With the widespread use of Artificial Intelligence (AI) systems, understanding the behaviour of intelligent agents and robots (remote or colocated) is crucial to guarantee a smooth explainer-explainee relationship. This is because it is not straightforward for an explainee (mainly human) to understand an explainer’s state of mind. In the relationship with humans, the explainer could be computers, machines, AI, agents, or robots. The relationship could take several forms: interaction, cooperation, collaboration, team, symbiosis, and/or integration. Explainability in Human-Computer Interaction (HCI) refers to the ability of AI systems to provide transparent and understandable explanations of their functionalities. Most works on XAI focus on delivering sophisticated explanations specifically targeting AI researchers and domain experts, neglecting lay users in general. Explainable AI (XAI) can bring various advantages to HCI: increased trust and satisfaction, improved accountability and transparency, better user engagement, and improved decision-making. However, recent literature has pointed out that the design of XAI systems should consider the user’s background, cognitive skills, and prior knowledge. Thus, various challenges need to be considered: balancing explainability and performance; understanding user needs and preferences; addressing diversity, discrimination, and bias; considering complexity and overhead, and ethical and social implications; textual explanations and generation of model predictions; conducting evaluations and validation; and ensuring privacy, security, and trustworthiness.


explainer-Explainee Interaction analysis with communication/HCI theories/studies
effective communication in xAI with conversational strategies/interactive interfaces
improving explainer-explainee interaction in xAI with feedback loops for mutual understanding
explanation refinement with human feedback loops within AI systems
improving user engagement on AI systems with HCI via active/reinforcement learning
embodiment influence (physical or virtual) on human perception/comprehension of explanations of AI systems
investigation of embodied cognition theories/virtual embodiment techniques in XAI interfaces
considering contextual cues, communication bandwidth, and non-verbal feedback for tailored AI-explanation delivery
investigation of user acceptance effectiveness of anthropomorphic (human-like) AI explainers
crafting realistic explanations of AI systems with embodied AI agents/avatars
comparison of anthropomorphic AI explainers versus virtual agents in delivering explanations
design principles for anthropomorphic/non-anthropomorphic agents in XAI
trust, relatability, and cultural perceptions in explanation delivery.
effective collaboration between humans and robots/agents through xAI
co-creation of models and participatory design approaches for explainable collaborative interfaces
enhancing human-robot trust via explanations
explanations in cooperation/task performance in collaborative scenarios via user-centric evaluations
delivering explanations of AI-system in remove/face-to-face colocated settings
investigation of distance/medium on explanation effectiveness in XAI
adaptive explanation for xAI interfaces for on remote or face-to-face interactions
adaptive explanation for xAI interfaces for remote or face-to-face interactions
natural language explanations via conversational AI systems
dialog-based explanation generation models and context-aware conversational interfaces
impact of agent appearance/behaviour on explainability perception
identification of optimal prompt structures for different XAI tasks and user scenarios
designing prompts/queries techniques for eliciting more interpretable and informative responses from AI models
conversation flow and user engagement metrics for evaluating explanations of AI systems
prompt design evaluation on the interpretability and fidelity of AI model outputs
identificaiton of optimal prompt structures for different XAI tasks and user scenarios
understanding user perception/mental models of AI explanations
exploration of perceptual psychology theories/cognitive science models to enhance XAI interfaces
cognitive load measurement techniques for evaluating explanations of/for AI systems
investigating the impact of explanation formats (text, visual, interactive) on user perception and comprehension in XAI
eye-tracking techniques, and perception-based evaluation metrics for xAI-based explanations
cognitive load measurement techniques for evaluating explanations of/for AI-systems
cognitive load measurement techniques for evaluating explanations of/for AI systems
user-centric evaluation frameworks for assessing the human-centered aspects of XAI
participatory design methodologies, personalized recommendation algorithms, and user-centred approaches for XAI development
cultural diversity, societal norms, and group dynamics in designing socially-aware XAI interfaces
transparency/interpretability roles in AI systems for promoting societal trust
addressing social biases in decision-making, employing social sciences methodologies in XAI
natural language generation models for crafting textual explanations for AI model predictions
leveraging techniques such as attention mechanisms/transformer architectures for crafting explanations
comprehensibility/effectiveness evaluation of textual explanations
enhancing user understanding of model predictions via readability assessments/linguistic analysis
investigating interpretable models for sentiment analysis tasks
sentiment-specific feature extraction techniques for explainability
XAI interfaces for sentiment analysis applications
elucidating the rationale behind sentiment predictions with AI-systems
sentiment visualization methods and sentiment-specific explanation algorithms
explanations for text generation models
elucidating the rationale behind sentiment predictions with AI systems
trade-offs fluency-explainability exploration in text generation tasks
evaluating user preferences/comprehension in text generation explainability interfaces
Context-aware AI explanations in HCI
contextual bandit algorithms, adaptive interface designs for timely/relevant explanations and user-context alignment
AI explanations adaptation to match user mental models/context in HCI
employing adaptive explanation generation methods and context-aware interfaces 
dynamic detail/complexity adjustment of AI explanations based on user expertise
human cognitive load and adaptive granularity control mechanisms for explainability design
assessing the usability/effectiveness of XAI interfaces in HCI using user-centered evaluation methodologies
think-aloud protocols, usability testing, task performance measures for xAI
Assessing the usability/effectiveness of XAI interfaces in HCI using user-centered evaluation methodologies

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