Tomorrow, I’m meeting with a Google VP who’s working on Humane Tech issues - user exploitation, privacy, etc.
I’d like to present him with an estimate of the total of all users’ extra time wasted by YouTube each day due to recommendation algorithms. In other words, if we know that users spend, say, 100 million hours a day on YouTube, can we put a rough guess on how many of those hours are due to recommendation algorithms?
If I can do this, I can multiply those hours by some $/hour to give an estimate of the social cost of YouTube recommendations just for time wasted (this would not include the social cost of opinions changed by extreme or “fake news” videos).
Lots of analyses for governments do this - for example, when there is a traffic light that wastes commuters’ time, a traffic engineer will estimate the amount of time wasted, multiply it by some $/hour (cost of commuters’ time), to calculate the social cost of that faulty traffic light. The traffic engineer then uses this to calculate whether it’s worth fixing the intersection + traffic light.
Of course, it’s highly subjective which recommended YouTube viewings are “wasted time” and which are not. I would even be OK saying, “Youtube users spend XX million hours a day on recommended videos, and if 50% of these are wasted time, then the social cost of YouTube’s recommendation system, just accounting for wasted time, is $XX million/day.”
Does anyone have any ideas on this?
- Mike Lanza