Tracks and topics

 The topics of interest include (but are not limited to):

MULTIMODAL

• XAI for Multi-modal Data Retrieval, Collection, Augmentation, Generation and Validation: from data explainability to understanding and mitigating data bias.

• XAI for Human-Computer Interaction (HCI): from Explanatory User Interfaces to interactive and interpretable machine learning approaches with human-in-the-loop.

• Augmented Reality for Multi-modal XAI.

• XAI approaches leveraging application-specific domain knowledge: from concepts to large knowledge repositories (ontologies) and corpus.

• Design and Validation of Multi-modal explainers: from endowing explainable models with multi-modal explanation interfaces to measuring model explainability and evaluating quality of XAI systems.

• Quantifying XAI: from defining metrics and methodologies to assess the effectiveness of explanations in enhancing user understanding and trust.

• Large knowledge bases and graphs that can be used for Multi-modal Explanation generation.

• Large language models and their generative power for Multi-modal XAI.

• Proof-of-concepts and demonstrators of how to integrate effective and efficient XAI into real-world human decision-making processes.

• Ethical, Legal, Socio-Economic, and Cultural (ELSEC) Considerations in XAI: Examining ethical implications surrounding the use of high-risk AI applications, including potential biases and the responsible deployment of sustainable “green” AI in sensitive domains.

AFFECTIVE

• Explainable Affective Computing in Healthcare, Psychology and Physiology

• Explainable Affective Computing in Education, Entertainment and Gaming

• Privacy, Fairness and Ethical considerations in Affective Computing and Explainable AI applied in Affective Computing

• Bias in Affective Computing and Explainable AI applied in Affective Computing

• Multimodal (textual, visual, vocal, physiological) Emotion Recognition Systems

• User environments for the design of systems to better detect and classify affect

• Sentiment Analysis and Explainability

• Social Robots and Explainability

• Emotion Aware Recommender Systems

• Accuracy in Emotion Recognition and Explainable AI applied in Affective Computing

• Affective Design

• Machine learning using biometric data to classify biosignals

• Virtual Reality in Affective Computing

• Human-computer interaction (HCI) and human in the loop (HITL) approaches in Affective Computing

INTERACTIVE

• Dialogue-based approaches to XAI

• Use of multiple modalities in XAI systems

• Approaches to dynamically adapt explainability in interaction with a user

• XAI approaches that use a model of the partner to adapt explanations

• Methods to measure and evaluate the understanding of the users of a model

• Methods to measure and evaluate the ability to use models effectively in downstream tasks

• Interactive methods by which a system and a user can negotiate what is to be explained

• Modelling the social functions and aspects of an explanation

• Methods to identify a user’s information and explainability needs