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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”
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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.”
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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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