Fair Isaac Corporation

09/22/2025 | Press release | Distributed by Public on 09/22/2025 06:05

The CIO’s Role in Achieving Responsible AI: Conduit in Chief

The word "conduit" can mean a lot of things. If you're an electrician, it's a tube that protects wires and cables, giving electricity a safe and organized path to travel. If you're a diplomat, a conduit can act as a channel of information and influence connecting two or more parties. And if you're a Minecraft player, a conduit provides superpowers, restoring oxygen to players underwater, giving them night vision, and increasing their mining speed by 16.7%. (Who knew?)

At FICO, I find myself functioning as "conduit in chief." As the chief information officer (CIO), spearheading the strategic use of technology and data at an AI decisioning and software company, I'm the conduit to operationalizing Responsible AI.

Why does the CIO-as-conduit role matter? Because, as revealed in the new FICO and Corinium report, "State of Responsible AI in Financial Services: Unlocking Business Value at Scale " CIO and CTO respondents report that only 12% of organizations have fully integrated AI operational standards. This gap represents a big opportunity for improvement across the financial services industry, with a critical emphasis on implementing Responsible AI standards-the key to building AI decisioning systems that can be trusted, thus unlocking their business value.

Furthermore, a platform is a premier vehicle by which Responsible AI standards can be achieved. Over 75% of the 252 business and IT leaders who participated in the study believe collaboration between business and IT leaders, and a shared AI platform, could drive ROI gains of 50% or more.

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Ideally, a platform breaks down functional silos across different teams and roles to build AI decisioning models, test, and monitor them. By codifying a corporate AI development standard and automatically enforcing it, a platform can speed time to deployment by greatly improving model performance, reducing risk, and ensuring accountability.

Here's a quick blueprint for how CIOs can do it.

Make Responsible AI a Platform Strategy, Not a Project

Many enterprises are saturated with point solutions that address various steps of the analytic model development process, from data wrangling to model performance testing. Unfortunately, a disconnected approach will not work over-time or have lasting success because there are no contiguous controls to ensure that models are robust, explainable, ethical, and auditable-the four cornerstones of Responsible AI. An end-to-end platform that encompasses every aspect of model development, deployment, and monitoring is the only way to get there.

Additionally, financial services organizations are extremely aware of the need for auditability; when a regulator asks how a decision was rendered, a complete response must be produced. Ideally, this audit log should be immutable and populated automatically throughout the model development lifecycle. Persisting each granular development decision to an immutable record, such as that provided by blockchain, smooths the audit process by delivering an irrefutable record of events.

Automate and Hardwire Responsible AI Standards through MLOps

Machine learning is at the heart of any AI decisioning model. Correctly implemented, machine learning operations (MLOps) is the key to operationalizing Responsible AI development and governance practices by automating and hardwiring them. A platform happens to be the best place to do it.

MLOps borrows concepts from DevOps, which is a combination of cultural principles, processes, and tools to accelerate software development, delivery, and operations. DevOps is nearly ubiquitous in software development and provides numerous principles that are mirrored in MLOps, including:

  • Pipelines: Continuous integration, continuous deployment (CICD) pipelines are the basis of the MLOps mindset. Controls, test cases, test scenarios, validations and more are embedded in the pipelining process; these mechanisms serve as the framework for automating and hardwiring Responsible AI practices into all ML and AI model development.

Fair Isaac Corporation published this content on September 22, 2025, and is solely responsible for the information contained herein. Distributed via Public Technologies (PUBT), unedited and unaltered, on September 22, 2025 at 12:05 UTC. If you believe the information included in the content is inaccurate or outdated and requires editing or removal, please contact us at [email protected]