Microsoft's Responsible AI Principles, as outlined in the AI-900 certification materials and official Microsoft documentation, emphasize six guiding principles: fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability. The principle of transparency means that AI systems should be designed so their decisions and processes are understandable and explainable to users and stakeholders.
In this scenario, the AI system is being developed to decide whether a loan should be approved. Such a decision directly affects people's lives and finances, so it is essential that the system can explain which factors influenced its decision-for example, credit score, income, or payment history. Microsoft's Responsible AI framework stresses that transparency helps ensure trust between humans and AI systems. When decisions are explainable, users can understand and contest the reasoning if necessary.
The other options do not align precisely with this scenario:
* B. Inclusiveness focuses on making AI accessible to all people, regardless of ability or background.
* C. Fairness ensures that AI systems treat all individuals equally and do not discriminate. While fairness is important for loan assessment, the question specifically highlights the need for explainability, not equality.
* D. Privacy and Security deals with safeguarding user data, which is separate from explaining decisions.
Therefore, the principle demonstrated here is transparency, as it ensures decision-making processes are clear, explainable, and traceable-directly aligning with Microsoft's responsible AI guidance.
Reference:Microsoft Learn: "Identify guiding principles for responsible AI" - explains that transparency ensures AI systems are understandable and their decision logic can be examined and explained.