Will GDPR Make Machine Learning Illegal?

https://www.kdnuggets.com/2018/03/gdpr-machine-learning-illegal.html

by Vaslo • 8 years ago

111 95 8 years ago

No. This is an absurd victim roleplay. People’s rights to control what happens to their information is not going to make machine learning illegal. It’ll just give all users a little more privacy and make selling their data a little less lucrative.

Besides, there’s been plenty of work into improving interpretibility. Stop pushing the myth that we don’t understand anything about neural networks and they’re the biggest, blackest box since Schwarzschild.

gnud2• 8 years ago

GDPR might make certain uses of machine learning more difficult.

A hypothetical insurance company that uses ML to decide who gets offered which rate, might run into trouble when a customer asked for "meaningful information" about the logic behind the decision.

But for the same insurance company, using ML to flag possible fraud cases, and then having a human review them, seems not to pose a problem. All decisions were made by humans, the machines just showed where a decision was needed.

Of course, IANAL.

disgruntledphd22• 8 years ago

Insurance companies are already prevented by law from using methods which cannot be explained, so they will be fine.

I actually think that most ML will be fine, as the outcomes are trivial. The particular set of results that Google shows you on Google.com are not a decision about you, and I can't see those needing to be interpreted.

Anything more consequential (i.e. job hiring) probably will, but also has laws that prevent the use of indiscriminate data already.

So, I don't see this an issue.

bitL• 8 years ago

Would publishing a deep learning model on GitHub satisfy that requirement? You want to know how it makes decisions? Here you go!

hn_throwaway_99• 8 years ago

What's that saying about "The answer to any headline that is posed as a yes-or-no question is 'no'."

simcop2387• 8 years ago

https://en.wikipedia.org/wiki/Betteridge%27s_law_of_headline...

mitchty• 8 years ago

Betteridge's law of headlines I believe.

https://en.wikipedia.org/wiki/Betteridge%27s_law_of_headline...

reificator• 8 years ago

As per tradition: No.

https://news.ycombinator.com/item?id=9232419

matthewmacleod• 8 years ago

No.

It may make it illegal, via the aggregate "right to explanation", to make decisions about people solely on the basis of machine learning models. That is, "we fed some data into a computer and it said 'no', so too bad" is not going to be a valid explanation.

That sounds like a fucking amazing outcome to me.

John_KZ• 8 years ago

But unfortunately it's very easy to find a way around it: Make all decisions based on ML models, and only when a customer manages to go through the bureaucratic, expensive process of requesting an explanation, have a human review the case and come up with a plausible reason.

It's definitely a whole lot better than not having that law, but we're not quite there yet.

protomyth5• 8 years ago

only when a customer manages to go through the bureaucratic, expensive process of requesting an explanation, have a human review the case and come up with a plausible reason.

That doesn't sound exactly legal as the actual reason had to exist at the time of the decision and an after the fact reason would not be the truth.

Don't the ML models log why they made decisions in the first place?

gpm2• 8 years ago

That sounds a lot like fraud to me... are you sure this would be at all legal?

(Alternatively is your argument "but companies might blatantly and intentionally break the law"?)

maksimum2• 8 years ago

You're right, additional properties are desirable. The ACM has recently published a [set of guidelines](https://www.acm.org/binaries/content/assets/public-policy/20...) for algorithmic decision making. "Access" and "Auditability" are properties that mean it shouldn't be difficult to get the explanation, and that it shouldn't be possible to 'bullshit' an explanation afterwards.

singron• 8 years ago

Regardless of the whatever the GDPR actually says and even if ML is a complete black box, I would be very happy at least knowing my input features to ML algorithms.

e.g. If I'm denied a loan, I want to learn the various "facts" about me that led to the ML coming up with the denial. Then I can fact-check it (e.g. "actually I don't already have $X of debt") and come up with an appeal.

maxxxxx• 8 years ago

Agreed. We are building up this level of faceless bureaucracy the more automated algorithms with multiple inputs we are using.

I know someone who had to deal with identity theft and it's a sheer nightmare to figure out why you get denied a rental car or airplane boarding. The people facing the customer know that the person is blocked but don't know why and have no way to figure out why.

ThePhysicist4• 8 years ago

Personally, I think many people and organizations currently exaggerate the possible changes and risks (for companies) brought on by the GDPR, often with the goal to instill fear (and generate business) or get press coverage.

The relevant article (https://gdpr-info.eu/art-22-gdpr/) clearly states that automated decision making is allowed under the GDPR, it just gives the data subject the right to demand a manual assessment of that decision. In practice, this means that you're absolutely free to use any kind of automated algorithm (AI-based or not) to make decisions on individuals, but if people complain and demand an explanation you need to review the decision manually.

Addendum: If you want to learn more about the motivations behind article 22 as well as articles 13 & 14 (that concern the transparency requirements), I highly recommend the official document of the Article 29 working party:

http://ec.europa.eu/newsroom/article29/item-detail.cfm?item_...

dcd0101• 8 years ago

It sounds like they're suggesting that, assuming those rights exist in these situations (they claim there's ambiguity there), that there are big questions around what qualifies as an acceptable explanation.

Did I misunderstand?

