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You're missing the point. They don't need to release the actual code, but releasing "these are the inputs we use, these are the high level functions of the pip

You're missing the point. They don't need to release the actual code, but releasing "these are the inputs we use, these are the high level functions of the pipeline, these are the outputs" would be enough. And the fact that pipelines that drive how people consume an increasingly large majority of their content have become this convoluted, should get you annoyed at social media companies: not people who don't understand how bad its gotten

korla 4y ago
Isn't the point that companies should be judged by the cost of their actions, and not the details of their implementations?
- BoorishBears 4y ago
  Why the false dichotomy? The cost of their actions is easier to frame if you know the details of their implementation. If people just naturally like using FB that's fine. If FB is hiring experts for behavioral studies in order hypertune a pipeline that exploits blind spots in the way the human brain works... that's a lot less fine. And if the inputs are wider in scope than the general public realizes, it becomes even less ok. People will always have free will and most would rather we don't try and override that by just making all social media illegal or something... but there's an inherent asymmetry in the resources social media companies have in dissecting the psyche vs individuals have countering that. Forcing them to show their cards is one way to thrust the results of that asymmetry into the spotlight and better equip individuals who often don't understand the extent to which they're being manipulated.
threeseed 4y ago
> these are the inputs we use, these are the high level functions of the pipeline, these are the outputs" would be enough You do understand that these are ML models where there is no way to explain their behaviour in a layman way other than what is bleedingly obvious. That it will try and show you more content that you and the people you follow might like.
- beezlewax 4y ago
  It isn't entirely about what you like, it is about what will drive interaction. This is why hateful posts or controversial ones rise to the top so frequently. It encourages bad and often anti social behaviour. They've created a monster.
  - pixl97 4y ago
    I disagree, the monster always existed. There is a reason "If it bleeds, it leads" was a saying long before social media existed. Also, "A lie is half way around the world before the truth puts its pants on". These are default human behaviors that have always existed. In the past there were particular global limits on the spread of these behaviors base on things like "A horse only goes so fast", or there are a limited number of telegraph lines or radio stations. Controversial, hateful stuff was always going to rise to the top. The difference between now and then is we have pretty much unlimited bandwidth covering the globe to spread the message. If you made FB disappear you're going to see the exact same behavior in mastadon servers, just hoping the scale is limited by lower audience sizes.
- mjburgess 4y ago
  No ad platform has access to what people "like", this is a psychological property of people that isnt measurable, and at least, isnt being measured. ML is perfectly explainable, and all this mysticism around what are just weightings of features is silly. What all recommendation platforms could be required to provide is which are their most important variables for determining what a user sees, and sensitivity analysis.
  - simplotek 4y ago
    > No ad platform has access to what people "like", this is a psychological property of people that isnt measurable, and at least, isnt being measured. You're either commenting on something you are oblivious about, or are pulling a red herring just to be contrarian. Concepts like revealed preferences [1] are nearly a century old, not to mention the fact that the whole consumer behavior field as a whole has plenty of quantitative methods to infer "what people like". Facebook and Twitter support a feature where quite literally users specify in no ambiguous term that they like something. Theirnl platform is so effective that their problem is filtering out bad actors. Then there's also engagement. You can get a pretty good idea of what people are interested in by tracking what they read. You might try to nitpick about the true meaning of "like" but you'd need to be really determined to intentionally miss the point. [1] https://en.wikipedia.org/wiki/Revealed_preference https://en.wikipedia.org/wiki/Revealed_preference
imsaw 4y ago
But given the many parties trying to grab a slice of our attention on the platform, wouldn't knowing the algorithm inputs produce more exploitations?
simplotek 4y ago
> They don't need to release the actual code, but releasing "these are the inputs we use, these are the high level functions of the pipeline, these are the outputs" would be enough. I'm not sure you thought this problem through. Even if you can specify the inputs, the high level functions mean nothing and the output can and very often is determined by implementation details and the current state of the whole service. Say for example that personalized recency search of tweets takes into account bad actors being banned/shadow banned/downanked. How exactly do you expect to specify what output you expect from your inputs and your high-level description of your pipeline when a specific spammer is downranked? I'm sure you can setup a pretty little diagram, full of colors and nice rounded edges, but it would be completely meaningless as state matters, and it's practically impossible to specify what state a global or regional system has in place.

korla 4y ago

Isn't the point that companies should be judged by the cost of their actions, and not the details of their implementations?

