As businesses start to scale the use of AI as a transformative power to innovate and be more efficient, they have to manage the risks that come from it. Specifically, when dealing with sensitive customer data and in regulated industries, governance is a mandatory aspect of operations. However, as AI becomes more prevalent, there are new gaps that need to be addressed in governing the lifecycle of data as well as the models trained on that data. At the same time, governance processes should not impede the iterative nature of Data Science experiments that help build and operate AI applications. Join IBM to hear why AI governance is becoming increasingly important in today’s age, from governing for control to governing for efficiency and outcomes.
Defining Constituents, Data Vizzes and Telling a Data Story
AI Governance — Drive Compliance, Efficiency, and Outcomes from Your AI Lifecycle
1. AI Governance — Drive Compliance, Efficiency,
and Outcomes from Your AI Lifecycle
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Scott Buckles
North America Business Unit Executive
Information Architecture