Speaker Details

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Associate Principal Analyst at GetApp

Lauren Maffeo


Lauren Maffeo has reported on and worked within the global technology sector. She started her career as a freelance journalist covering tech trends for The Guardian and The Next Web from London. Today, she works as an associate principal analyst at GetApp, where she covers the impact of emerging tech like AI and blockchain on small and midsize business owners.

Lauren has been cited by sources including Forbes, Fox Business, CIO Online, DevOps Digest, The Atlantic, Entrepreneur, and Inc.com. Her writing on technology has also been cited by researchers at Cornell Law School, Northwestern University, and the University of Cambridge. She has spoken at global events including Gartner’s Symposium in Florida, The World Web Forum in Zurich, Open Source Summit North America in Vancouver, and DrupalCon in Seattle.

In 2017, Lauren was named to The Drum’s 50 Under 30 list of women worth watching in digital. That same year, she helped organize Women Startup Challenge Europe, which was the continent’s largest venture capital competition for women-led startups. She has served as a mentor for Girls in Technology’s Maryland chapter, and DCA Live included her in its 2018 list of “The NEW Power Women of Tech”. Lauren holds an MSc from The London School of Economics and a certificate in Artificial Intelligence: Implications for Business Strategy from MIT’s Sloan School of Management.


Lauren’s

Tech Talk


3:15 PM | Track #3

6 steps to stop ethical debt in AI product development

Advances in AI techniques like machine learning and deep neural networks have potential to save time and boost productivity. But what if we train these technologies using datasets that exclude large portions of the population?

For example, some facial recognition software doesn’t acknowledge dark skin. Why? People of color were excluded from the datasets that were used to train the software. If AI isn’t designed with inclusion upfront, its rewards won’t equally benefit us all. The good news is that while such unconscious bias is unavoidable, it is not insurmountable.

This talk will share:

* What machine bias is
* Why it’s dangerous for end users
* The root cause of machine bias
* How to add bias testing to product development lifecycles.