Table of Contents
Which responsible AI principle requires explainable loan decisions?
Prepare for your AI-900 exam by learning which Microsoft guiding principle for responsible AI demands that decisions, like loan approvals, must be explainable. Understand the role of transparency and how it differs from fairness, inclusiveness, and privacy in building trustworthy AI systems.
Question
When an AI system that assess whether loans should be approved, the factors used to make the decision should be explainable. This is an example of which Microsoft guiding principle for responsible AI?
A. Privacy and security
B. Inclusiveness
C. Fairness
D. Transparency
Answer
D. Transparency
Explanation
The guiding principle demonstrated in this scenario is D. Transparency. The requirement that the factors used in a decision should be explainable is the very definition of transparency in a responsible AI framework.
The Principle of Transparency
Transparency, also known as explainability or interpretability, is a core pillar of Microsoft’s responsible AI principles. It asserts that AI systems should be understandable. For a high-stakes decision like a loan approval, it is not enough for the system to simply provide a “yes” or “no” answer. Stakeholders, including loan officers, auditors, and even the customers themselves, must be able to understand why the AI reached its conclusion. This involves being able to inspect the factors and data points that most influenced the model’s decision, which builds trust and enables accountability.
Why Other Principles Are Less Relevant to this Specific Requirement
- Privacy and Security: This principle is about protecting the applicant’s personal and financial data from unauthorized access. It does not address the need to explain the decision-making process itself.
- Inclusiveness: This principle focuses on designing AI systems that empower everyone and engage people from diverse backgrounds. While related to fairness, it is broader and does not specifically refer to the explainability of a decision.
- Fairness: This principle addresses whether the AI system treats different groups of people equitably, without bias. While transparency is a critical tool for auditing a system for fairness (i.e., you can’t know if it’s fair without seeing why it makes decisions), the act of making the decision process explainable is in itself an application of the transparency principle.
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