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Unsure if the particular tests mentioned are worthy of attention - think there are probably other more relevant controversial practices.
In this instance, recommendation engines helped people get jobs. If you're a relatively unknown musician on Spotify, being "blessed by the algorithm" and put into algorithmic playlists associated with more popular artist translates to real-life streams and income. If you're an unknown artist pushing work on Twitter or a wannabe "influencer" on Instagram/Tiktok/YT, showing up in the algofeed and getting a bunch of views, likes, and reposts could be what makes you "go viral" and start your career.
There's a cottage industry of people making a career out of posting content on platforms, or trying to, and one of the biggest factors is whether or not these opaque, vaguely understood algorithms happen to select your content to be recommended. Crazy as it may seem, deploying a retrained recommendation ML model could impact whether someone makes rent this month.
NYTimes runs social experiments on users for years: https://open.nytimes.com/how-we-rearchitected-mobile-a-b-tes...
Did you read the article? It's right in the article: > The New York Times uses such tests to assess the wording of headlines and to make decisions about the products and features the company releases.
> "Did you even read the article? It mentions that" can be shortened to "The article mentions that."
NYTimes runs social experiments on users for years: https://open.nytimes.com/how-we-rearchitected-mobile-a-b-tes...
Did you read the article? It's right in the article: > The New York Times uses such tests to assess the wording of headlines and to make decisions about the products and features the company releases.
https://web.archive.org/web/20220924090459/https://www.nytim...
Looks like they realized they got caught and updated it. TBH, I could 100% believe that the reporter and editor had no idea the NYT did it themselves, because anyone would be clued in enough to understand that wouldn't have written the article in the first place.
Every company with algorithmic recommendations will run experiments to try to improve it according to various metrics. This is the well known knowledge in the industry, not a conspiracy theory. Trying to improve peoples chance of getting a job as opposed to something lame like "engagement" is laudable. The key question is how big the difference between the groups. If the difference is marginal, then I think the knowl…
> The key question is how big the difference between the groups. 2x increase in job opportunities. > They found that relatively weak social ties on LinkedIn proved twice as effective in securing employment as stronger social ties.
Every company with algorithmic recommendations will run experiments to try to improve it according to various metrics. This is the well known knowledge in the industry, not a conspiracy theory. Trying to improve peoples chance of getting a job as opposed to something lame like "engagement" is laudable. The key question is how big the difference between the groups. If the difference is marginal, then I think the knowl…
> The key question is how big the difference between the groups. 2x increase in job opportunities. > They found that relatively weak social ties on LinkedIn proved twice as effective in securing employment as stronger social ties.
That's one piece of the puzzle but not the whole thing. It's not the same as saying they got 2x more job opportunities. People can add friends outside of the recommendations for instance. Or they may be added by the other person. I tried to quickly skim the paper just now but I was unable to find the details.
Framing A/B testing as "social experiments" for shock value is disingenuous, but it is interesting to think about the real-world knock-on effects that recommendation engines have. In this instance, recommendation engines helped people get jobs. If you're a relatively unknown musician on Spotify, being "blessed by the algorithm" and put into algorithmic playlists associated with more popular artist translates to real-…
Is it? I've only done minor human experimentation, but had to go through the IRB process. What I did was on par with simple AB testing. The only difference here is that the AB testing is not being done to be published and not under the purview of the IRB. If it were, they would require the same sort of approve for human experimentation. Sure, the paperwork is nothing like a drug test, and I think the IRB is extra heavy weighted on the side of seeking too much approval, but there is a history of human experimentation to explain why that is the case. That new unmonitored experiments are happening under tech employees running loose instead of scientists running loose doesn't really indicate any significant difference and we should be watchful that similar abuses of power as had previously happened don't reoccur. I think we've seen enough with facebook and their influence on children to know it is a real concern and we might already be far enough along the path to be worth raising the alarm.
Framing A/B testing as "social experiments" for shock value is disingenuous, but it is interesting to think about the real-world knock-on effects that recommendation engines have. In this instance, recommendation engines helped people get jobs. If you're a relatively unknown musician on Spotify, being "blessed by the algorithm" and put into algorithmic playlists associated with more popular artist translates to real-…
I ask because I am trying to understand where your objection is coming from. And if you would consider the above a social experiment, might it not simply be desensitization? A/B testing has become a somewhat regular part of 'engagement optimization strategies' at many companies which sounds perfectly vanilla, yet in many cases engagement is just the software word of choice in the above fill-in-the-blank.