machine-learning methods43.”
From Detection of ovarian cancer via the spectral fingerprinting of quantum-defect-modified carbon nanotubes in serum by machine learning (2022) · cites this paper as [43]
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"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939778 · Cited by 17,555
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Intriguing properties of neural networks
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“Why Should I Trust You?”: Explaining the Predictions of Any Classifier
Ribeiro, M., Singh, S., & Guestrin, C. (2016) · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations · doi:10.18653/v1/n16-3020 · Cited by 6,682
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Lipton, Z. C. (2018) · Queue · doi:10.1145/3236386.3241340 · Cited by 5,427
The Mythos of Model Interpretability
Lipton, Z. C. (2016) · arXiv · doi:10.48550/arxiv.1606.03490 · Cited by 5,422
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How the machine “thinks”: Understanding opacity in machine learning algorithms
Burrell, J. (2016) · Big Data & Society · doi:10.1177/2053951715622512 · Cited by 3,196 · cited 4×
“Moreover, in many cases, stakeholders other than developers will require explanations of a particular system ’ s behavior, despite being unwilling or unable to acquire knowledge of learnable parameters (Burrell 2016).”
BackgroundInfluentialFodor, J. A. (1987) · The MIT Press eBooks · doi:10.7551/mitpress/5684.001.0001 · Cited by 2,826
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- Hupkes, D., Veldhoen, S., & Zuidema, W. (2018). Visualisation and’diagnostic classifiers’ reveal how recurrent and recursive neural networks process hierarchical structure. Journal of Artificial Intelligence Research, 61, 907–926. · DOI · Paper page
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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
Transparency and the black box problem: why we do not trust AI
von Eschenbach, W. J. (2021) · Philosophy & Technology · doi:10.1007/s13347-021-00477-0 · Cited by 786
Langer, M., Oster, D., Speith, T., Hermanns, H., Kästner, L., Schmidt, E., Sesing, A., & Baum, K. (2021) · Artificial Intelligence · doi:10.1016/j.artint.2021.103473 · Cited by 652
Longo, L., Brcic, M., Cabitza, F., Choi, J., Confalonieri, R., Ser, J. D., Guidotti, R., Hayashi, Y., Herrera, F., Holzinger, A., et al. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102301 · Cited by 626
Xu, L., Sanders, L., Li, K., & Chow, J. C. L. (2021) · JMIR Cancer · doi:10.2196/27850 · Cited by 571
Metadata
- Authors
- Zednik, C.
- Affiliations
- Otto-von-Guericke-Universität Magdeburg (DE)
- Corresponding author
- Zednik, Carlos
- ORCID
- Zednik, Carlos
- Venue
- Philosophy & Technology
- Published
- 2019-12-20
- Volume
- 34
- Issue
- 2
- Pages
- 265-288
- ISSN
- 2210-5433 (ISSN-L)
- 2210-5441
- OpenAlex
- W2955132579
- Cited by
- 419
- References
- 48
- Citations by year
- 2026: 67 · 2025: 95 · 2024: 87 · 2023: 53 · 2022: 74 · 2021: 29 · 2020: 11 · 2019: 3
- FWCI
- 13.20
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Philosophy & Technology (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Philosophy of Biology and Scientific Explanation
- Keywords
- explainable AI, black box problem, opacity, normative framework, philosophy of science, feature visualization, diagnostic classification, machine learning transparency
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