Why mediocre ML is so dangerous to the business « Machine Learning Times

By: Duncan Gilchrist and Jeremy Hermann

Originally published in Delphina, May 29, 2024.

When I (Duncan) was on Uber’s Marketplace team, we would (semi) joke that we were lurching from crisis to crisis.

Our dozens of core machine learning products directly controlled billions of company dollars — targeted promotions, surge pricing, driver incentives, ETAs, pool matching, upfront rider fares, subscription upsells, the list goes on. We lived in paranoia that these were fundamentally broken in a way that would sink the business.

Every week brought a new potential Data Science Disaster. The turmoil would always start the same way: someone would find something suspicious in the data. It might have been a spike in our internal metrics like “zeros”, which measured riders who opened the app and didn’t see any cars available. Were we under-surging and losing riders because of poor reliability? Had we deployed too many rider promos? The wrong driver incentives?

Or it could have been a tweet — perhaps a celebrity saw a fare that seemed high, and then they walked 20 feet and the price changed materially — and of course screenshots went viral and started spiraling.

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