Recent McKinsey research cites the top five limitations that prevent companies from adopting AI technology. Training data strategy is a common thread. Companies face challenges obtaining enough AI training data, developing strategies for robust data quality and ensuring that bias does not occur. In this presentation, we’ll explore training data strategies that avoid bias in the data and that consider legal and ethical factors. You will learn about common types of bias, how bias can creep into datasets, the impact of bias, how to avoid bias and how to test your model for bias. We’ll discuss legal and ethical considerations in data sourcing, including real cases where legal and ethical complications can arise, the impact of these complications, and best practices for avoiding or mitigating them.
Practical Approaches to Training Data Strategy: Bias, Legal and Ethical Considerations
Session Speakers
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Audrey Jill Boguchwal
Senior Product Manager, Samasource
Audrey is passionate about the intersection of ethics and artificial intelligence. As a trained anthropologist, she is fascinated by understanding how humans interact with technology and how we can use technology for good. Currently a Senior Product Manager at Samasource, a provider of training data for the world’s leading AI technologies, she guides cross-functional teams to create thoughtful product solutions. She has guided teams of designers and engineers at HUGE Inc. and NBCUniversal, and monitored user analytics at the Wall Street Journal. With a unique background in anthropology and an MBA, she is able to balance the voice of the customer with the goals of the business. Audrey is also passionate about using her digital technology and data analytics skills to facilitate social impact and environmental solutions through technology. Audrey holds a BA in history from Harvard, an MA in anthropology from Columbia and an MBA from UNC Chapel Hill KFBS.
