Titlebook: Artificial Intelligence in HCI; 5th International Co Helmut Degen,Stavroula Ntoa Conference proceedings 2024 The Editor(s) (if applicable)

Collision

Artificial Intelligence in HCI978-3-031-60611-3Series ISSN 0302-9743 Series E-ISSN 1611-3349

中子

MEET

Do You Trust AI? Examining AI Trustworthiness Perceptions Among the General Public crucial in human-AI interaction due to perceived risks arising from AI behaviors’ complexity and the technology’s nondeterministic nature. While several studies have examined trust in AI within specific domains and have focused on particular types of AI technology, this study aims to investigate tr

Resistance

Factors of Trust Building in Conversational AI Systems: A Literature Reviewhe service sector of the online world. This paper focuses on trust-building factors in conversational AI systems and explores strategies to strengthen trust. First, an overview of trust, AI, conversational AI systems, and related literature is provided before discussing related literature on the con

packet

BlocklyBias: A Visual Programming Language for Bias Identification in AI Dataday’s rapidly evolving landscape, marked by increasing complexity and challenges, there is a growing need to address the issue of biases and discrimination that can be exacerbated by algorithms. Biases can infiltrate data collection, whether conducted by humans or systems they design, highlighting t

Genome

flex336

Negative Emotions Towards Artificial Intelligence in the Workplace – Motivation and Method for Desigworkplace standards, such as fewer monotonous tasks and greater physical safety. Nevertheless, companies in the manufacturing sector in Germany see employee fears as one of the three main barriers to the use of AI. Against this backdrop, we present the results of a survey of manufacturing employees

Affection

丰满有漂亮

AXIOM

Model-Free Motion Planning of Complex Tasks Subject to Ethical Constraintsment Learning (RL) approach to address ethical constraints in motion planning problems, particularly in complex tasks within partially observable environments. Leveraging the Partially Observable Markov Decision Process (POMDP) for motion planning in environments with incomplete knowledge and Linear