Modern technologies like search engines and social media reshape the information environment in response to user behavior [cinelli_echo_2021, leung_narrow_2025]. As algorithms optimize for relevance and engagement, they construct environments that reflect and reinforce users’ existing search strategies. For instance, when seeking information online, people will often use search terms that narrowly reflect their hypothesis [leung_narrow_2025, e.g., “caffeine health risks (benefits)” instead of “caffeine health effects”;]. Search engines retrieve results that match these queries, effectively validating the user’s biased hypothesis with a skewed sample of resources. As a result, users’ existing beliefs are reinforced. Critically, people do not recognize their queries as biased or spontaneously correct for this.
CNET had already been dealing with credibility damage from its AI-generated articles controversy in early 2024. Then came Google's product review algorithm changes, followed by AI Overviews reducing click-through on the informational queries that drove much of its traffic.
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tool to generate a RELAX NG schema from XML instances. Implemented in
Modular Design and MIM
不同人形机器人应用对性能和可靠性的要求不同。比如此前火爆的用于武术、舞蹈等互动表演类、提供情绪价值的机器人，它的成本控制能力决定了批量的潜力，对性能和可靠性的要求相对宽容。