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AI Governance: Concepts, Criteria, and Credibility

While digitally-mature companies are busily implementing their data governance initiatives, the notion of AI governance is just now gaining awareness in the enterprise. Many organizations are still consumed with getting AI into production and may see governance as merely a “nice-to-have.” But there are strategic, ethical, and competitive reasons to start thinking about it now.

AI governance helps assure that AI outputs are aligned with an organization’s true intentions and its desired impact on end-stakeholders, be they internal or external. Done right, governance will address not just a model’s statistical accuracy but also help vet the training data and determine testing criteria and have re-use potential. Collaboration is key as well: data scientists and business teams have to be involved in the governance process, and their expertise must be captured in the governed models.

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Why Attend?

To learn more, join us for this free 1-hour webinar from GigaOm Research. The webinar features GigaOm analyst Andrew Brust and special guest, Sophie Dionnet, from Dataiku, a leader across the entire AI lifecycle.

You will discover:
• The roles of responsible AI and model management in AI governance
• Where governance fits in the progression of AI maturity
• How AI governance and model interpretability intersect
• Why organizations must bring risk managers and data scientists together

Who Should Attend:
• CIOs
• CTOs
• Chief Data Officers
• Business Analysts
• Data Scientists
• Machine Learning Engineers
• Data Stewards