xiii1408

[1] In all the planes I've flown, ADS-B is configured to transmit whenever the master electrical switch is turned on, but it can be configured to be turned on and off at will. See this video on a mid-air collision involving an eccentric character who liked to fly with ADS-B out turned off: https://youtu.be/G5y3JiOEnVs?si=rs5gNMurZ9ssUloS. If I recall correctly, he had his ADS-B wired to his nav lights so he could turn it on and off at will.

Here's a recent study on YouTube from the same author as this TikTok study finding left-leaning bias in US recommendations: https://academic.oup.com/pnasnexus/article/2/8/pgad264/72424....

Here's a somewhat older study from Twitter itself where they determined that their recommendations were biased toward right-leaning accounts: https://cdn.cms-twdigitalassets.com/content/dam/blog-twitter....

IMO the interesting question is not whether an individual platform is biased and what its biases are, but rather how we might regulate recommendations given that there is always a risk of bias.

- Results-based enforcement. i.e., companies are free to use whatever recommendation model they like, but have to recommend content within ideological bounds. i.e., you can't bias toward one partisanship more than X%. There's some precedent for this with the equal-time rule (https://en.wikipedia.org/wiki/Equal-time_rule) and FCC fairness doctrine (https://en.wikipedia.org/wiki/Fairness_doctrine).

- Algorithm-based enforcement. i.e., there are limits on the algorithm itself. Perhaps you have to present your algorithm to a government agency and provide a proof that it obeys certain properties. But the enforcement here is analytical rather than empirical.

Very similar methodology to an earlier study the government cited in their case against TikTok: https://networkcontagion.us/wp-content/uploads/NCRI-Report_-...

There are a number of issues with these studies, one being that the way the sock puppet bots interact with content is not exactly organic. Typically they search for content in a conditioning phase, followed by random scrolling during which the recommended videos are collected and classified by an LLM. Modern recommendation algorithms famously work by examining how long and how users engage with content, and there's none of that going on here. Still, the methodology itself and the use of LLMs to classify content is clever and probably about the best we can get.

Also, even if there _is_ a bias, it doesn't tell us why. Are the recommendations intentionally spiked, or is this simply the recommendation strategy that maximizes profit? (Or that the recommendation model thinks will maximize profit?) It's very difficult to tell, which is part of what makes these models dangerous and also part of what makes them difficult to regulate.

On a sidenote, TikTok (and presumably other content platforms) _really_ does not like these studies, as demonstrated by them nerfing search functionality after the second study above was released to prevent researchers using these techniques in the future. I haven't read the study in detail yet, but it will be interesting to see how the team at NYU Abu Dhabi adapted their methodology.