Questions and Answers
What is the primary goal of achieving fairness in AI systems?
To make the same recommendations to everyone with similar characteristics
What is the primary function of the error analysis component in the Responsible AI dashboard?
To detect discrepancies in the model's performance across specific demographic groups
What is the primary benefit of transparency in AI systems?
To enable stakeholders to comprehend how and why AI systems function
What is the primary function of the model interpretability component in the Responsible AI dashboard?
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What is the primary purpose of the Responsible AI scorecard in Azure Machine Learning?
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Study Notes
Responsible AI
- Responsible AI is an approach to developing, assessing, and deploying AI systems in a safe, trustworthy, and ethical way.
- It involves keeping people and their goals at the center of system design decisions and respecting enduring values like fairness, reliability, and transparency.
Microsoft's Responsible AI Standard
- The standard is a framework for building AI systems according to six principles: fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability.
- These principles are the cornerstone of a responsible and trustworthy approach to AI.
Six Principles of Responsible AI
- Fairness and Inclusiveness: AI systems should treat everyone fairly and avoid affecting similarly situated groups of people in different ways.
- Reliability and Safety: AI systems should operate reliably, safely, and consistently, and resist harmful manipulation.
- Transparency: AI systems should provide transparent decision-making processes, and people should understand how decisions were made.
- Privacy and Security: AI systems should protect people's privacy and ensure the security of their data.
- Inclusiveness: AI systems should be inclusive and avoid biased outcomes.
- Accountability: AI systems should be accountable for their decisions and actions.
Azure Machine Learning Support for Responsible AI
- Fairness Assessment: Enables data scientists and developers to assess model fairness across sensitive groups.
- Error Analysis: Enables data scientists and developers to analyze model errors and identify discrepancies.
- Model Interpretability: Enables data scientists and developers to generate human-understandable descriptions of model predictions.
- Counterfactual What-If: Enables understanding and debugging of machine learning models.
- Responsible AI Scorecard: A customizable scorecard for evaluating AI systems based on the six principles.
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