---
id: "quote-stop-asking-why"
type: "quote"
source_timestamps: ["§ How to Use Explainable AI Responsibly"]
tags: ["critical-thinking", "long-term-risks"]
related: ["concept-algorithmic-override", "action-encourage-second-guessing"]
speaker: "Alex Chan"
speakers: ["Alex Chan"]
sources: ["adoption"]
sourceVaultSlug: "hbr-seg-adoption"
originDay: 9
articleStem: "hbr-edu-37-employees-not-questioning-ai"
sourceUrl: "https://hbr.org/2026/06/employees-arent-questioning-ai-advice-enough"
sourceTitle: "Employees Aren’t Questioning AI Advice Enough"
---
# The biggest risk of AI is training people to stop asking why

> "The biggest risk of AI isn't just bad answers or lack of adoption. It's training people to stop asking why."
> — [[entity-alex-chan|Alex Chan]]

[[entity-alex-chan|Chan]] warns of the long-term cognitive degradation caused by over-reliance on AI. The ultimate danger is not just inaccurate outputs, but the **erosion of human critical judgment and curiosity**. This motivates [[action-encourage-second-guessing]] and the third prong of the [[framework-responsible-xai-deployment]] — valuing human judgment to preserve the [[concept-algorithmic-override]] muscle.

**Enrichment note:** This connects to the automation-bias literature — humans tend to over-trust automated systems and under-challenge recommendations. Chan's dual pattern (over-rely on predictions *and* under-consume explanations) compounds that risk, which is why the remedy is behavioral and organizational, not merely technical.