This volume contains revised versions of the papers selected for the first volume of the Online Handbook of Argumentation for AI (OHAAI). Previously, formal theories of argument and argument interaction have been proposed and studied, and this has led to the more recent study of computational models of argument. Argumentation, as a field within artificial intelligence (AI), is highly relevant for researchers interested in symbolic representations of knowledge and defeasible reasoning. The purpose of this handbook is to provide an open access and curated anthology for the argumentation research community. OHAAI is designed to serve as a research hub to keep track of the latest and upcoming PhD-driven research on the theory and application of argumentation in all areas related to AI.

This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. Would you trust an artificial intelligence algorithm that works eerily well, making accurate decisions 99.9 percent of the time, but is a mysterious black box? Every system fails every now and then, and when it does, we want explanations, especially when human lives are at stake. And a system that can't be explained can't be trusted. That is one of the problems the AI community faces as their creations become smarter and more capable of tackling complicated and critical tasks.

In this paper we describe an argumentation-based representation of normal form games, and demonstrate how argumentation can be used to compute pure strategy Nash equilibria. Our approach builds on Modgil's Extended Argumentation Frameworks. We demonstrate its correctness, prove several theoretical properties it satisfies, and outline how it can be used to explain why certain strategies are Nash equilibria to a non-expert human user.

Explainable AI provides insight into the "why" for model predictions, offering potential for users to better understand and trust a model, and to recognize and correct AI predictions that are incorrect. Prior research on human and explainable AI interactions has focused on measures such as interpretability, trust, and usability of the explanation. Whether explainable AI can improve actual human decision-making and the ability to identify the problems with the underlying model are open questions. Using real datasets, we compare and evaluate objective human decision accuracy without AI (control), with an AI prediction (no explanation), and AI prediction with explanation. We find providing any kind of AI prediction tends to improve user decision accuracy, but no conclusive evidence that explainable AI has a meaningful impact. Moreover, we observed the strongest predictor for human decision accuracy was AI accuracy and that users were somewhat able to detect when the AI was correct versus incorrect, but this was not significantly affected by including an explanation. Our results indicate that, at least in some situations, the "why" information provided in explainable AI may not enhance user decision-making, and further research may be needed to understand how to integrate explainable AI into real systems.

As artificial intelligence (AI) matures and new applications boom amid a transition to Industry 4.0, we are beginning to accept that machines can help us make decisions more effectively and efficiently. But, at present, we don't always have a clear insight into how or why a model made those decisions – this is'blackbox AI'. In light of alleged bias in AI models in applications across recruitment, loan decisions, and healthcare applications, the ability to effectively explain the workings of decisions made by AI model has become imperative for the technology's further development and adoption. In December last year, the UK's Information Commissioner's Office (ICO) began moving to ensure businesses and other organizations are required to explain decisions made by AI by law, or face multimillion-dollar fines if unable. Explainable AI is the concept of being able to describe the procedures, services, and outcomes delivered or assisted by AI when that information is required, such as in the case of accusations of bias.

Reputation is crucial to enabling human or software agents to select among alternative providers. Although several effective reputation assessment methods exist, they typically distil reputation into a numerical representation, with no accompanying explanation of the rationale behind the assessment. Such explanations would allow users or clients to make a richer assessment of providers, and tailor selection according to their preferences and current context. In this paper, we propose an approach to explain the rationale behind assessments from quantitative reputation models, by generating arguments that are combined to form explanations. Our approach adapts, extends and combines existing approaches for explaining decisions made using multi-attribute decision models in the context of reputation. We present example argument templates, and describe how to select their parameters using explanation algorithms. Our proposal was evaluated by means of a user study, which followed an existing protocol. Our results give evidence that although explanations present a subset of the information of trust scores, they are sufficient to equally evaluate providers recommended based on their trust score. Moreover, when explanation arguments reveal implicit model information, they are less persuasive than scores.

Dung's abstract argumentation theory plays a guiding role in the field of formal argumentation. The properties of argumentation semantics have been deeply explored in the previous literature. The SCC-recursiveness principle is a property of the extensions which relies on the graph-theoretical notion of strongly connected components. It provides a general recursive schema for argumentation semantics, which is an efficient and incremental algorithm for computing the argumentation semantics. However, in argumentation frameworks with uncertain arguments and uncertain attack relation, the SCC-recursive theory is absence. This paper is an exploration of the SCC-recursive theory in fuzzy argumentation frameworks (FAFs), which add numbers as fuzzy degrees to the arguments and attacks. In this paper, in order to extend the SCC-recursiveness principle to FAFs, we first modify the reinstatement principle and directionality principle to fit the FAFs. Then the SCC-recursiveness principle in FAFs is formalized by the modified principles. Additionally, some illustrating examples show that the SCC-recursiveness principle also provides an efficient and incremental algorithm for simplify the computation of argumentation semantics in FAFs.

With the recent advances in the field of artificial intelligence, an increasing number of decision-making tasks are delegated to software systems. A key requirement for the success and adoption of such systems is that users must trust system choices or even fully automated decisions. To achieve this, explanation facilities have been widely investigated as a means of establishing trust in these systems since the early years of expert systems. With today's increasingly sophisticated machine learning algorithms, new challenges in the context of explanations, accountability, and trust towards such systems constantly arise. In this work, we systematically review the literature on explanations in advice-giving systems. This is a family of systems that includes recommender systems, which is one of the most successful classes of advice-giving software in practice. We investigate the purposes of explanations as well as how they are generated, presented to users, and evaluated. As a result, we derive a novel comprehensive taxonomy of aspects to be considered when designing explanation facilities for current and future decision support systems. The taxonomy includes a variety of different facets, such as explanation objective, responsiveness, content and presentation. Moreover, we identified several challenges that remain unaddressed so far, for example related to fine-grained issues associated with the presentation of explanations and how explanation facilities are evaluated.

Many important questions about a model cannot be answered just by explaining how much each feature contributes to its output. To answer a broader set of questions, we generalize a popular, mathematically well-grounded explanation technique, Shapley Additive Explanations (SHAP). Our new method - Generalized Shapley Additive Explanations (G-SHAP) - produces many additional types of explanations, including: 1) General classification explanations; Why is this sample more likely to belong to one class rather than another? 2) Intergroup differences; Why do our model's predictions differ between groups of observations? 3) Model failure; Why does our model perform poorly on a given sample? We formally define these types of explanations and illustrate their practical use on real data.

We propose answer-set programs that specify and compute counterfactual interventions as a basis for causality-based explanations to decisions produced by classification models. They can be applied with black-box models and models that can be specified as logic programs, such as rule-based classifiers. The main focus is on the specification and computation of maximum responsibility causal explanations. The use of additional semantic knowledge is investigated.