In general, AI guidance can fall into one of two categories: attention signals and action signals. Attention signals flag decisions that are important without offering a recommendation: “This is a critical decision: pay close attention.” Action signals go further and prescribe a specific action: “Here’s what you should do.” But which type of signal actually helps us make better decisions, especially when the AI is reliable and provides highly accurate advice? This question is increasingly relevant, as AI tools become better calibrated and consistently dependable. As this trend continues, we must ask: Are there costs to relying on AI too much, even when its advice is correct? We explored these questions in a study using chess — a setting where AI recommendations are trusted, accuracy is exceptionally high, and decision quality is easy to measure.
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