Solving the Black Box Problem: A Normative Framework for Explainable Artificial Intelligence
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The technical complexity and opacity of algorithmic systems deprives workers of ability to understand and master the system that governs their work, limiting their voice and empowerment.
“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]
Section 3.1 identifies bank employees as operators and decision-makers, and §3.2 says a lack of relevant knowledge can prevent them from performing those roles. The paper does not establish the broader claim that opaque systems governing workers’ work limit their voice or empowerment. Page numbers were not available in our copy. (LLM, conf=0.86)
Experimental — accuracy not yet measuredTax administrations' abundance of data favours AI use: the volume is too large for humans to examine directly, and random errors tend to cancel out, leaving a clear pattern for AI models to learn.
“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]
Information detailing which biomarkers and molecular interactions primarily result in the disease fingerprint is unknown and largely cannot be determined by current machine-learning methods.
“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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- 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)
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Philosophy and History of Science
- Keywords
- explainable AI, black box problem, opacity, normative framework, philosophy of science, feature visualization, diagnostic classification, machine learning transparency
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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