Facchini, Alessandro and Termine, Alberto (2022) Towards a Taxonomy for the Opacity of AI Systems. [Preprint]
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Abstract
The research program of eXplainable AI (XAI) has been developed
with the aim of providing tools and methods for reducing opacity and making AI systems more humanly understandable. Unfortunately, the majority of XAI scholars actually classify a system as more or less opaque by confronting it with traditional AI systems such as linear regression models or rules-based systems, which
are usually assumed to be the prototype of transparent systems. In doing so, the concept of opacity remains unexplained. To overcome this issue, we view opacity as a concept whose meaning depends on the context of application, and on the purposes and characteristics of its users. Based on this, in this work, we distinguish between access opacity, link opacity and semantic opacity, hence providing the groundwork for a taxonomy of the concept of opacity for AI systems.
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| Item Type: | Preprint | |||||||||
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| Keywords: | Opacity, Machine Learning, Explainable AI, Scienti�c Understanding | |||||||||
| Subjects: | General Issues > Data Specific Sciences > Computer Science General Issues > Explanation Specific Sciences > Artificial Intelligence > Machine Learning |
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| Depositing User: | Mr. Alberto Termine | |||||||||
| Date Deposited: | 21 Apr 2022 04:04 | |||||||||
| Last Modified: | 21 Apr 2022 04:04 | |||||||||
| Item ID: | 20468 | |||||||||
| Subjects: | General Issues > Data Specific Sciences > Computer Science General Issues > Explanation Specific Sciences > Artificial Intelligence > Machine Learning |
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| Date: | 1 April 2022 | |||||||||
| URI: | https://philsci-archive.pitt.edu/id/eprint/20468 |
Available Versions of this Item
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A First Contextual Taxonomy for the Opacity of AI Systems. (deposited 25 Mar 2022 04:26)
- Towards a Taxonomy for the Opacity of AI Systems. (deposited 21 Apr 2022 04:04) [Currently Displayed]
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