Focus | AI: Breaking Bias




Focus | AI: Breaking Bias

6 August 2019

New York

Added 01-Jan-1970

We all know how Al improves our lives: Cancer detection, traffic reduction, cat name-generators. But we also know its less promising side — that Al bias can cost businesses revenue, brand perception, and talent. According to the Capgemini Research Institute, 34% of consumers would stop interacting with a company if their AI interaction resulted in ethical issues. Find out how companies can unlock business value with unbiased AI.

Machines are designed to favor some data over others. But mathematical models weren't judged until very recently, and now their biases can pervade and distort operational reality to create unintended consequences that are hard to undo. Cue damaged employee-employer relationships and recruiting, lost customers, and more.

To fix it, we need to understand how it arises. In the latest edition of Focus Al, the NYU Tandon Future Labs partner with Capgemini's Applied Innovation Exchange to explore the stages of understanding and preventing data bias — framing the problem, collecting the data, and preparing the data. Microsoft Cloud Customer Success Director Jonn de Havilland is the keynote speaker. Hear from Julia Stoyanovich, NYU Tandon professor and cofounder of Data, Responsibly, on what companies can do — and discard — to improve data acquisition, analysis, and implementation. Data scientists Jason Kodish (Capgemini) and Triveni Gandhi (Dataiku), and AI thought leader William Thompson (Forbes Insights) explore how to deploy unbiased AI, critical for forward-thinking companies. Startups Accrete and Remesh examine traditional and alternative data for unpredictable trends and unconscious thought processes.