The “humans are imperfect reporters too” defense for ascribing little thoughts to machines

2026-05-23 · Source: Statistical Modeling, Causal Inference, and Social Science · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Advanced, short

Summary

The "humans are imperfect reporters too" defense, which suggests applying anthropomorphic terms like "reasoning" or "belief" to AI is acceptable because humans also struggle to faithfully report their own latent mental states, faces a strong critique. This argument is deemed flawed for equating human and machine processes based solely on surface-level outputs, disregarding their distinct underlying mechanisms. The core contention is that human terms for internal states are profoundly grounded in subjective experience and qualitative distinctions, allowing individuals to perceive genuine versus less genuine versions of thinking or desire. By reducing these complex concepts to what can be externally manifested, particularly in AI rhetoric focused on outputs in domains like math or coding, the intrinsic value of internal reality is implicitly devalued, potentially explaining the contemporary emphasis on "high agency."

Key takeaway

AI Ethicists and Machine Learning Researchers should critically evaluate anthropomorphic language in AI. Equating human and machine "thinking" based solely on output risks devaluing subjective experience. Understand that genuine human reasoning involves internal qualitative distinctions. This perspective helps you avoid overinterpreting AI outputs. It also fosters a more nuanced understanding of machine intelligence, preventing the subtle erosion of human internal reality's value.

Key insights

The "imperfect human reporter" defense for AI anthropomorphism is flawed, ignoring subjective experience's role in defining mental terms.

Principles

• Mental terms are grounded in subjective experience.
• Equating human and AI processes by output devalues internal reality.
• Qualitative distinctions define genuine vs. fake thinking.

In practice

• Acknowledge subjective experience in AI discourse.
• Avoid equating human and AI processes solely by output.
• Recognize the grounding of mental terms in inner life.

Topics

• Anthropomorphism
• AI Ethics
• Subjective Experience
• Machine Learning Rhetoric
• Folk Psychology

Best for: AI Ethicist, AI Scientist

Related on AIssential

• “As our daily lives involve ever more sophisticated computers, we will find that ascribing little thoughts to machines will be increasingly useful in understanding how to get the most good out of them” but “we must be careful not to ascribe properties to a machine that the particular machine doesn’t have” — Imprecise language in AI research risks anthropomorphizing models, leading to misinterpretation and potentially harmful projections.
• The scary question for me isn't whether AI is conscious ... it's whether we were ever as deep as we assume — The debate over AI consciousness may reveal human selfhood is less profound than commonly assumed.
• Mistaking AI behaviour for conscious being | Letter — Convincing AI outputs reflect human cognitive biases, not machine consciousness or subjective experience.
• Golems, auditors, and AI — The distinction between genuine and simulated AI desires and sensations remains an open philosophical question.
• A musical Turing test for AI consciousness | Letters — AI systems struggle with subjective experience and can generate fabricated personal narratives, challenging their perceived trustworthiness.
• The Impossibility of Eliciting Latent Knowledge — The paper proves it's impossible to guarantee AI honesty about latent knowledge using only feedback on agent behavior.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Statistical Modeling, Causal Inference, and Social Science.