Algorithmic aesthetics: Cognitive perspectives on AI-generated visual art

Area of Science:

• Neuroaesthetics
• Social Cognition
• Human-Machine Interaction

Background:

• Generative artificial intelligence (AI) is increasingly used in art creation, from exhibitions to auctions.
• Despite AI art's prevalence, empirical evidence shows a bias against it when its origin is known.
• This bias poses questions about the future acceptance and celebration of AI-generated art.

Purpose of the Study:

• To investigate the underlying reasons for public bias against AI-generated visual art.
• To determine if this bias hinders the eventual acceptance and celebration of AI art.
• To develop evidence-based propositions for understanding the human-AI art relationship.

Main Methods:

• Synthesizing empirical evidence from neuroaesthetics, social cognition, art history, and human-machine interaction.
• Developing novel perspectives on AI art bias rooted in social cognitive neuroscience.
• Proposing new avenues for empirical evaluation of AI art acceptance.

Main Results:

• Identified a clear public bias against AI-generated visual art when its artificial origin is disclosed.
• Highlighted the need for further research into the psychological and social factors influencing AI art perception.
• Established a framework for understanding AI art acceptance through a social cognitive neuroscience lens.

Conclusions:

• The bias against AI art is a significant factor influencing its reception.
• Understanding the cognitive and social underpinnings of this bias is crucial for its potential mitigation.
• Further empirical research is needed to fully grasp the complex relationship between humans and AI in the realm of art.