People's self-reported encounters of Perceiving Mind in Artificial Intelligence

Area of Science:

• Cognitive Science
• Human-Computer Interaction
• Artificial Intelligence Ethics

Background:

• The increasing prevalence of artificial intelligence (AI) systems in daily life necessitates understanding human perception of AI.
• Previous research has explored AI capabilities, but less is known about how people attribute 'mind' to AI based on perceived attributes.

Purpose of the Study:

• To investigate how people describe their everyday encounters with AI systems that exhibit attributes of mind.
• To collect qualitative data on human perceptions of AI, focusing on specific mental and human-like characteristics.

Main Methods:

• Conducted two surveys presenting participants with prompts about AI attributes (e.g., planning, memory, emotion, human-like features).
• Collected qualitative descriptions of personal encounters with AI systems.
• Coded and categorized qualitative responses, alongside quantitative measures of mind perception and demographics.

Main Results:

• Participants provided detailed qualitative accounts of AI encounters, often attributing mental states and human-like qualities.
• Survey 1 focused on cognitive and functional attributes, while Survey 2 explored emotional and social attributes.
• Data includes qualitative responses, coded categories, and quantitative measures of mind perception.

Conclusions:

• Human perception of AI mind is shaped by specific, observable attributes and everyday interactions.
• The findings provide insight into the psychological mechanisms underlying human-AI relationships.
• This research lays groundwork for understanding the societal impact of AI and the development of human-centered AI systems.