arXiv:2605.23952v1 Announce Type: cross Abstract: Artificial agents now generate behavior rich enough to invite trust, surprise, and concern, yet our evaluation tools still privilege capability scores over psychological structure. This paper argues that the philosophical impasse between two symmetrical errors (Artificial Mind Blindness, which dismisses psychological organization in non-biological systems, and Artificial Mind Projection, which infers human-like inner life from fluent behavior alone) can be circumvented not by resolving the consciousness question, but by introducing a discipline
As AI models achieve human-like fluency, the need for robust psychological evaluation alongside capability scores becomes critical to understanding their true nature and potential impact.
A 'Mathematical Psychology of Artificial Intelligence' could fundamentally alter how we develop, regulate, and interact with advanced AI, moving beyond superficial performance metrics to deeper understanding.
The focus of AI evaluation shifts from purely 'what it can do' to 'how it thinks' or 'how it is organized psychologically,' enabling more nuanced and responsible AI development.
- · AI ethicists and safety researchers
- · Developers of introspective AI architectures
- · Regulatory bodies focused on AI accountability
- · Developers focused solely on benchmark-driven AI performance
- · Those resisting deeper psychological scrutiny of AI
New metrics and methodologies emerge for assessing AI's 'inner' workings and psychological structures.
AI development paradigms begin to incorporate psychological principles and evaluation from early stages.
Public and policy discussions surrounding AI evolve to address psychological implications, potentially influencing broader societal integration and legal frameworks.
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