Transparency and explainability: Which responsible-AI principle does this configuration most directly serve?
Disclosing AI use and giving understandable reasons for outputs is transparency and explainability.
The question
A customer-service chatbot is configured to clearly tell users they are interacting with an AI and to explain, in plain language, the main reasons behind each recommendation it makes. Which responsible-AI principle does this configuration most directly serve?
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- Privacy and security, because informing users and explaining recommendations is chiefly a way to protect their personal data from unauthorized access or misuse.Plausible but wrong: disclosure and explanation are not primarily data-protection controls.
- Safety and reliability, because telling users they are using AI is mainly intended to ensure the system performs consistently and avoids technical failures.Plausible but wrong: disclosure does not address consistent, failure-resistant performance.
- Accountability, because explaining recommendations to users is fundamentally about identifying who is answerable when the AI system produces an incorrect result.Plausible but wrong: accountability concerns answerability, not user-facing disclosure and explanation.
- Transparency and explainability, because disclosing the AI's nature and giving understandable reasons for outputs is precisely what this principle requires. ✓Correct: disclosure of AI use plus understandable reasoning is the definition of transparency and explainability.
The trap
Confusing explainability toward users with accountability for who is answerable. How to remember it
Disclosing AI use and giving understandable reasons for outputs is transparency and explainability.
How many of these would you get right?
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Every answer, right and wrong, comes with its own explanation.
