Multimodal XAI 

• 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 and machine-in-the-loop approaches

• 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 the quality of XAI systems

• Quantifying XAI: From defining metrics and methodologies to assessing the effectiveness of explanations in enhancing user understanding, reliance, 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 XAI

• Explainable affective computing in healthcare, psychology, physiology, education, entertainment, and gaming

• Privacy, fairness, and ethical considerations 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 and explainability in emotion recognition

• 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 XAI

• 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

• XAI approaches for collaborative decision-making between humans and AI models

• 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 users’ information and explainability needs