Researchers propose framework to regulate AI mental-state inference

Researcher Shinnosuke Horiuchi from Rikkyo University has published a comprehensive paper proposing a novel regulatory framework for AI systems that infer human mental states from neural signals or behavioral data. The work, titled 'Governing Mental-State Inference: Source-Neutral Regulatory Triggers and Tiered Obligations,' challenges traditional source-bound regulations that create loopholes when identical attributions come from different input types.

The paper introduces a source-neutral trigger mechanism that separates the regulatory process into three cumulative stages: elicitation (how data is gathered), attribution (how conclusions are drawn), and use (how results are applied). This creates a tiered system with escalating obligations based on harm pathways and risk levels. High-risk applications like non-consensual closed-loop neural interventions face presumptive prohibition, while lower-risk scenarios follow graduated compliance requirements. The framework specifically addresses the challenge of treating fallible AI outputs as factual representations of human cognition, a growing concern as neurotechnology and affective computing advance rapidly.