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Comment by spacemanspiff01 | Hacker News Reader

From the laws text:

For the purposes of this Regulation, the following definitions apply:

(1) ‘AI system’ means a machine-based system that is designed to operate with varying levels of autonomy and that may exhibit adaptiveness after deployment, and that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments; Related: Recital 12

https://artificialintelligenceact.eu/article/3/ https://artificialintelligenceact.eu/recital/12/ So, it seems like yes, software, if it is non-deterministic enough would qualify. My impression is that software that simply takes "if your income is below this threshold, we deny you a credit card." Would be fine, but somewhere along the line when your decision tree grows large enough, it probably changes.

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Notably, Recital 12 says the definition "should not cover systems that are based on the rules defined solely by natural persons to automatically execute operations."

https://uk.practicallaw.thomsonreuters.com/Glossary/UKPracti... describes a bit of how recitals interact with the operating law; they're explicitly used for disambiguation.

So your hip new AI startup that's actually just hand-written regexes under the hood is likely safe for now!

(Not a lawyer, this is neither legal advice nor startup advice.)

It doesn't seem to be clear to me whether auto-formatted code (or even generated code from copilot for example) would be classified as AI.

unless your AI generated code gets deployed into production without any human supervision/approval, probably not.

It seems to me the key phrase in that definition is "that may exhibit adaptiveness after deployment" - If your code doesn't change its own operation without needing to be redeployed, it's not AI under this definition. If adaptation requires deployment, such as pushing a new version, that's not AI.

I'm not sure what they intended this to apply to. LLM based systems don't change their own operation (at least, not more so than anything with a database).

We'll probably have to wait until they fine someone a zillion dollars to figure out what they actually meant.

It's as if the person who wrote it, their entire understanding of AI is based solely on depictions from science fiction.

I'm pretty sure that was not the case.

For LLMs we have "for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments".

For either option you can trace the intention of the definitions to "was it a human coding the decision or not". Did a human decide the branches of the literal or figurative "if"?

The distinction is accountability. Determining whether a human decided the outcome, or it was decided by an obscure black box where data is algebraically twisted and turned in a way no human can fully predict today.

Legally that accountability makes all the difference. It's why companies scurry to use AI for all the crap they want to wash their hands of. "Unacceptable risk AI" will probably simply mean "AI where no human accepted the risk", and with it the legal repercussions for the AI's output.

This would be an excellent outcome (and probably the one intended).

> We'll probably have to wait until they fine someone a zillion dollars to figure out what they actually meant.

In reality, we will wait until someone violates the obvious spirit of this so egregiously and ignore multiple warnings to that end and wind up in court (a la the GDPR suits). This seems pretty clear.

It's unclear whether "may" means "can (and does)" or whether it renders that entire clause optional.

I've personally reviewed all 7b parameters of my model and they won't adapt after deployment :)

That means you can answer the question whether they comply with the relevant law in the necessary jurisdiction and can prove that to the regulator. That should be easy, right? If it's not, maybe it's better to use two regexps instead.

The model says yes.

Off to the model jail it goes.

I understand that phrase to have the opposite meaning: Something _can_ adapt its behavior after deployment and still be considered AI under the definition. Of course this aspect is well known as online learning in machine learning terminology.

No offense but this is a good demonstration of a common mistake tech people (especially those used to common law systems like the US) engage in when looking at laws (especially in civil law systems like much of the rest of the world): you're thinking of technicalities, not intent.

If you use Copilot to generate code by essentially just letting it autocomplete the entire code base with little supervision, yeah, sure, that might maybe fall under this law somehow.

If you use Copilot like you would use autocomplete, i.e. by letting it fill in some sections but making step-by-step decisions about whether the code reflects your intent or not, it's not functionally different from having written that code by hand as far as this law is concerned.

But looking at these two options, nobody actually does the first one and then just leaves it at that. Letting an LLM generate code and then shipping it without having a human first reason about and verify it is not by itself a useful or complete process. It's far more likely this is just a part of a process that uses acceptance tests to verify the code and then feeds the results back into the system to generate new code and so on. But if you include this context, it's pretty obvious that this indeed would describe an "AI system" and the fact there's generated code involved is just a red herring.

So no, your gotcha doesn't work. You didn't find a loophole (or anti-loophole?) that brings down the entire legal system.

> Notably, Recital 12 says the definition "should not cover systems that are based on the rules defined solely by natural persons to automatically execute operations."

That's every AI system. It follows the rules defined solely by the programmers (who I suppose might sometimes stretch the definition of natural persons) who made pytorch or whatever framework.

It means a decision tree where every decision node is entered by humans is not an AI system, but an unsupervised random forest is. It’s not difficult to see the correct interpretation.

In strict mathematical reading, maybe - depends on how you define "rules", "defined" and "solely" :P. Fortunately, legal language is more straightforward like than that.

The obvious straightforward read is along the lines of: imagine you make some software, which then does something bad, and you end up in court defending yourself with an argument along the lines of, "I didn't explicitly make it do it, this behavior was a possible outcome (i.e. not a bug) but wasn't something we intended or could've reasonably predicted" -- if that argument has a chance of holding water, then the system in question does not fall under the exception your quoted.

