Argumentation Theoretical Frameworks for Explainable Artificial Intelligence (2020)

Abstract: This paper discusses four major argumentation theoretical frameworks with respect to their use in support of explainable artificial intelligence (XAI). We consider these frameworks as useful tools for both system-centred and user-centred XAI. The former is concerned with the generation of explanations for decisions taken by AI systems, while the latter is concerned with the way explanations are given to users and received by them.

## introduction the enforcement of gdpr ( https://gdpr-info . eu/) by the eu has made explainable artificial intelligence (xai) into a rapidly growing area of research over the last two years. while there is no standard definition of explainable ai systems yet, the need itself is undisputed as evidenced by the gdpr requirements. also, there is agreement that explainability for ai systems is as diverse as the systems themselves. neerincx et al. have defined three phases in the explanation of an ai system: (1) explanation generation, (2) explanation communication, and (3) explanation reception @xcite . based on this, recent xai literature can be divided into two types: system-centred and user-centred xai. system-centred xai is focused on phase 1. broadly, systems fall into two main categories: black-box subsymbolic systems such as those based on deep learning and white-box symbolic systems like decision trees or rule-based. a consequence of the gdpr implementation has been a recent explosion in grey-box systems, which aim to add some symbolic layer to black-box systems to add transparency @xcite @xcite . user-centred xai, which is concerned with aspects related to user-interaction and experience @xcite , is mainly focused on phases 2 and 3 and aims to integrate a user into the loop of an ai system's decision making as much as possible @xcite . phase 2 deals with what is exactly to be provided to the end-user and how to present it, while phase 3 is concerned with the level of understanding that is achieved in an end-user with an explanation. for these varying tasks identified within systemcentred and user-centred xai, it is useful to consider which argumentation theoretical framework can best provide the output that is most effective in a particular setting. in this paper, we briefly discuss the roles that some of the main argumentation theories can play for both system-centred and user-centred xai approaches. section 2 presents the role of dung's theories and walton's dialogue for achieving system-centred xai, while section 3 explores how pragma-dialectics and inference anchoring theory contribute towards user-centred xai. finally, section 4 makes some final observations about the suitability of each theory to xai. ## system-centred xai most of the literature on system-centred xai does not differentiate between interpretability and explainability of learning models. guidotti et al. @xcite consider explainability as an interface between interpretable models and human users. they formalise four types of explanations for black boxes: (1) simulating them with an equivalent symbolic model that explains its working, (2) explaining only the black-box outcome rather than its working, (3) providing visual representation of the black-box mechanism for inspection, and (4) a transparent model that is fully explainable on its own without needing any supplementary explanations model. rudin makes a distinction between interpretable ml and explainable ml @xcite where the latter involves the first three types of explanations as identified by guidotti et al. while the former includes the last type. based on this discussion, recent approaches to system-centred xai can be classified into two main types: interpretable and non-interpretable. interpretable black-box models can either be purely symbolic models or grey-box models, that is, those that generate intermediate symbols which can be leveraged for generating a trace of the reasoning process used by the model. non-interpretable models will then refer to blackboxes for which only input and output are available. thus, achieving explainability nails down to answering the question of how to generate or extract the symbols out of the black-boxes that make up the explanations. in the next two sections, we explore some preliminary ideas on how abstract argumentation framework (af) and dialogue theory can help us answer this question. ## user-centred xai user-centred xai focuses on the way explanations generated by ai systems are communicated to nonexpert and non-technical users, who often do not require a full understanding of the inner workings of the system they are interacting with. instead, this type of user will be primarily interested in natural language explanations that are maximally understandable, that build trust and confidence in the system's recommendations, and that inform a user about how to alter the outcome of a decision @xcite . for example, when an ai system rejects a user's application for a bank loan, it should explain in natural language which variable (e.g. salary, or outstanding debt) is responsible for this output and what is needed in order to be eligible for a loan. providing explanations to non-expert users of ai systems is widely recognised as an essential component in xai, but adapting these explanations to the particular needs of a user and the communicative setting in which they occur remains a challenging task @xcite . in order to endow ai systems with trustworthy and realistic interactive capabilities it is necessary to model the dialogical setting in which users interact with ai systems and to determine which types of communication and reasoning are most effective for informing users. the following two sections discuss the pragma-dialectical theory of argumentation and inference anchoring theory, which are theoretical frameworks for modelling argumentation and reasoning in natural language. ## discussion and future work in this paper, we have differentiated between system-centred and user-centred xai, and discussed how four major argumentation theoretical frameworks can be applied to these challenges. depending on the type of explanation required from an ai system, it is useful to consider the various theoretical tools that these approaches offer. abstract argumentation and dialogue theory excel in generating explanations of the inner workings of an ai system and modelling inter-system interaction. pragma-dialectics and inference anchoring theory are especially suited towards modelling the dialogical setting of human-ai interaction and identifying which type of reasoning is most effective there. future work on system-centred xai could explore how abstract argumentation framework and dialogue theory can be used in a multi-agent recommender system. in this case, the goal is to achieve explainability for the joint recommendation made by multiple systems after consensus. however, in order to achieve consensus, we need dialogue between the different systems. in this context, we can explore using abstract argumentation framework for justifying the recommendation and dialogue theory for achieving consensus on the recommendation itself. for user-centred xai, we propose to investigate how pragma-dialectics and inference anchoring theory can be applied for modelling users in social media. to this end, natural language processing techniques such as argument mining can help create an image of a user's linguistic profile, which provides insight into their communicative behaviour and reasoning patterns (i.e. argumentation schemes). in turn, these argumentation schemes can form a blueprint for the generation of arguments and explanations that are tailored to a specific communicative situation and a particular user. in that capacity, argumentation schemes carry substantial value for tasks in explainable ai related to language generation, inter-agent communication, and personalising ai systems to end users. to conclude, in order to further improve our understanding of, and our interaction with ai systems, we believe it is fruitful to build on existing argumentation theoretical frameworks in various ways towards more robust and accurate methods for explainable artificial intelligence.