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How can I notice growing dependence on AI's approval instead of independent judgment?

E032 · E Evaluating and correcting · Trust, use and disengagement

Key answer

Frequency alone is not the issue; consider whether you retain reasons, accept real-world feedback and can stop seeking approval. Frequent tool use differs from requiring its agreement before deciding.

Full explanation

Fang et al. ran a four-week randomized experiment with 981 people comparing chat modes/topics. Their October 2025 revision reported associations between greater voluntary use, trust/social attraction and higher dependence or problematic use. Usage duration was not randomized and there was no no-AI control, so associations do not establish that more use causes dependence. Study v2, results/discussion/limitations

Observe concrete changes:

· Do you form reasons first or repeatedly ask until receiving “you are right”?

· When AI conflicts with reliable material, real outcomes or trusted people, what do you verify?

· Are repeated rephrasings seeking the same reassurance without new information/action?

· Is pausing difficult, or is use displacing valued work, rest or relationships?

These are habits to observe, not a diagnostic scale. For a low-risk decision, write your judgment, reasons and uncertainty first. After AI advice, record only new facts/tradeoffs changing your view and decide in your own words. Compare with real outcomes rather than comfort alone.

Help inspect reasons and evidence, identifying missed options and costs. I will state my judgment first; then you add information, and I explain my decision. Do not end discussion with agreement alone.

If use persistently disrupts life, adjust boundaries and discuss actual effects with trusted people; AI need not decide whether you are dependent. Observe clearer reasons, progress in real action and continued choice to use or disengage.

References

1. Fang et al. (2025), How AI and Human Behaviors Shape Psychosocial Effects of Extended Chatbot Use: A Longitudinal Randomized Controlled Study, preprint v2, revised October 2, 2025