Facchini, Alessandro and Termine, Alberto
(2021)
A First Contextual Taxonomy for the Opacity of AI Systems.
[Preprint]
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 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 propose to view opacity as a pragmatic, or contextual, concept. Based on this, we then explicit the distinction between access opacity, link opacity and semantic opacity, hence providing the groundwork for a conceptual taxonomy of the concept of opacity for AI systems.
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A First Contextual Taxonomy for the Opacity of AI Systems. (deposited 25 Mar 2022 04:26)
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