Dive into the realm of responsible AI with Microsoft’s transparency principle. Explore a vivid illustration — understanding the dynamics and significance of explaining credit loan application outcomes. Uncover the power of ethical AI practices that redefine transparency in decision-making processes.
Providing an explanation of the outcome of a credit loan application is an example of the Microsoft transparency principle for responsible AI.
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. Yes.
Providing an explanation of the outcome of a credit loan application is an example of the Microsoft transparency principle for responsible AI. Transparency is one of the six key principles that Microsoft outlines for creating responsible and trustworthy AI systems. According to Microsoft, transparency means that people who create AI systems should be open about how and why they are using AI, and open about the limitations of the system. Additionally, everyone must understand the behavior of AI systems.
In the context of a credit loan application, transparency means that the AI system that evaluates the application should provide a clear and understandable explanation of how it reached its decision, and what factors influenced the outcome. For example, the AI system could explain that the applicant was denied a loan because of their low credit score, high debt-to-income ratio, or lack of collateral. This way, the applicant can understand the rationale behind the decision, and potentially take steps to improve their situation or appeal the decision if they believe it was unfair or inaccurate.
Transparency is important for responsible AI because it helps to build trust and confidence in AI systems, and to ensure that they are aligned with human values and expectations. Transparency also enables accountability, which is another principle of responsible AI that means that people who create and use AI systems should be responsible for how they operate and the outcomes they produce. By providing explanations of the AI system’s decisions, the creators and users of the system can be held accountable for its performance and impact, and address any issues or errors that may arise.
Microsoft provides various tools and resources to help developers and data scientists implement transparency in their AI systems. For example, Azure Machine Learning includes a Responsible AI dashboard that enables data scientists and developers to generate human-understandable descriptions of the predictions of a model. The dashboard includes model interpretability and counterfactual what-if components that can help to explain the model’s behavior and explore alternative scenarios. Microsoft also offers a Human-AI Experience (HAX) Workbook that helps organizations define and implement best practices for human-AI interaction. The workbook covers topics such as designing for transparency, providing feedback, and establishing trust.
Microsoft Docs > Azure > Cloud Adoption Framework > Adopt > Innovate > Responsible and trusted AI
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