Not All Explanations Are Sought: Information-Seeking Psychology for Human-Centered XAI
This position paper argues that human-centered explainable AI (HCXAI) should incorporate insights from the psychology of information seeking. Drawing on Sharot and Sunstein's framework of information-seeking motives, we propose that people evaluate whether to engage with explanations based on three types of expected utility: instrumental (will it help me act better?), hedonic (will it make me feel better?), and cognitive (will it improve my understanding?). Each utility is estimated through a lens shaped by well-documented cognitive biases, including illusion of control, automation bias, unrealistic optimism, impact bias, overconfidence, and confirmation bias. These biases can lead to two...
| Authors | Andrea Beretta, Salvatore Rinzivillo |
|---|---|
| Primary category | cs.AI |
| Published | 15 July 2026 |
| Last updated at source | 15 July 2026 |
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