Mapping Knowledge Representations to Concepts [Elektronisk resurs] A Review and New Perspectives
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Holmberg, Lars (författare)
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36th AAAI Conference on Artificial Intelligence, February 28-March 1 2022, Vancouver, Canada
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Davidsson, Paul (författare)
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Linde, Per (författare)
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Malmö universitet Fakulteten för teknik och samhälle (TS) (utgivare)
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Malmö universitet Internet of Things and People (IOTAP) (utgivare)
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Malmö universitet Fakulteten för kultur och samhälle (KS) (utgivare)
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Malmö universitet Fakulteten för kultur och samhälle (KS) (utgivare)
- Publicerad: 2022
- Engelska.
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Ingår i: Explainable Agency in Artificial Intelligence Workshop Proceedings. ; 61-70
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Sammanfattning
Ämnesord
Stäng
- The success of neural networks builds to a large extent on their ability to create internal knowledge representations from real-world high-dimensional data, such as images, sound, or text. Approaches to extract and present these representations, in order to explain the neural network's decisions, is an active and multifaceted research field. To gain a deeper understanding of a central aspect of this field, we have performed a targeted review focusing on research that aims to associate internal representations with human understandable concepts. In doing this, we added a perspective on the existing research by using primarily deductive nomological explanations as a proposed taxonomy. We find this taxonomy and theories of causality, useful for understanding what can be expected, and not expected, from neural network explanations. The analysis additionally uncovers an ambiguity in the reviewed literature related to the goal of model explainability; is it understanding the ML model or, is it actionable explanations useful in the deployment domain?
Ämnesord
- Natural Sciences (hsv)
- Computer and Information Sciences (hsv)
- Computer Sciences (hsv)
- Naturvetenskap (hsv)
- Data- och informationsvetenskap (hsv)
- Datavetenskap (datalogi) (hsv)
Genre
- government publication (marcgt)
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