mlsanityData to 7 Oct 2026

Sources of Opacity in Computer Systems: Towards a Comprehensive Taxonomy

arXiv 2307.14232 ↗First posted 26 Jul 2023cs.SE cs.AIcited by 1

Not on the map. Why?

Abstract (arXiv)

Modern computer systems are ubiquitous in contemporary life yet many of them remain opaque. This poses significant challenges in domains where desiderata such as fairness or accountability are crucial. We suggest that the best strategy for achieving system transparency varies depending on the specific source of opacity prevalent in a given context. Synthesizing and extending existing discussions, we propose a taxonomy consisting of eight sources of opacity that fall into three main categories: architectural, analytical, and socio-technical. For each source, we provide initial suggestions as to how to address the resulting opacity in practice. The taxonomy provides a starting point for requirements engineers and other practitioners to understand contextually prevalent sources of opacity, and to select or develop appropriate strategies for overcoming them.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 16 of its 63 references that are in the map. First posted on arXiv 26 Jul 2023.

Builds on

16 of 63 references.

References · 16

  1. 2013Intriguing properties of neural networkscited by 5,114
  2. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  3. 2013Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Mapscited by 2,348
  4. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,543
  5. 2017Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)cited by 1,309
  6. 2017Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPRcited by 1,086
  7. 2016The Mythos of Model Interpretabilitycited by 716
  8. 2017Network Dissection: Quantifying Interpretability of Deep Visual Representationscited by 703
  9. 2019A Survey of Deep Learning Techniques for Autonomous Drivingcited by 401
  10. 2016European Union regulations on algorithmic decision-making and a "right to explanation"cited by 343
  11. 2018An overview of deep learning in medical imaging focusing on MRIcited by 140
  12. 2021What Do We Want From Explainable Artificial Intelligence (XAI)? -- A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Researchcited by 119
4 more
  1. 2017Explainable AI: Beware of Inmates Running the Asylum Or: How I Learnt to Stop Worrying and Love the Social and Behavioural Sciencescited by 84
  2. 2018The age of secrecy and unfairness in recidivism predictioncited by 53
  3. 2021Exploring Explainability: A Definition, a Model, and a Knowledge Cataloguecited by 15
  4. 2021Contemporary COBOL: Developers' Perspectives on Defects and Defect Locationcited by 3

Built on by

Cited by year

Citing papers per year: 2025: 1.

1 paper.