AI Governance: The Key to Successful Deployment

Imagine a world where AI detects fraud with pinpoint accuracy and operates transparently and ethically. Nexi’s collaboration with CROZ highlights the importance of strong MLOps practices and AI governance in setting up advanced machine learning models for fraud detection. Nexi, a leading online payment solutions provider, faces several challenges in implementing these models, including data quality, security, evolving fraud tactics, and model bias. Effective MLOps integration, skilled human expertise, and adherence to regulatory compliance are vital for maintaining trust and ensuring the models’ effectiveness.

Is MLOps enough? In the current landscape, it is an essential set of principles to follow, yet it needs to be complemented by AI governance. AI governance is fundamental for building trustworthy AI systems and ensuring regulatory compliance. It complements the MLOps lifecycle by integrating technical monitoring into a regulatory framework.

AI governance involves establishing processes, standards, and frameworks to ensure that AI systems are developed and used responsibly, ethically, and in compliance with regulations–see this reference. It encompasses ethical guidelines that promote fairness, transparency, and accountability, ensuring AI respects human rights and societal values. Regulatory compliance is critical, helping organizations adhere to laws such as the EU AI Act, which sets specific requirements for high-risk AI applications. Effective risk management is essential to identifying and mitigating potential risks like biases, privacy issues, and harmful outcomes. AI governance requires collaboration among various stakeholders, including developers, users, policymakers, and ethicists, to create a comprehensive approach that aligns with societal expectations and maintains public trust.

Designing and Deploying AI Governance with IBM watsonx.governance 

At CROZ, we approach MLOps and the design of AI governance through a looping paradigm, continuously refining our processes based on practical application and community insights. Our methodology begins with defining an AI use case and identifying the involved actors, focusing on risk identification and stakeholder engagement. We prioritize governance first, ensuring the ethical use of AI and integrating AI applications within the broader governance and risk management framework. Once governance structures are in place, we proceed with MLOps as usual, implementing robust model development and deployment practices.

We maintain a continuous loop between MLOps and AI governance, recognizing that models and regulations can evolve. This iterative approach ensures that our projects comply with current standards and adapt to future changes. By combining technical expertise with a strong governance mindset, we ensure that our AI initiatives are innovative and compliant, ready to meet the challenges of a dynamic regulatory landscape. 

Let’s examine a practical example of fraud detection. This example uses publicly available data available on Kaggle and is intended for demonstration purposes. 

The first step is discussing the fraud detection modeling business case with the relevant stakeholders. We track it with IBM watsonx.governance by opening an AI use case that details the associated risk, a description of the model used in the business context, and the model owner. This latter can overlap with different personas with a business or technical background. The essential task is matching business information with compliance regulations and the model’s technical metrics. 

The following two steps are the foundation of MLOps practices and data science projects. They include modular pipelines for sample split, feature selection, and experiments across different models to predict fraudulent transactions. 

A data scientist or ML engineer would then assess the performance and compliance of the different experiments to move into the project’s next stage for model testing and validation.

At this stage, the AI governance framework loops in again because relevant technical information for each stage of the model lifecycle – the development, testing, deployment– need to be recorded into the AI Factsheet, a document that complements the AI use case, an entity that is by definition broader and contains risk’s details of the risk and regulation (for example, DORA, GDPR, the forthcoming EU AI Act). 

Embedding models’ technical metrics into the AI Factsheet and aligning them with business context information in the AI use case offers several advantages. It enhances explainability and fairness in machine learning modeling by ensuring that metrics are transparent and consistently discussed. This practice helps maintain fairness, reduce bias, and improve explainability, especially as data samples evolve. 

Additionally, this approach strengthens risk management. A looping MLOps-AI governance framework promotes transparency and continuous assessment, which can effectively mitigate risks associated with model drifts and other potential issues. This ongoing evaluation helps ensure that models remain reliable and compliant with regulatory standards. 

Deploying AI Governance for auditing 

From a pragmatic point of view, AI governance is essential to ensure AI auditors can assess the AI framework underlying the solution. AI audits are vital for ensuring compliance with regulations and managing risks associated with AI systems. These audits involve a comprehensive assessment of the AI system’s compliance with laws like GDPR and the EU AI Act and evaluating potential biases and security threats. Continuous monitoring and regular audits help maintain the system’s performance and address emerging issues. Organizations can mitigate risks, ensure ethical AI practices, and build trust with their users and stakeholders by implementing robust AI accountability measures. 

AI Governance Essentials 

Since the landscape of AI regulations is evolving and pressure on establishing solid AI governance is growing, organizations are hurrying up to establishing internal processes and outline frameworks. The path can look overwhelming, and the best strategy is to tackle AI governance in steps. A roadmap to achieve such goals may look like the following: 

1. Define Governance Roles: Assign clear responsibilities for AI oversight and involve diverse stakeholders.
2. Schedule Regular Audits: Plan and conduct periodic audits using third-party auditors. 
3. Perform Impact Assessments: Use Data Protection Impact Assessment to evaluate risks and document findings. 
4. Ensure Transparency: Provide accessible documentation on AI operations and decision-making. 
5. Maintain Human Oversight: Train operators for intervention and establish intervention protocols. 
6. Develop Accountability Metrics: Create and monitor performance metrics for AI systems.
7. Engage Stakeholders: Involve community members in discussions and gather feedback.
8. Publish Reports: Regularly release reports on AI performance and accountability.

By fostering a culture of compliance, responsibility, and innovation, we can build trustworthy AI systems that benefit everyone.