Princeton's Arvind Narayanan argues short-term A/B testing rewards deceptive UI like fake wait times while degrading long-term trust · Digg

/Tech13h ago

Princeton's Arvind Narayanan argues short-term A/B testing rewards deceptive UI like fake wait times while degrading long-term trust

Thomas Dietterich proposed crowdsourcing actual pickup times to expose manipulation

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Arvind Narayanan@random_walker#267inTech

This is how so many data-driven companies screw their users and shoot themselves in the foot. My guess is every time Uber runs a 2-week A/B test where they give some users accurate time estimates and other users deceptive time estimates, the deceptive arm wins — many people probably compare Uber and Lyft time estimates and pick one, so lying about wait times increases the number of trips booked in the short run. But what the data won't show is that people gradually catch on to the deception over a period of months or years and lose trust in the company or the sector altogether, and use the app less or quit entirely.

John Loeber 🎢@johnloeber

Standard experience booking an Uber

“5 minutes away”

I order it

“Finding your driver…”

“Pickup in 7 minutes”

I open my timer

9 minutes and 21 seconds later, it arrives

This is so tedious and I am tired of being deceived at the margin

6:29 AM · Jun 9, 2026 · 51.4K Views

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Thomas G. Dietterich@tdietterich

@random_walker Has anyone built an app to collect data? We enter the time estimate and the actual pickup time; build a predictive model, etc.

Arvind Narayanan@random_walker

This is how so many data-driven companies screw their users and shoot themselves in the foot. My guess is every time Uber runs a 2-week A/B test where they give some users accurate time estimates and other users deceptive time estimates, the deceptive arm wins — many people probably compare Uber and Lyft time estimates and pick one, so lying about wait times increases the number of trips booked in the short run. But what the data won't show is that people gradually catch on to the deception over a period of months or years and lose trust in the company or the sector altogether, and use the app less or quit entirely.

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