Addressing Bias in Face Recognition
Addressing Bias in Face Recognition


AI models guiding autonomous vehicle systems must be trained to anticipate and react correctly (i.e. safely) when faced with any of the myriad road scenarios they could encounter. Industry leaders are harnessing ever-greater volumes of data in order to perform this training, exposing AI to vast image datasets of known road factors and collected experiences that AI can call upon when interpreting given scenarios. This exposure to new data is essential to the pursuit of Level 3 (L3) autonomous driving, in which vehicles are able to take responsibility for all safety-critical functions under certain conditions (but where the human driver must take over when the AI encounters an unfamiliar situation).
AI is already able to solve many discrete driving issues, such as visually recognizing traffic light colors and obeying those rules. However, the tougher challenge is that, while image dataset volumes are exponentially increasing, this growth leads to diminishing returns as far as how much that data actually improves an AI model. And while L3 autonomous driving means that AI can navigate safely in all but less common “edge cases,” the reality is that driving in the real world means encountering such edge cases fairly frequently.
Because of this, autonomous vehicle companies face significant questions as to how to know when their AI has reached the threshold of being safe enough for the road – and how much data that is going to take. This question is exacerbated by ongoing consumer expectations around (and apprehension about) self-driving cars, where even achieving accident rates that are twice or three times safer than human drivers may still not be enough to win their trust.
The presentation will address the question of how much data will be required to succeed in reaching L3, and possible paths to get there and beyond.


Doug Aley has spent his career helping to found, lead, and scale startups. He is currently Ever AI’s CEO and an Operating Partner at Atomic, a venture fund based in San Francisco, CA. Prior to Ever AI, Mr. Aley held positions as a Sr. Product Manager early in his career at Amazon ($AMZN), was VP of Marketing, Product, and Business development at Jott Networks ($NUAN), helped Zulily scale from $100M to $700M in sales as VP of Product and Corporate Development in the run up to the company’s IPO ($QVCA.O), held the same role at Room77 ($GOOG), and started and led Minted’s (private) digital growth team. At 19, he co-founded and was CEO of his first company, Level Access (sold majority stake to JMI Equity in 2017). He currently serves on the boards of PACT and The Marin Montessori School. Mr. Aley holds a BA from Stanford University and an MBA from Harvard Business School, and lives in Greenbrae, CA with his wife, Susan, and their two boys.

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