Employees Aren’t Questioning AI Advice Enough

2026-06-24 · Source: Feeds - HBR.org · Field: Business & Management — Corporate Strategy & Leadership, Operations & Process Management, Human Resources & Workforce Development · Depth: Novice, medium

Summary

A study by Harvard Business School Assistant Professor Alex Chan, published in June 2026, reveals that employees often avoid questioning AI advice, even when transparency could expose biases. In an experiment with 2,512 participants acting as lending officers reviewing \$10,000 loan requests, 80% wanted AI-generated risk scores, but only 46% sought explanations for those predictions. Participants were nearly 20% more likely to decline explanations when their bonuses depended on loan repayment. Furthermore, when informed that explanations might reveal race or gender bias, avoidance rates rose by over 10 percentage points, reaching 23%. The research indicates that people actively avoid information that could complicate decisions or cause moral discomfort, challenging the assumption that users naturally desire more AI transparency. Viewing explanations, however, made participants 6 percentage points more likely to override AI recommendations.

Key takeaway

For business leaders implementing AI in high-stakes decisions, you must move beyond mere "checkbox transparency." Your organization needs to build robust oversight into AI processes and create explicit incentives for employees to critically engage with AI explanations. Otherwise, you risk fostering "willful blindness" and inadvertently devaluing human judgment, potentially leading to biased outcomes and regulatory non-compliance. Encourage your teams to actively question AI recommendations to ensure ethical and informed decision-making.

Key insights

People often avoid AI explanations, particularly when financial incentives or potential biases make decisions harder or morally uncomfortable.

Principles

• Transparency alone doesn't ensure AI explanation engagement.
• Incentives can drive "willful blindness" to AI reasoning.
• Over-reliance on AI risks devaluing human judgment.

In practice

• Integrate oversight into AI decision-making processes.
• Design incentives for critical AI engagement.
• Encourage employees to challenge AI recommendations.

Topics

• Explainable AI
• AI Bias
• Human-AI Interaction
• Organizational Incentives
• AI Governance
• Ethical Decision-Making

Best for: CTO, VP of Engineering/Data, Director of AI/ML, Consultant, Executive, Operations Professional

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Editorial summary, takeaway, and curation by AIssential. Original article published by Feeds - HBR.org.