Azure Responsible AI Fundamentals Explained

Azure Responsible AI is built on the principle of fairness, which means that AI models should not discriminate against certain groups of people.

Fairness is achieved by ensuring that AI models are transparent, explainable, and accountable.

Azure Responsible AI uses a fairness framework that includes three key principles: fairness, accountability, and transparency.

This framework helps developers create AI models that are fair, transparent, and accountable, which is crucial for building trust in AI systems.

Azure Responsible AI also emphasizes the importance of data quality, which is essential for developing accurate and reliable AI models.

Fairness is a key principle of responsible AI, ensuring that AI systems treat everyone equally and provide the same recommendations to all individuals.

To achieve fairness, AI systems should be designed to prevent discrimination based on personal characteristics, such as age, gender, or ethnicity.

The Cloud Adoption Framework for Azure integrates responsible AI principles, including fairness, into its guidance and recommendations.

Azure AI services provide transparency notes, which include services within the Azure AI services suite, to help users understand how AI systems make decisions.

The Responsible AI dashboard for Azure Machine Learning assesses AI systems and provides a single interface to implement responsible AI principles.

Data analysis is a crucial feature of the Responsible AI dashboard, allowing users to understand and explore dataset distributions and statistics.

Model interpretability is another important feature, enabling users to understand their model's predictions and how their model makes individual and overall predictions.

The Responsible AI dashboard also includes error analysis, counterfactual what-if analysis, and causal analysis, which help users understand and improve their AI systems.

Here are the six key principles of responsible AI:

You can customize the Responsible AI dashboard to fit your specific needs, allowing you to design tailored, end-to-end model debugging and decision-making workflows.

One way to do this is by using the dashboard's components in different combinations, such as starting with a model overview and then drilling down into error analysis or fairness assessment.

For example, you can use the following flow to identify model errors and diagnose them by understanding the underlying data distribution: Model overview > error analysis > data analysis.

Here are some specific use cases for different flows:

By customizing the Responsible AI dashboard, you can gain a deeper understanding of your AI models and make more informed decisions about their deployment and use.

Developing AI systems requires careful consideration to ensure they are effective, trustworthy, and inclusive. The HAX Toolkit is a great resource to use early in your design process to conceptualize what your AI system does and how it behaves.

You can use the Conversational AI guidelines to design bots that earn the trust of others. This is especially important for user-facing AI products.

The Inclusive AI design guidelines are also essential to help you design AI that is accessible to everyone. This is crucial for creating a positive user experience.

To ensure your AI system is fair and unbiased, use the AI fairness checklist. This will help you determine whether your system is meeting the necessary standards.

You can find more resources on responsible AI in Machine Learning, which is essential for building AI systems that are trustworthy and effective.

Customizing the Responsible AI dashboard is a powerful tool for tailoring your model debugging and decision-making workflows to your specific needs. The dashboard's components can be put together in various ways to analyze scenarios in diverse ways.

You can start with a model overview to get a general understanding of your AI system. From there, you can dive into error analysis to identify potential issues. For instance, you can analyze the data distribution to diagnose model errors. This is a key takeaway from the article, which highlights the importance of understanding the underlying data distribution.

Another way to customize the dashboard is to focus on fairness assessment. This involves identifying potential biases in your AI system and diagnosing them by understanding the underlying data distribution. This approach can help you ensure that your AI system is fair and unbiased.

You can also use the dashboard to diagnose errors in individual instances with counterfactual analysis. This involves understanding the minimum change required to lead to a different model prediction. This approach can help you pinpoint specific issues with your AI system.

In addition to these approaches, you can use the dashboard to understand the root cause of errors and fairness issues introduced via data imbalances or lack of representation of a particular data cohort. This involves analyzing the data distribution to identify potential issues.

Here are some example workflows that you can use to customize the Responsible AI dashboard:

By using these workflows, you can customize the Responsible AI dashboard to suit your specific needs and improve the performance and fairness of your AI system.

Having an integrated compute resource is a crucial part of getting the most out of your Responsible AI dashboard.

Some features require dynamic computation, so you might find some functionality missing without a connected compute resource.

Connecting a compute resource enables full functionality for key components like error analysis, feature importance, counterfactual what-if, and causal analysis.

These components are essential for getting a deep understanding of your AI model's performance and making informed decisions.

Connecting a compute resource is a straightforward process that can be completed in just a few steps.

Here are the components that become fully functional with a connected compute resource:

To enable full functionality of the Responsible AI dashboard, you need to connect a compute resource to the dashboard. This is because some features, such as error analysis, feature importance, and counterfactual what-if, require dynamic computation.

You can connect a compute resource by selecting a running compute instance in the Compute dropdown list at the top of the dashboard. If you don't have a running compute, create a new compute instance by selecting the plus sign (+) next to the dropdown.

To select a running compute instance, follow these steps:

If the process takes a while and your Responsible AI dashboard is still not connected to the compute instance, or a red error message bar is displayed, it means there are issues with starting your Responsible AI endpoint.

Individual Feature Importances are a powerful tool for understanding how your model is making predictions. You can use them to see which features are most important for each data point.

To access Individual Feature Importances, you can choose up to five data points to compare feature importances for. The Point selection table allows you to view your data points and select up to five points to display in the feature importance plot or the ICE plot.

