Explanation:

According to the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module "Identify guiding principles for responsible AI," Fairness is one of Microsoft's six core principles of Responsible AI. The principle of fairness ensures that AI systems treat all individuals and groups equitably, and that the models do not produce biased or discriminatory outcomes.
Bias in AI systems can occur when training data reflects existing prejudices, inequalities, or imbalances. For example, if a dataset used for a hiring model underrepresents a certain demographic group, the AI system might produce unfair recommendations. Microsoft emphasizes that AI should not reflect or reinforce bias and that developers must actively design, test, and monitor models to mitigate unfairness.
Microsoft's Six Responsible AI Principles:
* Fairness - AI systems should treat everyone equally and avoid bias.
* Reliability and safety - AI systems must operate as intended even under unexpected conditions.
* Privacy and security - AI must protect personal and business data.
* Inclusiveness - AI should empower all people and be accessible to diverse users.
* Transparency - AI systems should be understandable and their decisions explainable.
* Accountability - Humans should be accountable for AI system outcomes.
The other options do not fit this context:
* Accountability ensures human responsibility for AI decisions.
* Inclusiveness focuses on accessibility and empowering all users.
* Transparency relates to making AI systems understandable.
Therefore, the correct answer is fairness, as it directly addresses the principle that AI systems should NOT reflect biases from the datasets used to train them.