ArgNLE

The First Workshop on Natural Language Argument-Based Explanations

Co-located with ECAI 2024. October 20th 2024, Universidad de Santiago de Compostela.

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Content & Topics

Explainability and Computational Argumentation have usually been approached as separate, independent research topics, which neglects many aspects arising from considering the interdependencies between them. To be effective for human users, explanations are required to be formulated in natural language, possibly in an argumentative fashion. A workshop on exploring Natural language Argument-based Explanations is proposed to investigate this challenging topic, at the crossroad of these different research fields.

Providing high quality explanations for AI predictions based on machine learning is a challenging and complex task. To work well it requires, among other factors: selecting a proper level of generality/specificity of the explanation; considering assumptions about the familiarity of the explanation beneficiary with the AI task under consideration; referring to specific elements that have contributed to the decision; making use of additional knowledge (e.g., metadata) which might not be part of the prediction process; selecting appropriate examples; providing evidence supporting negative hypothesis. Finally, the system needs to formulate the explanation in a clearly interpretable, and possibly convincing, way.

Given these considerations, the workshop welcome contributions showing an integrated vision of Explainable AI (XAI), where low level characteristics of the deep learning process are combined with higher level schemas proper of the human argumentation capacity. These integrated vision relies on three main considerations:

• In neural architectures the correlation between internalstates of the network and the justification of the network classification outcome is not well studied
• High quality explanations are crucially based on argumentation mechanisms (e.g., provide supporting examples and rejected alternatives)
• In real settings, providing explanations is inherently an interactive process involving the system and the user. Accordingly, the workshop calls for cross-disciplinary contributions in three areas, i.e., deep learning, argumentation and interactivity, to support a broader and innovative view of explainable AI

More precisely, the workshop is intended to discuss research challenges that will allow to advance the state of the art in explainable AI. Providing explanations to support a certain conclusion has been largely studied in logic, as a fundamental characteristic of human reasoning. As a result, both theoretical and computational models of human argumentation are investigated. The recent resurgence of AI highlighted the idea that low level system behaviors not only need to be interpretable (e.g., showing those elements that most contributed to the system decision), but also need to fit high level human schemas to produce convincing arguments.

Suggested topics of the workshop include but are not limited to:

• Natural language argument-based explanations
• Neuro-symbolic explainable argumentation
• Dialectical, dialogical and conversational explanations
• AI methods to support argumentative explainability
• User-acceptance and evaluation of argumentation-based explanations
• Tools that provide argumentation-based explanations
• Use of argument-based explanations for research from the social sciences, digital humanities, and related fields
• Real-world applications, including argument-based explanations search, customer reviews, argument analysis in meetings, and applications in specific domains, such as education, law, and scientific writing