Operationalizing Responsible AI Standards: Why a Platform Approach Matters

There are thousands of AI and GenAI development tools, and dozens of unified decisioning platforms available today—for example, on a recent visit I made to a large bank in the Asia Pacific region, 27 unified decisioning platforms were said to be in use across their organization!  

Given the complexity of building AI systems that are responsible—that is, robust, explainable, ethical, and auditable—granular AI development standards must be defined and then enforced across the organization. Then, once developed, the AI needs to be deployed and properly monitored while in production. This simply can’t be done in enterprises with a sprawling hodgepodge of AI tools and decisioning platforms––that’s where a unified decisioning platform becomes critical.  

How a Unified Decisioning Platform Enforces AI Standards 

A unified decisioning platform provides a central place for managing the expectations of Responsible AI in operation. The platform combines data management, AI and machine learning (ML) execution, and AI monitoring, decisioning, and execution. Responsible AI goes beyond developing AI capabilities properly; it requires an emphasis on responsible deployment including monitoring, passing the correct data, audit, and delivering the right outcomes.  

Responsible AI also comes with expectations for what is required to deploy AI models, such as essential auditable AI capabilities like AI blockchains, which are largely ignored in AI deployment today. These capabilities provide full transparency into whether the AI or GenAI system is operating correctly; decision rules are executed as expected, and decisions are in accordance with corporate policy. 

To operationalize decisioning capabilities that enforce Responsible AI standards, the unified platform must also be able to:   

• Employ interpretable neural networks that make the latent behaviors, which drive machine learning outputs, easily understandable by human analysts at banks and regulatory agencies, thus speeding the path to production.

• Ensure that GenAI models trust scores are sufficient to confidently decision using model outputs. This derived risk-based score allows users to assess whether specific GenAI output is likely correct, reliable, and trustworthy—the key to financial institutions operationalizing GenAI at scale.

• Monitor bias thresholds in latent features during deployment and compare them to their values in AI development. Latent features can interact in unintended ways, potentially introducing bias and other malignancies into production environments.

• Monitor any shift in reason codes and latent feature activation. Changes can either reveal customer data that deviates from the behaviors on which the model was trained, indicating potential bias, the need for decision strategy adjustment, or dropping to an Humble AI alternative decisioning.

• Monitor production systems for model performance and for model drift, yet another factor that can introduce reduced accuracy, bias, and other unforeseen consequences.  

Finally, the unified decisioning platform must offer auditability of the entire development process and, equally important, during deployment. This can only be achieved through an immutable record—such as one created with blockchain technology—to provide proof of adherence to Responsible AI standards and continued monitoring to confirm that the model operates in adherence.