The overall point seems to be to make sure systems that can cause harm always have humans that can be held accountable. Software where it's possible to trace the bad outcome back to specific decisions made by specific people who should've known better is OK. Software that's adaptive to the point it can do harm "on its own" and leaves no one but "the system" to blame is not allowed in those applications.

If the thinking machine rejects my mortgage application, it should be possible to point out which exact rule triggered the rejection. With rules explicitly set by an operator it's possible. It's also possible to say that the rules in place comply with the law and stay compliant during the operation, for example it doesn't unintentionally guess that I'm having another citizenship based on my surname or postal code.

If the mortgage application evaluation system is deterministic so that the same input always produces the same output then it is easy to answer "Why was my application rejected?".

Just rerun the application with higher income until you get a pass. Then tell the person their application was rejected because income was not at least whatever that passing income amount was.

Maybe also vary some other inputs to see if it is possible to get a pass without raising income as much, and add to the explanation that they could lower the income needed by say getting a higher credit score or lowering your outstanding debt or not changing jobs as often or whatever.

That tells you how sensitive the model is along its decision boundary to permutations in the input -- but it isnt a relevant kind of reason why the application was rejected, since this decision boundary wasnt crafted by any person. We're here looking for programs which express prior normative reasoning (eg., "you should get a loan if...") -- whereas this decision boundary expresses no prior normative reason.

It is simply that, eg., "on some historical dataset, this boundary most relibaly predicted default" -- but this confers no normative reason to accept or reject any individual application (cf. the ecological fallacy). And so, in a very literal way, there is no normative reason the operator of this model has in accepting/rejecting any individual application.

> If the mortgage application evaluation system is deterministic so that the same input always produces the same output then it is easy to answer "Why was my application rejected?".

But banks, at least in my country (central EU), don't have to explain their reasons for rejecting a mortgage application. So why would their automated systems have to?

In the US they do have to explain. It's a requirement of the Equal Credit Opportunity Act of 1974 [1]. Here is an article with more detail on what is required in the explanation [2].

[1] https://en.wikipedia.org/wiki/Equal_Credit_Opportunity_Act#R...

[2] https://www.nolo.com/legal-encyclopedia/do-lenders-have-to-t...

They don't have to explain to the applicant. They do have to explain to the regulator how exactly they stay compliant with the law.

There is so called three line system -- operational line does the actual thing (approves or rejects the mortgage), the second line gives the operational line the manual to do so the right way, the internal audit should keep an eye that whatever the first line is doing is actually what the policy says they should be doing.

It's entirely plausable that operational line is an actual LLM which is trained on a policy that the compliance department drafted and the audit department occasionally checks the outputs of the modal against the policy.

But at this point it's much easier to use LLM to write deterministic function in your favorite lisp based on the policy and run that to make decisions.

>Just rerun the application with higher income until you get a pass. Then tell the person their application was rejected because income was not at least whatever that passing income amount was.

Why do you need an AI if what you are doing is "if X < N" ?

It would not be just an "if X < N". Those decisions are going to depend on a lot of variables besides income such as credit history, assets, employment history, debts, and more.

For someone with a great credit history, lots of assets, a long term job in a stable position, and low debt they might be approved with a lower income than someone with a poor credit history whose income comes from a job in a volatile field.

There might be some absolute requirements, such as the person have a certain minimum income independent of all those other factors, and they they have a certain minimum credit score, and so on. If the application is rejected because it doesn't meet one of those then sure, you can just do a simple check and report that.

But most applications will be above the absolute minimums in all parameters and the rejection is because some more complicated function of all the criteria didn't meet the requirements.

But you can't just tell the person "We put all your numbers into this black box and it said 'no'. You have to give them specific reasons their application was rejected.

Doesnt all this contradict what I initially replyed to?

I don't see any contradiction.

Say a lender has used machine learning to train some sort of black box to take in loan applications and respond with an approve/reject response. If they reject an application using that the Equal Credit Opportunity Act in the US require that they tell the applicant a specific reason for the rejection. They can't just say "our machine learning model said no".

If there were not using any kind of machine learning system they probably would have made the decision according to some series of rules, like "modified income must be X times the monthly payment on the loan", where modified income is the person's monthly income with adjustments for various things. Adjustments might be multipliers based on credit score, debt, and other things.

With that kind of system they would be able to tell you specifically why your were rejected. Say you need a modified income of $75k and you are a little short. They could look at their rules and figure out that you could get a modified income of $75k if you raised your income by a specific amount or lowered your debt by a specific amount, or by some combination of those.

That kind of feedback is useful to the application. It tells them specific things they can do to improve their chances.

With the company using a machine learning black box they don't know the rules that the machine has learned. Hence my suggestion of asking the black box what-if scenarios to figure out specific things the applicant can change to get approval.

>That kind of feedback is useful to the application. It tells them specific things they can do to improve their chances.

