More training data won’t fix biased AI if the labeling system itself is broken.
Annie Brown, founder and CEO of Reliabl, built a patented AI annotation approach after scaling a social media platform to 100,000 users.
They discuss:
Why an audio dataset labeled Black women’s neutral voices as angry, potentially corrupting model predictions.
How changing only the gender in a chatbot career prompt produced different job-search advice.
Why the label “racy” in Google Vision AI moderation can introduce gender bias.
How smaller, expertly labeled datasets can outperform massive volumes of noisy training data.
Why paying data workers better and involving local communities can improve AI accuracy.
How Reliabl uses human expertise to correct missing information in mapping datasets.
“you’re pushing more data through a tainted system and then it’s coming out tainted and you’re wondering why.”
If expert-labeled data can outperform larger datasets, should AI companies spend less on collecting data and more on the people labeling it?
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