In recent years, research on intelligent systems that can explain their inferences and decisions to (human and machine) users has emerged as an important subfield of Artificial Intelligence (AI). In this context, the interest in symbolic and hybrid approaches to AI and their ability to facilitate explainable and trustworthy reasoning and decision-making -- often in combination with machine learning algorithms -- is increasing. Computational argumentation is considered a particularly promising paradigm for facilitating explainable AI (XAI). This trend is reflected by the fact that many researchers who study argumentation have started to:

apply argumentation as a method of explainable reasoning;
combine argumentation with other subfields of AI, such as knowledge representation and reasoning (KR) and machine learning (ML), to facilitate the latter's explainability;
study explainability properties of argumentation.

Given the substantial interest in these different facets of argumentative XAI, this workshop aims at providing a forum for focused discussions of the recent developments on the topic. The workshop will feature works and discussions on diverse perspectives of argumentative explainability. Specifically, we will cover the formal foundations of explaining argumentative inferences and argumentative properties of explanations, as well as applications of argumentation to facilitate explainability. The event would appeal to the growing part of the core argumentation community that works on explainable argumentation, as well as to applied researchers who intend to use computational argumentation for explainability purposes.

To get an overview of argumentation and XAI, interested researchers may consult Francesca Toni's invited talk about explainable reasoning at KR 2021, as well as the following two survey papers:

Argumentation and Explainable Artificial Intelligence: A Survey. The Knowledge Engineering Review. 2021.
Argumentative XAI: A Survey. 30th International Joint Conference on Artificial Intelligence. 2021.

Topics

Topics include, but are not limited to:

Argumentative Explainability
- Formal definitions of explanations
- Computational properties of explanations
- Neuro-symbolic explainable argumentation
- Explanation as a form of argumentation
- Human intelligibility of formal argumentation
- Dialectical, dialogical and conversational explanations
- AI methods to support argumentative explainability
Argumentation for XAI
- Applications of argumentation for explainability in the fields of AI (e.g. machine learning, machine reasoning, multi-agent systems, natural language processing) and overlapping fields of research (e.g. optimisation, human-computer interaction, philosophy and social sciences)
- User-acceptance and evaluation of argumentation-based explanations
- Software systems that provide argumentation-based explanations
- Other defeasible reasoning approaches to XAI

Program (TBD)

The program will feature paper presentations followed by general discussions, invited talks/keynotes by Roberta Calegari and Nick Bassiliades, and an open or panel discussion on whether explanations should be argumentative or social.

Submission

Submissions should be up to 12 pages in PDF format, including abstract, figures and references, and according to the CEUR-WS template (single column). The reviewing will be single-blind. All submissions will be made electronically, here through the EasyChair conference system.

Accepted papers will be included in CEUR-WS workshop proceedings after a careful review process. At least one of the authors will be required to register and attend the COMMA conference to present the paper in order for it to be included in the workshop proceedings.