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Calegari, R., Omicini, A., & Sartor, G. 2020, Explainable and ethical ai: A perspective on argumentation and logic programming. Paper presented at International Conference of the Italian Association for Artificial Intelligence. 
Resource type: Proceedings Article
BibTeX citation key: Calegari2020
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Categories: Artificial Intelligence, Cognitive Science, Computer Science, Decision Theory, Ethics, General, Law
Subcategories: Augmented cognition, Decision making, Deep learning, Human decisionmaking, Machine learning, Neurosymbolic, Psychology of human-AI interaction
Creators: Calegari, Omicini, Sartor
Publisher:
Collection: International Conference of the Italian Association for Artificial Intelligence
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Abstract
In this paper we sketch a vision of explainability of intelligent systems as a logic approach suitable to be injected into and exploited by the system actors once integrated with sub-symbolic techniques.

In particular, we show how argumentation could be combined with different extensions of logic programming – namely, abduction, inductive logic programming, and probabilistic logic programming – to address the issues of explainable AI as well as some ethical concerns about AI.


  
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