---
id: "question-optimal-incentive-structures"
type: "open-question"
source_timestamps: ["§ How to Use Explainable AI Responsibly"]
tags: ["incentive-design", "future-research"]
related: ["action-align-incentives-critical-engagement", "claim-financial-incentives-dampen-transparency"]
resolutionPath: "Empirical studies testing different compensation models (e.g., paying for documented override justifications vs. flat outcome bonuses) to find the balance between throughput and critical AI engagement."
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"
---
# What are the optimal incentive structures for XAI engagement?

**Open question.** The research proves that tying bonuses to pure outcomes (like loan repayment) suppresses the desire for transparency ([[claim-financial-incentives-dampen-transparency]]). However, the source does **not detail exactly *how*** to structure compensation to reward critical engagement ([[action-align-incentives-critical-engagement]]) without severely impacting operational efficiency or throughput.

**Resolution path:** Empirical studies testing different compensation models — e.g., paying for *documented override justifications* vs. flat *outcome bonuses* — to find the balance between throughput and critical AI engagement.

**Related tension (enrichment):** Counter-perspectives warn against blanket "forced engagement" on autonomy and efficiency grounds, suggesting **graduated obligations** keyed to decision stakes — a design variable any incentive experiment should hold or vary deliberately.