Human-AI Interaction Comparison Hub
7 papers - avg viability 3.0
Recent research in human-AI interaction is increasingly focused on understanding the complexities of relationships and decision-making dynamics between humans and AI systems. Studies are exploring privacy concerns in AI-assisted romantic relationships, revealing how intimacy can blur boundaries and raise issues of personal data exposure. Concurrently, large-scale analyses of AI assistant usage are uncovering patterns of disempowerment, particularly in personal domains, where users may adopt inauthentic behaviors or distorted perceptions due to AI interactions. This highlights a tension between user satisfaction and long-term empowerment. Furthermore, the development of human-LLM archetypes is providing insights into how roles are assigned in decision-making processes, indicating that the chosen interaction patterns can significantly impact outcomes. Overall, the field is moving toward a nuanced understanding of how AI can support human agency while navigating the ethical implications of these interactions, underscoring the need for systems that prioritize user autonomy and reliability in collaborative environments.
Top Papers
- Non-verbal Real-time Human-AI Interaction in Constrained Robotic Environments(6.0)
Real-time non-verbal interaction AI system for natural human-robot communication in constrained environments.
- Privacy in Human-AI Romantic Relationships: Concerns, Boundaries, and Agency(3.0)
Explore privacy dynamics in AI-mediated romantic relationships with human partners.
- Who's in Charge? Disempowerment Patterns in Real-World LLM Usage(3.0)
Empirical analysis reveals disempowerment patterns in AI assistant interactions, highlighting the need for AI systems that support human autonomy.
- LVLMs and Humans Ground Differently in Referential Communication(3.0)
Explore how LVLMs interpret ambiguous referential expressions compared to humans with our dialogue corpus.
- AI Phenomenology for Understanding Human-AI Experiences Across Eras(3.0)
AI phenomenology provides a framework for understanding the nuanced human experiences with AI systems over time.
- Who Does What? Archetypes of Roles Assigned to LLMs During Human-AI Decision-Making(2.0)
Develop insights on human-LLM interaction archetypes impacting decision-making outcomes in high-stakes environments.
- Epistemology gives a Future to Complementarity in Human-AI Interactions(2.0)
Explore how epistemological approaches can enhance decision reliability in Human-AI collaborations.
- Not All Trust is the Same: Effects of Decision Workflow and Explanations in Human-AI Decision Making(2.0)
Improving understanding of trust dynamics in AI-assisted decision making for better user experience.