AI-900: Achieving Transparent AI by Documenting and Explaining Models

Building responsible AI demands transparency. Learn how documentation, explainability, and representative data enable ethical AI systems that meet Microsoft’s principles.

Question

You are building an AI system. Which task should you include to ensure that the service meets the Microsoft transparency principle for responsible AI?

A. Ensure that all visuals have an associated text that can be read by a screen reader.
B. Enable autoscaling to ensure that a service scales based on demand.
C. Provide documentation to help developers debug code.
D. Ensure that a training dataset is representative of the population.

Answer

C. Provide documentation to help developers debug code.

Explanation

The correct answer is C. You should provide documentation to help developers debug code to ensure that the service meets the Microsoft transparency principle for responsible AI.

The transparency principle for responsible AI means that people who create and use AI systems should be open about how and why they are using AI, and open about the limitations of the system. This principle involves providing clear explanations and justifications for the decisions and actions of AI systems, and enabling users to understand and interact with AI systems in meaningful ways.

By providing documentation to help developers debug code, you are following the transparency principle. Documentation is a way of communicating the purpose, functionality, and design of your AI system to other developers and users. Documentation can help developers debug code by providing information about the data, models, algorithms, and parameters that underlie your AI system, and the expected inputs and outputs of your AI system. Documentation can also help developers identify and fix errors, bugs, or anomalies in your AI system, and improve its performance and quality.

The other options are not related to the transparency principle, but to other principles or aspects of responsible AI.

Ensuring that all visuals have an associated text that can be read by a screen reader is related to the inclusiveness principle, which means that AI systems should empower and engage communities around the world, and to do this, we partner with under-served minority communities to plan, test, and build AI systems. By providing text alternatives for visuals, you are making your AI system more accessible and respectful to users who have visual impairments or use assistive technologies.

Enabling autoscaling to ensure that a service scales based on demand is related to the reliability and safety principle, which means that AI systems should perform reliably and safely. By enabling autoscaling, you are making your AI system more robust and resilient to changes in the environment, such as fluctuations in the workload or traffic. Autoscaling can help your AI system handle increased demand without compromising its performance or quality.

Ensuring that a training dataset is representative of the population is related to the fairness principle, which means that AI systems should treat all people fairly. By ensuring that your training dataset is representative of the population, you are trying to avoid potential biases in the data that could lead to discrimination or harm for any individuals or groups. A representative dataset can help your AI system produce accurate and consistent outcomes for diverse users.

References

Microsoft Docs > Learn > Browse > Identify guiding principles for responsible AI

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