AI Governance: Your Business’s Competitive Edge or Its Biggest Risk?

Author(s): David Sweenor

Originally published on Towards AI.

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The bridge to responsible innovation must be robust. Photo by author David E. Sweenor

As artificial intelligence (AI) becomes ubiquitous, it’s reshaping decision-making in ways that go far beyond the scope of traditional business automation. Using a combination of predictive and generative AI, systems can now make tactical, operational, and strategic decisions at scale. They dynamically adjust product prices, recommend your next binge-worthy TV show, and generate sales and marketing content for mass diverse audiences. Yet scaling such AI use cases requires governance frameworks that do more than just manage data — effective AI governance frameworks encompass systems that continuously learn, adapt, and operate with minimal human intervention.

In this blog, we’ll unpack the differences between data and AI governance, examining the new factors leaders must consider when designing their AI governance programs.

What makes AI governance different from data governance? AI governance focuses on outputs–the decisions, predictions, and autonomous content created by AI systems. As the world turns and data drifts, AI systems can deviate from their intended design, magnifying ethical concerns like fairness and bias. Such off-track systems might invade privacy, inadvertently release intellectual property (IP), and exacerbate nontransparent decision-making…. Read the full blog for free on Medium.

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Published via Towards AI