Responsible AI With Dataiku: Scale With Speed & Trust

Safely Procure & Process Data for Responsible AI to Mitigate Biases Upfront

Responsible AI best practices begin before machine learning models are even created. To reduce the risk of biased ML models, the first step is to ensure the correct data preparation methods are used. This will provide a solid foundation for the ML models to be built upon.

Dataiku provides powerful tools to promote Responsible AI principles. These include advanced statistical analysis, enhanced data discovery, metrics and checks, and data privacy that meets major regulations (i.e., GDPR, CCPA).

Experiment & Build Responsible AI Models While Controlling for Quality

Ensuring models are fair can be a difficult and lengthy task, especially at scale. Dataiku provides features to improve the accuracy of your Responsible AI process.

Model assertions and debugging allow you to check if predictions for subpopulations meet certain conditions. Additionally, they provide warnings if new model versions don’t work as expected. Sanity checks for model training identify imbalances in your datasets and variations in variable importance.

Detailed Reports to Detect Issues Quickly

Model fairness reports allow you to choose metrics to measure bias in your model:

• Demographic Parity
• Equalized Odds
• Equality of Opportunity
• Predictive Rate Parity

Partial dependence plots and subpopulation analysis help you easily understand how predictions change at global, regional, and local levels.

Build Easy-to-Understand Documentation & Visualization for Responsible AI

Dataiku offers explainable AI features to bring greater accountability and transparency to your ML process. This helps your team quickly understand where your models stand. With individual row-level explanations, discover why a model is producing a given prediction and easily export via API. 

Analyze different input scenarios with what-if analysis and robust analytics. Publish the results to give business users better visibility across all roles. Teams can also leverage model evaluation and monitoring to calculate key model metrics and get notified for specific model errors. 

Lastly, generate model documentation that includes what the model does, as well as how it was built, tuned, and performed. The documentation can be automated for regulatory compliance or in accordance with company rules. It is also collaborative and has traceable chats, task assignments, and wikis.

Define an AI Governance Strategy to Pair With Responsible AI

When models are pushed to production without the appropriate risk management, they may run afoul of Responsible AI best practices.

With Dataiku Govern, you can build out the right checks and balances without slowing down your analytics and AI teams. Governance teams often have a set of criteria that need to be made tangible and measurable. This enables analytics and AI teams to align their work with those expectations.

Teams can create standard project plans for the development and deployment of analytics and AI projects. Additionally, they can leverage workflow blueprints with clear steps and gates.

Drive Visibility and Ensure Audit Readiness

Dataiku provides a central registry that includes models (whether developed in Dataiku or externally) and analytics projects. It allows users to see all models and projects in one place, versioned and with performance metrics.

Moreover, project summaries are provided for leaders and project managers. Finally, ensure audit-readiness on deployment decisions with model sign-off prior to deploying AI. 

Responsible AI is one dimension of how we support safely scaling AI with AI Governance and MLOps. ML development lifecycle activities and checks can be shared with governance teams to ensure principles are followed. MLOps teams monitor important metrics, like model bias and assumptions.

Support & Guidance Following Responsible AI Industry Best Practices

Dataiku experts will go beyond just the technical aspects of Responsible AI. They will help identify areas of opportunity tailored to your company’s needs. Core features support the responsible development of AI pipelines. These features include data exploration, privacy controls, model robustness and fairness, explainability, and documentation. 

Leverage risk/value mapping workshops to prioritize Responsible AI initiatives and translating intentions to actionable checklists. Teams can be educated on Responsible AI, both on a high-level and practical level.

This will enable them to put Responsible AI practices into action. It will also help ensure they are accountable for the new types of risks being addressed by regulation around fairness and quality of data. Finally, Responsible AI reviews of high-risk projects and support for custom bias detection and mitigation techniques are available.