---
id: "action-transparent-tradeoffs"
type: "action-item"
source_timestamps: ["§ Be Transparent and Honest"]
tags: ["ai-ethics", "consumer-trust"]
related: ["claim-magic-marketing-backfire", "framework-literacy-tailored-ai-strategy"]
action: "Explicitly educate consumers about AI biases, fallibility, and tradeoffs, especially in high-stakes domains."
outcome: "Sustainable, responsible AI usage that prevents misplaced trust, ethical lapses, and long-term brand damage."
speakers: ["Chiara Longoni", "Gil Appel", "Stephanie M. Tully"]
sources: ["adoption"]
sourceVaultSlug: "hbr-seg-adoption"
originDay: 9
articleStem: "hbr-edu-39-understanding-ai-not-embrace"
sourceUrl: "https://hbr.org/2025/07/why-understanding-ai-doesnt-necessarily-lead-people-to-embrace-it"
sourceTitle: "Why Understanding AI Doesn’t Necessarily Lead People to Embrace It"
---
# Disclose AI Tradeoffs Transparently

**Action:** Explicitly educate consumers about AI biases, fallibility, and tradeoffs, especially in high-stakes domains.

**Detail:** Do not use the [[concept-ai-magic-effect]] as an excuse to keep consumers uninformed. In high-stakes domains — **hiring, healthcare, education** — explicitly inform users of tradeoffs, potential biases in training data, and the fallibility of automated systems. Overreliance on intuitive impressions leads to ethical lapses and eventual loss of trust (see [[claim-magic-marketing-backfire]]). This is Step 5 of the [[framework-literacy-tailored-ai-strategy]] — the non-negotiable guardrail that overrides awe-preservation whenever stakes are high.

**Outcome:** Sustainable, responsible AI usage that prevents misplaced trust, ethical lapses, and long-term brand damage.

> **Enrichment:** This aligns with responsible-AI frameworks (NIST AI Risk Management Framework, EU AI Act, OECD) that treat transparency, explainability, and informed consent as prerequisites in sensitive settings — where "magic" framing should be secondary or avoided entirely.