How to Build Fair and Transparent AI with Azure Copilot

Discover how Azure Copilot empowers teams to build ethical, transparent and scalable AI solutions — bringing responsible AI principles to life across real-world workloads and use cases.

In brief:

• Azure Copilot integrates with fairness and security tools to help teams build, manage, and monitor AI systems ethically and transparently.
• The platform automatically generates compliance and fairness prompts, detecting issues like hardcoded credentials or biased training datasets before they become problems.
• Azure Copilot supports Microsoft’s six responsible AI principles: fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability.
• Teams can build real-world AI workloads with responsible AI practices built in from the start.
• Natural language queries make it easy for developers and analysts to gain insights into model performance and fairness metrics without deep technical expertise.

Why Responsible AI Matters

Responsible artificial intelligence (AI) is the future of AI.

To fulfil its potential of unlocking business value, AI must be reliable, safe, secure and unbiased. It also must exhibit transparency into how it makes decisions and accountability for all the humans involved in its deployment and operation.

That may sound like an impossible goal, but it doesn’t have to be. Thanks to modern technologies, responsible AI is becoming operational instead of theoretical.

However, even companies that have done the necessary AI governance work and implemented policies to ensure reliable, safe, secure and unbiased AI continue to struggle with implementing responsible AI.

Plus, as the authors of the Brookings Institute paper “Ethical AI Development: Evidence From AI Startups” note: “A set of ethical AI principles in and of itself is not important unless firms adhere to those principles.”

In this blog post, we’ll share how one modern technology, Azure Copilot, helps enforce your principles and accelerate the responsible AI movement. Azure Copilot integrates with out-of-fairness and security tools such as Fairlearn and InterpretML, which then help teams build, manage and monitor AI systems ethically and transparently.

The result is responsible AI brought to life across workloads and everyday use cases. This blog shows how Azure Copilot and its integrations make it easy to build powerful, trustworthy AI solutions so your teams can move confidently from prototypes to scalable, real-world AI deployments.

What Is Azure Copilot?

Copilot is Microsoft’s AI tool. When added to your Microsoft 365 Business or Enterprise license, it becomes your virtual assistant across all the apps you and your employees use every day.

Azure Copilot is the same tireless digital helper for your Azure Cloud experience. Just as Word Copilot can help you draft documents or Excel Copilot can suggest formulas using AI, Azure Copilot can help you perform your daily Azure functions, but it also enables responsible AI.

How to Use Azure Copilot to Go Beyond Compliance

When many people first hear about the principles behind responsible AI, their thoughts instinctively go to compliance. With IBM finding that 97 percent of companies surveyed that had experienced security incidents “lacked proper AI access controls,” compliance is essential.

And with fines in the U.S. for biased AI having ranged from the hundreds of thousands to $2.5 million for a student loan vendor, noncompliance has both security- and fairness-related impacts.

Still, responsible AI isn’t only about checking boxes and avoiding fines. It’s about making AI work effectively.

After all, if employees fear using AI because they’re worried about putting company data at risk, your AI solutions will fail — or they may take on their own AI projects, leading to shadow IT within your organization.

If customers feel that your AI has left them and their concerns out, they’ll turn to other companies with more inclusive models.

And if no one at your company is accountable for AI, the model can drift and become misaligned with company objectives.

In other words, operating AI irresponsibly can have lasting consequences for your customers, finances and brand.

Adding to the challenge: Responsible IT involves both humans and technology. MSNBC notes that “incomplete or unrepresentative datasets could limit AI’s objectivity, while biases in development teams that train such systems could perpetuate that cycle of bias.”

That’s why Azure Copilot addresses responsible IT with technology that alerts humans to otherwise unseen biases or security risks to support Microsoft’s six responsible AI principles:

1. Fairness: Microsoft Fairlearn assesses model fairness for various demographic groups.
2. Reliability and Safety: Azure’s Responsible AI dashboard offers tools like error analysis to identify where a model underperforms.
3. Privacy and Security: Azure uses data encryption and access controls to ensure that your AI meets privacy regulations, such as GDPR.
4. Inclusive: Azure’s accessibility tools, support for nearly 50 languages, and use of diverse datasets help ensure that your AI solutions represent as many needs and perspectives as possible.
5. Transparency: Model interpretability integrations, such as InterpretML, explain how models work, the data they use, and any limitations they may have.
6. Accountability: Azure empowers your company to track AI projects with version control, logging and governance tools.

Copilot for AI also allows users to use natural language queries to easily gain insight into model performance, fairness metrics and more, helping them build smarter, more ethical AI from the ground up. The result is more reliable AI solutions that boost stakeholder confidence, are easier to adopt, include everyone, and align with global AI performance frameworks.

Enhancing Responsibility With AI-Generated Prompts

Microsoft’s six responsible AI principles can sometimes be hard to see. Developers have thousands of lines of code to review, and biases can sneak in regardless of our best intentions. To help, Azure Copilot can automatically generate user compliance and fairness prompts.

