Exploring Contrastive Explanations in Formal Argumentation
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With the growing usage of artificial intelligence (AI) in daily life, explainable systems become more important. Explainable AI (XAI), which is a set of tools and frameworks to help you understand and interpret predictions made by AI, has risen in popularity due to this. Formal argumentation is a suitable tool that can be used to model contrastive explanations for (X)AI systems. In this thesis, the concept of contrastiveness will be modelled in various formal argumentation settings. The definition for contrastiveness is based on definitions found in literature from the field of the social sci- ences and humanities. First, an extensive literature research is conducted to find suitable definitions for contrastiveness, then those concepts are modelled in Dung’s abstract argumentation frameworks, preference-based argumentation frameworks and finally in the structured argumentation setting of ASPIC+. These definitions are then evaluated at the hand of examples based on real-life situations. This work successfully models various notions of contrastiveness in various formal argumentation set- tings and paves the way for the further study and application of contrastiveness in (argumentative) XAI.