The Feature importance plot is a bar plot of the importance of each feature for the model's prediction on the selected data points. You can specify the number of features to show importances for by using a slider, and sort the bar plot by the absolute values to see the most impactful features.

Here are the key features of the Feature importance plot:

You can also switch to the Individual Conditional Expectation (ICE) plot, which shows model predictions across a range of values of a particular feature. To do this, you can specify the feature to make predictions for and the range of values to show. For numerical features, you can specify the lower and upper bounds of the range and the number of points to show. For categorical features, you can specify which feature values to show predictions for.

Microsoft Azure Responsible AI is committed to ensuring that AI systems are used responsibly. They provide a list of transparency notes for AI-relevant Azure services, including services within the Azure AI services suite.

The company sets rules for enacting responsible AI and clearly defines roles and responsibilities for teams involved. They also foster readiness to adopt responsible AI practices within their company and with their customers and partners. Review of sensitive use cases helps ensure their responsible AI principles are upheld in their work.

Microsoft's internal AI and ethics committee, Aether, performs research and provides recommendations on responsible AI issues. Aether members include experts in responsible AI and engineering, as well as representatives from major divisions within Microsoft. They organize working groups focused on issues, analysis, and development of the six responsible AI principles.

To ensure accountability, Microsoft uses machine learning operations (MLOps) based on DevOps principles and practices that increase the efficiency of AI workflows. Azure Machine Learning provides MLOps capabilities for better accountability of AI systems, including registering, packaging, and deploying models, capturing governance data, and notifying and alerting on events in the machine learning lifecycle.

Here are some key stakeholders who can use the Responsible AI dashboard and scorecard to build trust with AI systems:

Fairness and inclusiveness are crucial aspects of AI development, and Microsoft Azure Responsible AI takes these concerns seriously.

Microsoft provides a list of transparency notes for AI-relevant Azure services, which includes services within the Azure AI services suite.

AI systems should treat everyone fairly and avoid affecting similarly situated groups of people in different ways. For example, when AI systems provide guidance on medical treatment, loan applications, or employment, they should make the same recommendations to everyone who has similar symptoms, financial circumstances, or professional qualifications.

The fairness assessment component of the Responsible AI dashboard enables data scientists and developers to assess model fairness across sensitive groups defined in terms of gender, ethnicity, age, and other characteristics.

Here's a breakdown of how to assess model fairness using Azure Machine Learning:

By prioritizing fairness and inclusiveness, we can create AI systems that benefit everyone, regardless of their background or circumstances.

Transparency is a critical aspect of responsible AI. It's essential to understand how AI systems make decisions that impact people's lives.

A crucial part of transparency is interpretability, which provides a useful explanation of the behavior of AI systems and their components. This helps stakeholders identify potential performance issues, fairness issues, exclusionary practices, or unintended outcomes.

The model interpretability component of the Responsible AI dashboard in Azure Machine Learning provides multiple views into a model's behavior. This includes global explanations, local explanations, and model explanations for a selected cohort of data points.

For example, a bank might use an AI system to decide whether a person is creditworthy. In this case, the model interpretability component can provide explanations such as: what features affect the overall behavior of a loan allocation model? or why was a customer's loan application approved or rejected?

The counterfactual what-if component of the Responsible AI dashboard enables understanding and debugging a machine learning model in terms of how it reacts to feature changes and perturbations.

Here are some examples of how transparency can be applied in Azure Machine Learning:

By providing transparency, stakeholders can build trust in AI systems and make more informed decisions. This is essential in heavily regulated spaces where AI systems are used to make critical decisions.

As AI becomes more prevalent, protecting privacy and security is crucial. With AI, access to data is essential for making accurate predictions and decisions about people, making data security a top priority.

Privacy laws require transparency about the collection, use, and storage of data. This means consumers have the right to know how their data is being used.

Azure Machine Learning enables administrators and developers to create a secure configuration that complies with their companies' policies. This includes restricting access to resources and operations by user account or group.

With Azure Machine Learning, users can also restrict incoming and outgoing network communications. This helps prevent unauthorized access to sensitive data.

Encrypting data in transit and at rest is also a key feature of Azure Machine Learning. This ensures that even if data is intercepted or accessed without permission, it will remain secure.

Azure Machine Learning also allows users to scan for vulnerabilities and apply and audit configuration policies. This helps identify and fix potential security issues before they become major problems.

Microsoft has created two open-source packages to further implement privacy and security principles: SmartNoise and Counterfit. SmartNoise helps keep individual data safe and private, while Counterfit simulates cyberattacks against AI systems to assess their security.

Here are some key features of Azure Machine Learning's security features:

Microsoft sets clear rules for enacting responsible AI and defines roles and responsibilities for teams involved.

Their internal AI and ethics committee, Aether, performs research and provides recommendations on responsible AI issues. Aether members include experts in responsible AI and engineering, as well as representatives from major divisions within Microsoft.

Engineering teams at Microsoft define and operationalize a tooling and system strategy for using AI responsibly. Compliance tooling is implemented to help monitor and enforce responsible AI rules and requirements.

Aether provides research-based recommendations, which are often codified into official Microsoft policies and practices. Aether organizes working groups focused on issues, analysis, and development of the six responsible AI principles.

Here are the six responsible AI principles that Aether focuses on: