This Research Topic is the second volume of the Research Topic "On the “Human” in Human-Artificial Intelligence Interaction". Please see the first volume
here.
Artificial Intelligence (AI) and Machine Learning (ML) are now part of our lives and both of them lie at the core of many activity sectors that have embraced new information technologies. AI refers to the simulation of human intelligence by computers so that computers are trained to sense, reason, act, and adapt as humans do. Professionals in contexts ranging from healthcare to business will increasingly interact directly with AI to solve complex problems, make decisions (e.g., medical diagnosis), take actions, or will apply modern AI techniques and tools for effectively analyzing human-machine and human-human interactions. Despite AI having a major impact on everyone’s life, knowledge regarding users’ attitudes and behavior towards AI, as well as intrinsic and apparent properties of AI systems, is still limited.
One important issue is trust. Many professionals express uncertainties about using AI to make decisions that will affect the real world, especially when those may have consequences on their careers if unforeseen errors occur. Another relevant issue is education: involvement of AI in many professional contexts may affect formation about changes in practices and policies, yet it is still to be understood what skills, knowledge, and critical thinking should be promoted in those who are supposed to employ AI solutions. Finally, another issue is the study of User Experience and how-to-design interfaces when it comes to the AIs of the future, taking into account that future technologies should be able to justify their own contributions to problem-solving so that those could be understood and trusted (XAI, eXplainable Artificial Intelligence) leading to desirable outcomes.
We are interested in topics such as:
• Collaborative decision making involving AI
• Multimodal pattern analysis
• Human behavior analysis from visual and multimodal information
• Human-centered AI
• Affective computing, automatic emotion detection, analysis, and recognition
• Attitudes, opinions, misconceptions, and emotions towards AI
• AI acceptance within professional fields (e.g., healthcare) and technology implementation
• Users’ interaction with AI, and the development of effective user interfaces
• Multi-criteria metrics to analyze human-AI interaction and/or the selection of personnel using AI within specific professional contexts
• The impact of user behavior on work processes mediated by AI
• The issues of accountability, error, and explanation in AI development and implementation
• AI usage ethics
• AI user training and education
• Cognitive interaction and understanding
• Visual attention models and systems, gaze analysis
• Crowd, scene, and social behavior understanding
• Innovative human - virtual agent interfaces
