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AI-900: Decoding Responsible AI: Microsoft’s Guiding Principle for Transparent Loan Assessments

Uncover the essence of responsible AI as we explore Microsoft’s guiding principles. Delve into the world of transparent decision-making in loan assessments, a hallmark of ethical AI design. Gain insights into the pivotal principle shaping fairness and accountability in the era of artificial intelligence.

Question

When you design an AI system to 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. transparency
B. inclusiveness
C. fairness
D. privacy and security

Answer

A. transparency

Explanation

Achieving transparency helps the team to understand the data and algorithms used to train the model, what transformation logic was applied to the data, the final model generated, and its associated assets. This information offers insights about how the model was created, which allows it to be reproduced in a transparent way.

The correct answer is A. transparency.

Transparency is one of the six Microsoft guiding principles for responsible AI. It means that people should be able to understand how an AI system works and how it makes decisions. Transparency also implies that the factors, data, and algorithms used to create an AI system should be documented and available for review.

In the scenario of designing an AI system to assess whether loans should be approved, transparency is important because the decision-making process should be clear and understandable to the applicants, the lenders, and the regulators. The factors used to make the decision should be explainable and justified, and the AI system should provide feedback and evidence for its outcomes. This way, the AI system can build trust and confidence among its stakeholders, and also avoid potential bias, discrimination, or unfairness.

Therefore, transparency is the most relevant principle for this scenario. The other principles are also important, but they are not directly related to the explainability of the factors used to make the decision. Inclusiveness means that the AI system should consider the needs and perspectives of diverse groups of people. Fairness means that the AI system should treat everyone equally and avoid harmful bias. Privacy and security means that the AI system should protect the data and information of its users and respect their choices. Accountability means that the AI system should have mechanisms to monitor, audit, and correct its performance and behavior.

Incorrect Answers:

B: Inclusiveness mandates that AI should consider all human races and experiences, and inclusive design practices can help developers to understand and address potential barriers that could unintentionally exclude people. Where possible, speech-to-text, text-to-speech, and visual recognition technology should be used to empower people with hearing, visual, and other impairments.

C: Fairness is a core ethical principle that all humans aim to understand and apply. This principle is even more important when AI systems are being developed. Key checks and balances need to make sure that the system’s decisions don’t discriminate or run a gender, race, sexual orientation, or religion bias toward a group or individual.

D: A data holder is obligated to protect the data in an AI system, and privacy and security are an integral part of this system. Personal needs to be secured, and it should be accessed in a way that doesn’t compromise an individual’s privacy.

Reference

Microsoft Azure AI Fundamentals AI-900 certification exam practice question and answer (Q&A) dump with detail explanation and reference available free, helpful to pass the Microsoft Azure AI Fundamentals AI-900 exam and earn Microsoft Azure AI Fundamentals AI-900 certification.

Microsoft Azure AI Fundamentals AI-900 certification exam practice question and answer (Q&A) dump

Alex Lim is a certified IT Technical Support Architect with over 15 years of experience in designing, implementing, and troubleshooting complex IT systems and networks. He has worked for leading IT companies, such as Microsoft, IBM, and Cisco, providing technical support and solutions to clients across various industries and sectors. Alex has a bachelor’s degree in computer science from the National University of Singapore and a master’s degree in information security from the Massachusetts Institute of Technology. He is also the author of several best-selling books on IT technical support, such as The IT Technical Support Handbook and Troubleshooting IT Systems and Networks. Alex lives in Bandar, Johore, Malaysia with his wife and two chilrdren. You can reach him at [email protected] or follow him on Website | Twitter | Facebook

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