For example, imagine that a developer writes an application using Python, including this snippet:

api_key = “12345-ABCDE”

Copilot can detect that the code contains a sensitive value, such as an application programming interface (API) key or password, and prompt the developer: “This code snippet includes hardcoded credentials. Storing passwords directly in code can pose a security risk. Would you like help moving them to a secure location like Azure Key Vault?”

In addition, GitHub Copilot for Azure — one benefit of Microsoft’s 2018 acquisition of GitHub — helps developers write secure code by flagging risky patterns, suggesting best practices, and integrating with Azure tools like Key Vault and Defender for Cloud.

Or, on the ethical side, consider a data analyst using Azure Machine Learning to train a customer service chatbot. However, most users in the dataset are aged 18–35, and few are over age 60. As Centric Consulting’s Director of IT Strategy Joseph Ours notes, “AI tools might inadvertently perpetuate or even amplify existing biases in data, leading to unfair treatment or discrimination against certain groups or individuals.”

Azure Copilot can detect such biases and prompt the analyst: “Your training dataset shows significantly fewer examples for older adults compared to younger users. This could lead to biased predictions. Would you like help balancing the data or reviewing fairness metrics?”

As you can see, Copilot writes these prompts in natural language, just as developers and others use natural language queries for Azure Copilot’s various responsible AI components. Combined, these capabilities open the door not only to responsible AI but also to responsible innovation.

To illustrate, let’s consider a typical use case.

Use Case: AI-Powered Code Review Assistant for .NET and Java Teams

Your development team is facing a challenge. They need to write a large amount of code in .NET or Java, but reviewing the code for bugs, code smells that may indicate deeper problems, or inconsistent style requires too much time and subjectivity.

To solve the problem, the team decides to build an intelligent code review assistant using Azure AI services. They need the code to help developers identify common issues more quickly while maintaining fair and transparent reviews. The team creates an approach that incorporates Azure Copilot Responsible AI tools:

1. Build Code Analysis Model

Using Azure Machine Learning, the team trains a model that analyzes code snippets or pull requests, predicting issues like bugs, style inconsistencies, or security vulnerabilities.

2. Include Explainable Insights

Employing Azure Responsible AI tools — such as the Responsible AI dashboard and InterpretML — the team surfaces exactly why the model flagged specific lines of code, such as those containing risky API calls, missed null checks, or inconsistent naming. The transparency gained helps developers understand and trust model suggestions.

3. Conduct Fairness Checks

To ensure the assistant treats all developers fairly — whether senior, junior, backend, or frontend — the team uses fairness dashboards through Fairlearn integration to detect unfairly flagged patterns.

4. Seamlessly Integrate the Workflow

Deploy the model as a REST API endpoint. Integrate with continuous integration and continuous delivery (CI/CD) pipelines (Azure Pipelines, Jenkins) or pull request (PR) systems (Copilot GitHub, Azure Repos). The Azure Copilot assistant helps prepare code that Azure DevOps and GitHub Actions can use to annotate PRs with inline suggestions while allowing developers to accept or reject them — keeping humans fully in control.

As a result of the team’s efforts, the developers receive immediate, AI-generated feedback on code quality, while model decisions are open and explainable, boosting confidence. In addition, the fairness checks avoid biased recommendations and unbalanced workloads, while easy-to-use, plug-and-play REST APIs allow for easy integration for the .NET and Java teams.

The team is now more confident in its AI solution for analyzing large quantities of code more smoothly, more consistently, and more fairly.

Using Responsible AI to Build Common AI Workloads

With Azure Copilot’s responsible AI features in place, your organization will be better prepared to build real-world AI workloads using Azure’s many AI building blocks, such as:

• Computer vision teaches systems to “see” — for instance, detecting objects, recognizing faces, reading text from images (OCR), and tagging images.
• Natural language processing (NLP) lets systems understand or generate human language — think sentiment analysis, entity extraction, speech-to-text, translation and text summarization.
• Document intelligence/document processing automates the extraction of structured data like fields, tables, or key values from forms — great for invoices, contracts, receipts and more.
• Knowledge mining converts unstructured data (documents, emails, media) into searchable knowledge, helpful for trend spotting, sentiment analysis, or smart insights.
• Generative AI produces creative outputs such as text, images, or code using large language models (LLMs), such as ChatGPT-style text generation, image synthesis and code suggestions.

With responsible AI in place, there is virtually no limit to where Azure’s AI tools can take you.

Conclusion

Using Azure’s AI workloads and responsible AI toolbox, you can create intelligent, ethical, and scalable solutions — from generating insights and processing documents to building chatbots and recommendation systems. The best part? Your focus remains on building great solutions — with people’s trust baked in.

Need guidance on implementing responsible AI with Azure Copilot? Centric Consulting is a Microsoft Cloud Partner, and our Microsoft consulting services team can help you make the most of your Microsoft services. Contact our team today. Let’s talk