BoorishBears 4y ago
Why the false dichotomy? The cost of their actions is easier to frame if you know the details of their implementation. If people just naturally like using FB that's fine. If FB is hiring experts for behavioral studies in order hypertune a pipeline that exploits blind spots in the way the human brain works... that's a lot less fine. And if the inputs are wider in scope than the general public realizes, it becomes even less ok. People will always have free will and most would rather we don't try and override that by just making all social media illegal or something... but there's an inherent asymmetry in the resources social media companies have in dissecting the psyche vs individuals have countering that. Forcing them to show their cards is one way to thrust the results of that asymmetry into the spotlight and better equip individuals who often don't understand the extent to which they're being manipulated.

threeseed 4y ago

> these are the inputs we use, these are the high level functions of the pipeline, these are the outputs" would be enough You do understand that these are ML models where there is no way to explain their behaviour in a layman way other than what is bleedingly obvious. That it will try and show you more content that you and the people you follow might like.

beezlewax 4y ago
It isn't entirely about what you like, it is about what will drive interaction. This is why hateful posts or controversial ones rise to the top so frequently. It encourages bad and often anti social behaviour. They've created a monster.
- pixl97 4y ago
  I disagree, the monster always existed. There is a reason "If it bleeds, it leads" was a saying long before social media existed. Also, "A lie is half way around the world before the truth puts its pants on". These are default human behaviors that have always existed. In the past there were particular global limits on the spread of these behaviors base on things like "A horse only goes so fast", or there are a limited number of telegraph lines or radio stations. Controversial, hateful stuff was always going to rise to the top. The difference between now and then is we have pretty much unlimited bandwidth covering the globe to spread the message. If you made FB disappear you're going to see the exact same behavior in mastadon servers, just hoping the scale is limited by lower audience sizes.
mjburgess 4y ago
No ad platform has access to what people "like", this is a psychological property of people that isnt measurable, and at least, isnt being measured. ML is perfectly explainable, and all this mysticism around what are just weightings of features is silly. What all recommendation platforms could be required to provide is which are their most important variables for determining what a user sees, and sensitivity analysis.
- simplotek 4y ago
  > No ad platform has access to what people "like", this is a psychological property of people that isnt measurable, and at least, isnt being measured. You're either commenting on something you are oblivious about, or are pulling a red herring just to be contrarian. Concepts like revealed preferences [1] are nearly a century old, not to mention the fact that the whole consumer behavior field as a whole has plenty of quantitative methods to infer "what people like". Facebook and Twitter support a feature where quite literally users specify in no ambiguous term that they like something. Theirnl platform is so effective that their problem is filtering out bad actors. Then there's also engagement. You can get a pretty good idea of what people are interested in by tracking what they read. You might try to nitpick about the true meaning of "like" but you'd need to be really determined to intentionally miss the point. [1] https://en.wikipedia.org/wiki/Revealed_preference https://en.wikipedia.org/wiki/Revealed_preference

imsaw 4y ago

But given the many parties trying to grab a slice of our attention on the platform, wouldn't knowing the algorithm inputs produce more exploitations?

simplotek 4y ago

> They don't need to release the actual code, but releasing "these are the inputs we use, these are the high level functions of the pipeline, these are the outputs" would be enough. I'm not sure you thought this problem through. Even if you can specify the inputs, the high level functions mean nothing and the output can and very often is determined by implementation details and the current state of the whole service. Say for example that personalized recency search of tweets takes into account bad actors being banned/shadow banned/downanked. How exactly do you expect to specify what output you expect from your inputs and your high-level description of your pipeline when a specific spammer is downranked? I'm sure you can setup a pretty little diagram, full of colors and nice rounded edges, but it would be completely meaningless as state matters, and it's practically impossible to specify what state a global or regional system has in place.