In that sense it's very practical, but it kicks the can down the road. Maybe the thing has a hidden parameter that represents the risk of the applicant being fired, which increases the threshold by 5% if the salary is a round number. Or it is more likely to reject everyone with an income between 73 and 75k because it learned this is a proxy to a parameter you are explicitly forbidden to have.

Let's just say it doesn't have a discontinuity, and actually produces the threshold which is deterministically compared with your income. How does it come up with this threshold? You may not be required to disclosed that to the applicant, but it would be a shame if people will figure out that the threshold is consistently higher for a certain population (for example people who's given name ends with a vowel).

It's fairly reasonable to for a regulator to ask you to demonstrate it doesn't do any of this things.

Sure, it's the rule that multiplies all the weights of matrix C1, with your transformed inputs. It's right there. What part of that rule don't you understand?

Seems very reasonable. Not all software has the same risk profile, and autonomous+adaptive software certainly have a more dangerous profile than simpler software, and should be regulated differently.

What? Why? Shouldn't those same use cases all be banned regardless of what tech is used to build them?

Using a machine, instrument or technology for some intended outcome will nevertheless have a distribution of outcomes. Some good some bad. A kitchen knife will usually cut food, but will occasionally cut your finger. If maliciously used the bad outcomes become a lot more.

Two different machines can be designed for the same use case, but the possible bad outcomes in either "correct" use or malicious use of the two machines can be very different. So it is reasonable to ban the one which has unacceptable bad outcomes.

For example, while both a bicycle and a dirt bike are mobility vehicles, a park may allow one and ban the other.

Not necessarily. Interpretability of a system used to make decisions is more important in some contexts than others. For example, a black box AI used to make judiciary decisions would completely remove transparency from a system that requires careful oversight. It seems to me that the intent of the legislation is to avoid such cases from popping up, so that people can contest decisions made that would have a material impact on them, and that organisations can provide traceable reasoning.

Is a black box AI system less transparent than 12 jurors? It would seem anytime the system is human judgement, an AI system would be as transparent (or nearly so).

It would seem accountable would only be higher in systems where humans were not part of the decision making process.

I mean on the one hand I agree, none of these use cases seem legitimate, and most give off very totalitarian vibes.

However for those that might not be purely 1984 inspired, I do think that we need to have legislation that is capable of making the distinction between : - algorithms that can be reasoned about and analysed - "AI" systems that resist such analysis

The main issue is around responsability. Who would be held responsible for illegal (discriminatory) biases in an AI systems ? How would regulators even detect, specify and quantify those biases ?

In non-AI systems, we can analyse the algorithm and evaluate if the biases are due to errors (negligence) or are by design (malice / large scale criminality)

> and that may exhibit adaptiveness after deployment

So if an AI can't change its weights after deployment, it's not really an AI? That doesn't make sense.

As for the other criteria, they're so vague I think a thermostat might apply.

> they're so vague I think a thermostat might apply

Quite.

One wonders if the people who came up with this have any actual understanding of the technology they're attempting to regulate.

> As for the other criteria, they're so vague I think a thermostat might apply.

As long as the thermostat doesn't control people's lives, that's fine.

It _may_ exhibit adaptiveness after deployment, which would not change it being AI. I think that is the right reading of the definition.

Keyword 'may'.

A learning thermostat would apply, say one that uses historical records to predict changes in temperature and preemptively adjusts. And it would be low risk and unregulated in most cases. But attach to a self-heating crib or premature baby incubator and that would jump to high risk and you might have to prove it is safe.

So if the thermostat jumps to 105 during the night, that's not considered 'high-risk?'

Maybe you are right and it is still risky for sleeping adults. In any case, even high risk the standard that needs to be followed might be as simple as 'must have a physical cutoff at 30C'.

Hm, not a too bad definition. Seems like written by some people who know what machine learning is.

Good. You cannot have a functioning society where decisions are made in a non-deterministic way. Especially when those decision deviate from agreed protocols (laws, bylaws, contracts, etc.).

So no more predicting the weather with sensors?

Which of the criteria of the risk levels that will be regulated which are listed in the article do you think would include weather predictions?

something like a long-term weather forecast could lead to declining to issue an insurance policy for someone's home or automobile in an area. it could significantly impact the price. make it publicly available, and an insurance company could use that prediction. clearly an unacceptable risk

Do you use biometric data to predict the weather?

I trained a CV model to tell me the temp when I stand in front of the mirror based on if my nipples are hard or not, and how hard or soft they are.

if you set up your thermostat to respond to the output from your model you could get it to turn up the temperature by pinching your nipples

Too bad the EU will never experience such innovation

That's fucking brilliant!

Ah, it detects if it’s a bit nippy? ;)

I like your thinking B)

I have a project which uses weather info to predict avalanche risk. Reading the articles its hard for me to understand whether this would apply or not but my feeling is it might (If I ever need to run this in the EU I would talk to a lawyer). https://openavalancheproject.org

As it should; people would use it to make life-or-death decisions, so that application is high-risk.

We already have ways to predict avalanche risk that are well understood and explainable. There should be a high threshold on replacing that.

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