Artificial intelligence (AI) governance is how companies can ensure their AI and machine learning projects achieve their goals. Company goals must respect business requirements while avoiding biases and ethical risks. Today, AI is a powerful tool for companies that can provide a critical advantage over competitors. However, it can also backfire if the right processes to control and validate results aren’t in place.

AI models that affect people – like those in cases like fraud detection, default prediction, and others – can generate ethical concerns, inadvertently incorporating undesirable biases based on race, nationality, and other characteristics.

The large models used in AI also lose interpretability, which can diminish end-user trust. This is one of the key aspects for the success or failure of AI projects. Interpretation and transparency are essential elements for building trust. That transparency also dispels concerns about data privacy, a subject under intense scrutiny from both users and regulators. Users can’t just comply with rules – they must demonstrate how they do so.

Above all, good AI governance allows you to reap the benefits of AI while complying with regulations, respecting privacy and ethical concerns, and building trust with your customers.

AI is a Team Sport

Altair has always believed AI is a team sport. One of the most important requirements for good AI governance is the possibility of bringing all stakeholders together into the same discussion over an AI use case. To make an AI project successful, businesspeople must pose the right questions based on their knowledge of the market and the company's needs. Analysts or subject matter experts must be able to design and architect the right solutions. The contribution of data scientists is often crucial in providing technical advice about the project’s feasibility and technical aspects. This collection of personas – each bringing a unique perspective and knowledge to the problem at hand – is the most effective way to ensure proper AI governance.

We’ve built this philosophy directly into our products. Altair® AI Cloud – part of the Altair® RapidMiner® data analytics and AI platform – has been designed as a multi-persona platform, where users with different points of view can contribute. Altair AI Cloud supports full automation for novices, notebooks or IDEs for data science experts, a visual drag-and-drop workflow design for those in between, and guided auto AI or dashboarding for more business-oriented users. Altair AI Cloud also provides project management capabilities by integrating these tools. These capabilities provide full internal transparency, which is the best way to guarantee that standards are enforced, and best practices and guidelines are followed. 

From Development to Production

By some means, AI projects can be software development projects. AI projects can be software development projects if they are implemented through coding or visual tools. Development and operationalization processes must be adequately managed to preserve quality and security. Transparency and consistency require the separation of development and production, while keeping the path from one to the other clear. 

Altair AI Cloud is both a development and a deployment platform for AI projects. Solutions undergo the cycle of development, testing, releasing in production, and consent validation, with security and best practices enforced at each stage. AI solution generation needs a clear framework at the base of AI governance.

Change Management and Accountability

Change management is another key aspect of governance. Model development versions of code and production enable projects to reproduce current and past situations. Both development versions understand the project’s evolution. The project’s changes provide a full audit and help with compliance and security requirements.

Managing Artifacts Inside an AI Project

AI projects can be viewed as models that get deployed to make predictions, but it’s a bit more complex than that.

Data

Data is typically stored in diverse data sources, like local or cloud data lakes. Data comes from traditional structured databases to unstructured text. An important aspect of AI governance is getting the right access to data. Altair AI Cloud’s connectivity framework allows administrators and data owners to control how their data is used and is adapted to modern authentication methods.

Code

Code is needed to transform data, prepare it for model training, and train models. Code reads the data and uses the model to make predictions. In addition, code needs to be managed. Altair AI Cloud uses standard Git, which includes the experience and best practices coming from standard code development.

Models

Models trained within the platform can be complex. Usually, many models are trained as part of the experimentation and optimization process. The model’s historical information is critical once the model is in production. Historical information includes data used in training, model performance, and the performance’s evolution. This allows users to decide what model to use based on performance, interpretability, potential biases, and more.

Conclusion

AI governance has multiple components, including:

• Bias minimization and control
• Transparency
• Interprebility of results
• Change management
• Security
• Compliance

By integrating Altair AI Cloud for AI governance, organizations can build trust amongst their customers, streamline communication, increase security and privacy, and comply with all regulations. Organizations can unify the entire data science life cycle from data exploration and machine learning to model operations and visualization. In summation, Altair AI Cloud provides an enterprise-ready data science solution that makes data science more engaging and approachable for all. 

To learn more about Altair AI Cloud, visit https://altair.com/altair-ai-cloud.