Introducing IBM AI Governance

2 min read

IBM AI Governance is a new, one-stop solution built on IBM Cloud Pak® for Data.

Many organizations struggle when adopting artificial intelligence (AI). Challenges include access to the right data, manual processes that introduce risk and inhibit scalability, multiple unsupported tools used in building/deploying and monitoring models and platforms and practices not optimized for AI. These challenges inhibit organizations from delivering transparent, explainable and trusted AI decisions that are necessary to meet today's growing AI regulations and ethical standards. Meeting these challenges requires AI governance designed to operationalize AI, manage risk and provide scalability while complying with growing AI regulations   

Understanding AI governance

AI governance drives control and predictability to help meet AI regulations and ethical concerns, and it can drive confidence even in these times of economic uncertainty. Automating the tracking and documentation of the origin of data, models and associated metadata and pipelines provides information for audits and addresses stakeholder, organizational and customer concerns. Documentation should include the data that was influential in the development of the model, the techniques that trained each one, the hyperparameters used and the metrics from testing phases. The result of this documentation is increased transparency into the model’s behavior throughout the lifecycle and its possible risks.

IBM AI Governance: Driving enterprise capabilities

IBM AI Governance is a new, one-stop solution built on IBM Cloud Pak® for Data using automated software that works with your organizations current data science platform. Included in this solution is everything needed to develop a consistent transparent model management process, capturing model development time, metadata, post-deployment model monitoring and customized workflows.

This new solution automates the capture of model metadata across the AI/ML lifecycle so data science teams can focus on other tasks, rather that model documentation. Data science leaders and model validators benefit from always having an accurate, up-to-date view of their models. Businesses benefit from the ability to scale and deliver transparent, explainable outcomes free from harmful bias and drift. IBM AI Governance increases the accuracy of predictions by identifying how AI is used and where retraining is indicated.

Model risk management is used in IBM AI Governance to identify, manage, monitor and report on risk and compliance initiatives at scale. Dynamic dashboards provide clear, concise customizable results that enable a robust set of workflows, enhance collaboration and drive AI regulatory compliance across multiple regions and geographies.

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