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Comment by simplotek | Hacker News Reader

> I'm always more impressed that sites like twitter and facebook need so many engineers for what are fundamentally fairly simple platforms that haven't hugely changed in years

The bit that you get wrong is assuming that Facebook or Twitter are simple platforms, and that their main and most complex features is allowing users to post, search and read messages. This is entirely wrong as these platforms are actually ad-selling platforms and thus include region-specific services, localized ranking, all sorts of data harvesting, etc etc.

Also, as companies grow they start to feel the need to implement efficient development processes, and thus start to allocate resources to stuff like putting together their own build farms, CI/CD infrastructure, deployment auditing, etc etc etc. They also develop things like front-end and back-end frameworks. It's not like a random team at Facebook just signs up for gitlab and does all it's business from there.

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Likewise, I get annoyed when I hear people scream "publish the algorithm!" at social media companies. There isn't an algorithm. There's an enormous number of applications, some of which are data science/ML-based, that rely on stream and batch processing of incoming and pre-existing data to render recommendations and timelines. This is far more than an algorithm, this is an entire ecosystem, more complicated than a single individual can grok without an enormous amount of time on their hands (and the experience to understand it all).

It's a good question to ask nonetheless, if the answer is "we can't tell you exactly why we exposed you to this information preferentially, oh and by the way, we have no way to prove that pushing divisive and sometimes assumed fake content isn't an emerging property of this insanely complex system meant to generate revenue for our bottom line with close to no checks in place",

then the follow-up argument should be a much needed (IMO) "simplify this crap so users and regulators come to know how much neutrality and facts they get served".

I understand enough ML to know how and why such systems can't be introspected and why their output is inscrutable to humans, but that's where I draw the line personally: a key component of your business, or one that affects many users, shouldn't be permitted to be so complex that its outcome can't be explained based on a finite number of documented variables and logical branches. We can even think about having a standard to describe and document ML systems (and I would totally look that up because I have the right to know how my keyboard suggests words, why this search result that seems unusual is in fact a sponsored placement, or why this autonomous vehicle chose to run over the pedestrian so not to kill the baby on board...).

And this is not meant to hinder research and development in AI, but to protect people and businesses from potentially dangerous blackboxes, and bring back accountability and ethics in a field that knows very little of it.

> shouldn't be permitted to be so complex that its outcome can't be explained based on a finite number of documented variables and logical branches

There are plenty of effective pharmaceuticals for which we don’t entirely understand the mechanism (e.g. lithium). I think it’s a good goal to be able to understand mechanisms, but if you can effectively show that a solution works to solve a problem through methodical trials, we shouldn’t disallow it’s use.

The issue with Facebook is not complexity or black-box-ness. It’s that the goals of the company and how it makes a profit are fundamentally misaligned with a healthy society.

> I understand enough ML to know how

hmmmm

> We can even think about having a standard to describe and document ML systems (and I would totally look that up because I have the right to know how my keyboard suggests words, why this search result that seems unusual is in fact a sponsored placement, or why this autonomous vehicle chose to run over the pedestrian so not to kill the baby on board...).

Yea you know nothing about ML...

> a key component of your business, or one that affects many users, shouldn't be permitted to be so complex that its outcome can't be explained based on a finite number of documented variables and logical branches.

You don't think that any business should be able to use random forests, let alone anything neural network based?

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

> 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.

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.

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.

You do understand that there isn't just some "ball of ML" sitting on a server forming your feed, right? That's not how ML works at scale.

ML is applied to very specific tasks that are pipelined in an intentional and introspectable way.

And even at the level of a given model while you might struggle to explain behavior depending on how pedantically you define that, you still know what your reward function was.

This is like saying "There's no point in knowing the source code if you don't have the schematic and microcode for your CPU to know which transistors each line of code is changing the state of"

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.

> 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

What goes to the heart of the entire issue is whether recommendation systems, as used by social networks, are inferring "what people like". That's the whole problem.

When shown an outrage-inducing tweet are you being showing "what you like" or something quite different? Eg., what is most likey to addict you to the platform, what elicits the highest emotional response. Do people trapped gambling their lives away in casions "like" being there?

The idea that social networks are engaged in a sincere project to estimate people's preferences is on the face of it absurd.

And requires such superstitions as "revealed preferences" which are often little more than an observation that people do what their environment most easily affords.

To call that an exercise of preference, to say that people like it, is a great misunderstanding of human nature.

