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一篇 Hugging Face 与 Data & Society 合作的立场论文，指出当前 AI agent 设计中的人工监督（HITL）不仅因工作量与复杂度难以有效执行，还会导致监督者技能退化，呼吁在设计与组织层面同等重视监督者的认知需求。
The article introduces an AI dependency quiz grounded in academic research, assessing aspects like cognitive offloading, automation bias, and emotional reliance to evaluate user addiction to AI.
A new MIT-led study in Nature Medicine finds that AI assistance and explainability methods impact skin disease diagnosis accuracy differently depending on user expertise: non-experts over-trust AI explanations, while clinicians perform best with only the model's prediction. The results highlight the need for user-centered AI design that accounts for automation bias.
This essay argues that the greatest risk of AI is not hallucinations but the gradual erosion of human verification skills, leading to a civilization that cannot question AI outputs.
A thought piece arguing that as AI becomes more accurate, human oversight may degrade into routine approval, creating a 'Trust–Oversight Paradox' where high-performing AI can still fail due to incomplete representation, stale data, or automation bias, suggesting a shift from human review to governing boundaries.