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Make Data Work
September 11, 2018: Training & Tutorials
September 12–13, 2018: Keynotes & Sessions
New York, NY

Rationalizing risk in AI and ML

Kimberly Nevala (SAS Institute)
4:35pm–5:15pm Wednesday, 09/12/2018
Strata Business Summit
Location: 1E 12/13 Level: Non-technical
Secondary topics:  Ethics and Privacy, Machine Learning in the enterprise
Average rating: *****
(5.00, 1 rating)

Who is this presentation for?

  • CAOs, CDOs, analytics program managers, business executives, and data science practitioners

Prerequisite knowledge

  • A basic understanding of AI, ML, or advanced analytics concepts

What you'll learn

  • Learn how to rationally identify and quantify risks in AI and ML in order to overcome fear and resistance to change and how to design solutions that create effective man-machine collaboration while taking into account an organization's perception of and tolerance for risk

Description

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.

Photo of Kimberly Nevala

Kimberly Nevala

SAS Institute

Kimberly Nevala is the director of business strategies for SAS Best Practices, 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).