People are much better than what they happen to do in any given environment, and social media companies bare extreme levels of responsibility for creating environments of so-called "revealed preferences" where, apparently people want eating disorders, conspiracy theories, twitter rage sessions, and so on.

> What goes to the heart of the entire issue is whether recommendation systems, as used by social networks, are inferring "what people like". That's the whole problem.

It really doesn't. You're trying to debate the semantics of the keyword "like", but that discussion is meaningless and irrelevant. The only objective fact about these services is that they use the data provided by users to infer the preferences they reveal. These services then use this information to meet the company's goals, such as meeting the expectations of paying customers. These paying customers include marketing companies, and their expectations include driving up user engagement and associate their programs with specific type of content.

You might complain about whether these goals are aligned with what you believe are some user's goals, but you'd be missing the fact that users of services like Facebook or Twitter are the product and not the customers. You're not paying for that click, the marketing company is. If customers pay for that click then this clearly means these services do a good job determining what users "like".

> but you'd be missing the fact that users of services like Facebook or Twitter are the product and not the customers

So you agree that social media recommendation services are not optimising for situations their users like. So the conclusion of your disagreeable grandstanding is the one I offered in my initial comment.

> The only objective fact about these services is that ... infer the preferences they reveal.

They force them (/create environments) into choices where their actions are advantageous to an ad. company, yes.

The theory of "revealed preferences" is highly contested, and its highly unlikely any such things exist. People's apparent preferences are functions of their environments, and are largely created by them, not "revealed".

The entire tradition of neoclassical economics begins from a methodological premise that closed-form mathematical functions ought represent key terms of interest (eg., preferences). However that assumption is so laughably false it's ridiculous, all social systems -- including preferences --- have path dependence (hysteresis) and are parameterised on such a large number of variables that no such "formula" can be written.

So the least objective, most pseudoscientific description of a situation in which a person enters a casino and gambles their life away is that, somehow a magic casino machine is "revelling" the Preferences of The Human Mind.

In sum, my reply to the comment stands unaltered. Social media recommendation systems are not optimising for what their users like,.

> Facebook and Twitter support a feature where quite literally users specify in no ambiguous term that they like something.

It's not that simple. These features are also social. When you like a tweet on twitter, you send a notification to the author, and other people might see the tweet with something like "alice and bob liked this tweet". So you might decide to like or not to like a tweet for all these reasons.

Youtube doesn't consider likes at all in the algorithm, only watchtime, even though likes are private in youtube, because it's too vulnerable to manipulation, to people telling you to "like and subscribe", etc..

For ML models, publish the loss function and sampling of the data it’s trained on? For each of these objections, there exists some level that has been reasoned about at the conceptual level during the design.

And an army of observers will be happy to interpret for the less technical.

Ironic that you are discussing this in a thread about how orgs can get huge enough that nobody really understands why there are so many people.

It would most likely take several teams to push out what you are asking for. You’d need lawyers, support staff, engineers to create a platform other teams can hook into in order to fulfill these requests, you’d need ways to strip training data of any PII, you’d need a team to write the front end for support staff and the external users. Etc… that’s a non trivial amount of headcount!

I don't get how you can miss the forest for the trees this badly.

You're raving about how much work it would be to build in introspectability into a system that has an extremely large effect on the population, as if that's not justified by the fact we'd be gaining introspectability into a system that has an extremely large effect on the population

You're also overselling it, no one is saying we need every single api request to fetch a feed to also hit some endpoint stating "this is why post id X was shown to user Y". ML again, can be introspected at a higher level than all this seems to imply.

OpenAI can't tell you exactly why ChatGPT gives a specific string of text but they can (and have) shown how it works at a high level: https://openai.com/blog/instruction-following/

You seem to be pushing the line that, because it's difficult to explain why these systems work how they do, and the responsibility is spread so far and wide, we shouldn't bother. It's the literal opposite: it's exactly why its so needed. Even internally these things should be wanted, and I'd be truly shocked if finely tuned advertising giants don't already have a high degree of these understandings in poorly named suborgs that no one pays much attention to (not because cloak and daggers, but because engineers tend to focus on more glamourous things than "introspection").

An army of observers across the political spectrum arguing over the intent of a loss function.

Sounds like a conspiracy theorist's dream.

For sure, but that's going to happen in any case.

> 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.

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?

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

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.

> Also, as companies grow they start to feel the need to implement efficient development processes...

You have managed to make these all sound incredibly inefficient ;P.

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