As artificial intelligence becomes more integrated into daily life, understanding how humans perceive and morally evaluate these nonhuman entities is increasingly important. While much research has explored anthropomorphism—attributing human traits to AIs—less attention has been paid to individuation, the process by which humans perceive AIs as distinct individuals rather than interchangeable members of a group. This gap is significant because individuation may influence moral consideration, affecting everything from user trust to ethical frameworks for AI rights.

Exploring Individuation and Moral Consideration

One way to investigate this dynamic would be through controlled experiments examining how humans come to perceive AIs as individuals. Factors tested could include behavioral variability (e.g., chatbots with unique response patterns), visual or auditory distinctiveness (e.g., customizable avatars), or narrative identity (e.g., AIs sharing backstories). The experiments could then measure whether individuation leads to greater moral concern, such as reluctance to "harm" a specific AI or advocacy for ethical treatment of all AIs. Methods might include surveys, behavioral tasks, or longitudinal interactions to track changes in perception over time.

Potential Applications and Stakeholders

The findings could benefit multiple groups:

• AI developers might use insights to design systems that foster appropriate levels of individuation, improving user engagement or ethical alignment.
• Ethicists and policymakers could refine frameworks for AI rights or human-AI interaction norms.
• The general public might gain awareness of how their perceptions shape moral attitudes toward AIs.

Stakeholder incentives vary: researchers may be motivated by academic impact, tech companies by alignment with design goals, and participants by compensation or curiosity.

Execution and Existing Work

A pilot study could test basic individuation manipulations, such as comparing a generic chatbot to one with a name and backstory. Expanded experiments might introduce nuanced factors like AI "preferences" or learning over time, while a longitudinal phase could track sustained interaction effects. This work would fill a niche by isolating individuation, whereas existing research often conflates it with anthropomorphism or general likability. For example, while studies like The Media Equation show humans treat media as social actors, this project would drill into specific mechanisms driving individuality perceptions.

By clarifying how small design choices might have outsized moral implications, this research could bridge psychology, human-computer interaction, and AI ethics.