Predicting the long-term impact of AI assistants on collective intelligence
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…
Artificial intelligence (AI) assistants have the potential to boost individual performance, but their widespread use could diminish unaided performance and narrow the range of interests, leading to concerns about their long-term effects on collective intelligence (CI). In this study, researchers developed an evolutionary model where individuals predict, consult AI, and emulate better-performing peers.
Traditional reward structures that foster CI without AI can be undermined by Chatbot AI, as its predictions rely on users' interests, potentially diminishing interest diversity or spreading bias. While directly incentivizing or discouraging AI utilization does not effectively counteract these issues, a balanced reward system that maintains minority expertise and rewards collective contributions could help promote CI.
However, the model also indicates that an additional incentive is required to restore unaided collective accuracy. This research provides an explanation for the growing evidence of AI's societal impact and sheds light on its long-term implications on collective intelligence.
Written by urgent.news from bioRxiv's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
