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Towards a Taxonomy for the Opacity of AI Systems

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
Creators:
CreatorsEmailORCID
Facchini, Alessandroalessandro.facchini@idsia.ch0000-0001-7507-116X
Termine, Albertoalberto.termine@unimi.it0000-0001-5993-0948
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
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
Date: 1 April 2022
URI: https://philsci-archive-dev.library.pitt.edu/id/eprint/20468

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