Too often, the discussion of AI and ML includes an expectation—if not a requirement—for infallibility. But as we know, this expectation is not realistic. When compounded by a lack of AI/ML experience, ethical concerns, and public exposure, this risk aversion can quickly derail an AI/ML program—assuming the program gets off the ground in the first place.
Given AI’s transformative potential, waiting for perfection is not an option. So what’s a company to do?
While risk can’t be eliminated, it can be rationalized. Kimberly Nevala discusses three key dimensions of risk that must be considered when designing your AI/ML solution. Using real-life applications, Kimberly demonstrates how a deliberate approach to managing risk enables AI/ML implementation and adoption.
Kimberly Nevala is a Strategic Advisor for SAS where she balances forward thinking with real-world perspectives on business analytics, data governance, analytic cultures, and change management. Kimberly’s current focus is helping customers understand both the business potential and practical implications of artificial intelligence (AI) and machine learning (ML).
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