Responsible AI Principle: Data Source Disclosure
Short answer
D Stating the source of training data exemplifies the transparency principle by providing visibility into the model's origins and operational context.
Question
Key Takeaways
The core trap is confusing transparency (openness about data/process) with fairness (bias mitigation) or privacy (data protection). Disclosing origins is fundamentally about openness.
This question tests knowledge of Microsoft's Responsible AI principles, specifically identifying how disclosing training data sources relates to transparency.
Common mistake: Option C (Fairness) is often chosen because data sources can influence bias. However, fairness addresses the *outcome* of bias, while transparency addresses the *disclosure* of the inputs themselves.
Expert Analysis
The question asks to identify the responsible AI principle associated with stating the source of training data. Microsoft's Responsible AI principles include Transparency, Fairness, Reliability & Safety, Privacy & Security, Inclusiveness, and Accountability.
Transparency is defined as ensuring that people understand when they are interacting with AI, what capabilities the AI has, and how it works. This includes disclosing the sources of data used to train models so users can assess potential biases or limitations. By stating the source of data, an organization provides visibility into the model's origins, which directly supports transparency.
Privacy and security focus on protecting user data and preventing unauthorized access. Fairness focuses on avoiding bias and ensuring equitable outcomes. Reliability and safety ensure the system performs consistently under expected conditions without causing harm. None of these primarily address the act of disclosing data provenance.
As noted in community comments, "Stating the source of the data used to train a model is an example of the transparency principle." This aligns with official documentation emphasizing that transparency involves being open about how AI systems operate, including their data sources, to build trust and allow for informed decision-making by stakeholders.
Exam Strategy
When encountering questions about 'Responsible AI Principles' in AI-901, map specific actions to their definitions rather than guessing based on general ethics. For instance, if the action involves 'disclosing', 'explaining', or 'showing how', it likely points to Transparency or Explainability. If it involves 'protecting data' or 'consent', look for Privacy. Memorize the key verbs associated with each principle to quickly eliminate distractors.
Frequently Asked Questions
What are Microsoft's Responsible AI principles?
How does transparency differ from accountability?
Community Votes
100% of anonymous learners picked answer D. Votes are pick records left by other test-takers — they are not the verified answer.
Discussion 1 contributions
Compiled from public community threads and reader submissions, lightly edited for clarity. Handles and vote tallies are shown as recorded, and comments reflect the views of individual contributors.
From public community threads 1
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john5surreal 👍 1 Answered DStating the source of the data used to train a model is an example of the transparency principle
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