Predicting the long-term impact of AI assistants on collective intelligence
Kim, J.; Park, S. W.
Show abstract
Artificial intelligence (AI) assistants can improve individual performance but may weaken unaided performance and limit interest diversity, raising questions about their long-term effects on collective intelligence (CI). Here, we consider an evolutionary model in which individuals predict, consult AI, and imitate better-performing peers. Classic reward structures that promote CI without AI can fail under Chatbot AI, whose predictions depend on users' interests, by reducing interest diversity or propagating bias. Directly rewarding or penalizing AI use does not reliably mitigate these effects. Instead, a balanced incentive that preserves minority expertise and rewards collective contributions can promote CI, but an additional incentive is needed to restore unaided collective accuracy. Our framework offers an explanation for emerging evidence of AI's societal impact and clarifies its long-term implications.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- I judge you by your profit: Judgments of effort exerted by selfand others are influenced by task contingent rewards 94%
- Transmission Chains or Independent Solvers? A Comparative Study of Two Collective Problem-Solving Methods 94%
- The Cost of Appearing Suspicious? Information Gathering in Trust Decisions 94%
Similar papers in this journal
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.