The Building Blocks of Interpretability
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Interpretability methods would be required to determine whether the system has acquired a learned model of output-input contingencies in the course of training.
“Interpretability methods, such as examining the information encoded in hidden layers (Olah et al. 2018), would be required to determine whether the system has acquired such a model in the course of training.”
From Consciousness in Artificial Intelligence: Insights from the Science of Consciousness (2023) · cites this paper as Olah et al. 2018
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- Authors
- Olah, C., Satyanarayan, A., Johnson, I., Carter, S., Schubert, L., Ye, K., & Mordvintsev, A.
- Affiliations
- Google (United States) (US)
- Google Cloud
- Carnegie Mellon University (US)
- Venue
- Distill
- Publisher
- Distill Working Group
- Published
- 2018-03-06
- Volume
- 3
- Issue
- 3
- ISSN
- 2476-0757 (ISSN-L)
- OpenAlex
- W2792641098
- Cited by
- 591
- References
- 4
- Citations by year
- 2026: 23 · 2025: 31 · 2024: 36 · 2023: 60 · 2022: 71 · 2021: 89 · 2020: 112 · 2019: 101 · 2018: 65 · 2017: 1 · 2014: 1 · 2013: 1
- FWCI
- 51.36
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Distill (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Social Sciences › Decision Sciences › Information Systems and Management › Scientific Computing and Data Management
- Keywords
- interpretability, explainable AI, model interpretability
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