According to a new McKinsey Global Survey, adoption of artificial intelligence continues to advance, but the same foundational barriers still remain when trying to create value from AI at scale.
Among the challenges to successfully adopt AI is bias in data and algorithms. Audrey Boguchwal, Senior Product Manager at Samasource shares four practical approaches to training data strategy that can help AI teams avoid the effects of training data bias.
Models trained on biased data can be less accurate, resulting in insufficient training for your algorithm. Recent studies have shown that biased data can result in problems with facial recognition used in identification, surveillance, and law enforcement. Biased data can also perpetuate historical, negative stereotypes across race and gender.
Ensuring that reality is always represented in your data is a constructive way to minimize the impact of data bias, however, a clear training data strategy to legally and ethically source the data AI requires is fundamental for developing smarter models.
An effective training data strategy can help you determine ways to mitigate unwanted bias. Audrey Boguchwal, Senior Product Manager at Samasource will present "Practical Approaches to Training Data Strategy: Bias, Legal and Ethical Considerations this year at the 2019 Embedded Vision Summit conference.
Audrey's presentation will expand on the four strategies to avoid training data bias in this post by exploring use-cases that show how unintended bias can creep into datasets, sharing tests to detect dataset bias, and outlining legal and ethical data sourcing considerations.
If you'll be attending Embedded Vision Summit, stop by booth #621 to discuss your training data needs with the Samasource team, or click below to request a demo of our cloud-based data annotation platform, SamaHub.
Currently a Senior Product Manager at Samasource, Audrey 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 BA in history from Harvard, an MA in anthropology from Columbia and an MBA from UNC Chapel Hill KFBS, Audrey is passionate a using technology and data analytics facilitate social impact and environmental solutions through technology.