---
id: "framework-responsible-xai-deployment"
type: "framework"
source_timestamps: ["§ How to Use Explainable AI Responsibly"]
tags: ["organizational-design", "ai-governance"]
related: ["concept-checkbox-transparency", "action-align-incentives-critical-engagement", "action-encourage-second-guessing"]
steps: ["\\\"Build oversight into AI's decision-making processes — ensure businesses actually use mandated explanations to shape decisions", "not just provide them to users.\\\"", "\\\"Create incentives for employees to engage critically with AI — move beyond checkbox transparency via training and by aligning compensation/incentives so employees are rewarded for reviewing", "documenting", "and reflecting on AI explanations.\\\"", "Value human judgment — actively encourage employees to second-guess AI recommendations to prevent the devaluation of human critical thinking and stop the habit of blindly accepting answers."]
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"
---
# Responsible XAI Deployment Framework

[[entity-alex-chan|Alex Chan]] proposes a **three-pronged approach** for business leaders to ensure that [[concept-explainable-ai|Explainable AI]] is used responsibly, moving beyond mere regulatory compliance to actual operational effectiveness. It is the constructive answer to [[concept-checkbox-transparency]] and the practical response to [[claim-transparency-mandates-insufficient]].

**1. Build oversight into AI's decision-making processes.**
Ensure businesses are actually *using* mandated explanations to shape decisions, not just providing them to users. Oversight is structural, not optional.

**2. Create incentives for employees to engage critically with AI.**
Move beyond checkbox transparency by providing training and aligning compensation/incentives so employees are rewarded for reviewing, documenting, and reflecting on AI explanations. Operationalized as [[action-align-incentives-critical-engagement]]; the open design problem is [[question-optimal-incentive-structures]].

**3. Value human judgment.**
Actively encourage employees to second-guess AI recommendations to prevent the devaluation of human critical thinking and stop the habit of blindly accepting answers. Operationalized as [[action-encourage-second-guessing]]; guards against the long-term risk in [[quote-stop-asking-why]].

**Enrichment note:** The specific three-step formulation is a reasonable *synthesis* of Chan's recommendations and the broader responsible-AI governance literature — it is not stated verbatim in the cited articles but is consistent with their guidance (architect the decision environment and incentive structures so transparency is used rather than ignored). A nuance from the counter-perspectives: consider **graduated obligations** — enforced explanation review/documentation for high-stakes decisions (credit, hiring, medical) but lighter-touch, optional explanations for routine ones to avoid alert fatigue (see [[question-ui-ux-for-forced-engagement]]). Model-side constraints and dedicated audit teams are complementary governance levers that reduce reliance on universal front-line engagement.