ThePhysicist4• 8 years ago

Yes, but reading the official guidelines should clarify this ([1], p. 14 ff.):

The controller should find simple ways to tell the data subject about the rationale behind, or the criteria relied on in reaching the decision without necessarily always attempting a complex explanation of the algorithms used or disclosure of the full algorithm. The information provided should, however, be meaningful to the data subject.

There's also a detailed example in the document that should make it clearer what kind of explanation is required (and what is not required). Again, personally I understand that it's not clear what exactly will be required here, but titling an article "Will GDPR Make Machine Learning Illegal?" is just an attempt to garner attention by instilling (unfounded) fear.

[1] http://ec.europa.eu/newsroom/just/document.cfm?doc_id=47963

montrose2• 8 years ago

If you have to be able to review any decision manually that people complain about, that is significantly limiting. It means you can only write software to do things humans can do.

I don't think it's alarmist to worry about the GDPR. It's an extremely far-reaching law covering types of technology that are changing very rapidly. Our default reaction should be skepticism, because the default outcome of that combination is disaster.

annabellish• 8 years ago

I don't think that's quite correct, many (most?) kinds of AI achieve things which are difficult or impossible for humans to do, but that doesn't mean you can't figure out why a decision was made afterwards.

There are some scenarios where that isn't possible, but... if nobody knows why decisions impacting people's lives are made and it isn't possible to determine what they were done for, is that something we really want?

ThePhysicist4• 8 years ago

As you said it's often not possible to explain exactly why an AI-based algorithm took a given decision (if it were we wouldn't need such a complicated algorithm in the first place), but this isn't necessary either. What you need to explain to the data subject is the purpose of the automated decision making, the data you use for it and the basic logic structure of your algorithm. Again, I can only recommend to read the guidelines issued by the Article 29 working party, as they will guide the implementation and enforcement of the regulation in the member states. An overview can be found here (the relevant document is "wp251"):

http://ec.europa.eu/newsroom/just/item-detail.cfm?item_id=50...

gaius3• 8 years ago

If you have to be able to review any decision manually that people complain about, that is significantly limiting. It means you can only write software to do things humans can do

If you create an algorithm that by pure coincidence happens to discriminate against a protected class then any judge will take a dim view of you claiming to be unable to explain it.

I’d like to encourage everyone, even if just for fun, to insist on an explanation for every algorithmic decision they may be subjected to

cf498• 8 years ago

You forget to mention, that this isnt a regulation covering a technology. Its about how a specific technology is utilised in a very specific use case.

The entire debate is, like the name suggests, about the regulations of how personal information can be used in an economic context. Unsurprisingly, some of these use cases are illegal now and existed in a loophole before.

To simplyfy, framing a question this way is similar to asking if rights of gun ownership are infringed by outlawing shooting people.

mnm12• 8 years ago

You can develop whatever you want, just don't subject people to arbitrary binding decisions without appeal. The default attitude is one of skepticism, skepticism towards technology that could potentially affect human lives in very nasty ways. If you want your technology adopted instead of banned, it might be a good idea to listen to the people who will ultimately have to deal with the consequences of such technology. Otherwise, why should society adopt a technology that they cannot understand, that possibly no one understands or can explain or will explain? How can society trust you as the creator of such technology when it's a black box?

raverbashing• 8 years ago

to review any decision manually that people complain about, that is significantly limiting. It means you can only write software to do things humans can do

So are you happy with a black box that just says "computer says no" without any explanation or way to review this decision?

"Sorry, your insurance premium is now 300EUR higher than next year, computer says so"

s73v3r_• 8 years ago

I’m not seeing a problem. There should never be a situation where a human cannot review the decision and reverse it.

Someone• 8 years ago

”It means you can only write software to do things humans can do.”

For the things computers can do, they typically are way faster and way more reliable, but I don’t see how there could be anything software can do that humans can’t (quantum computing may change that)

Also, being able to explain what the computer did doesn’t imply being able to do it.

As a (silly) example, “We hashed the c.v. you sent us, getting a 150-digit number N. The N-th digit in the hexadecimal expansion of pi isn’t a zero, so you didn’t get invited for an interview” would be a perfectly good explanation of your hiring practices, even if you don’t know how to compute any of those decimals.

akshatpradhan• 8 years ago

It's in your best interest to reduce your risk by going through a De-identification process for data collected:

De-identification is adopted as one of the main approaches of data privacy protection. It is commonly used in fields of communications, multimedia, biometrics, big data, cloud computing, data mining, internet, social networks and audio–video surveillance.

https://en.wikipedia.org/wiki/De-identification

polskibus• 8 years ago

I have a related question - can EU citizen under GDPR demand that a company "unlearns" all of their machine learning models from his/her data? I'm not asking if this is technically feasible (you could do that by removing old model, removing user data from training set and then reruning learning). I'm asking whether GDPR will give citizens power to demadn that.

amarkov3• 8 years ago

GDPR only requires erasure of personal data on request, where "personal data" is defined as information relating to some identified or identifiable person. Machine learning models trained on personal data aren't going to be personal data themselves.

yxhuvud• 8 years ago

Well, unless they can be used to identify the person.

Asdfbla• 8 years ago

Machine learning models leaking private information is a real problem though, unless mechanisms to ensure something like differential privacy are applied. Those approaches may still be too theoretical and hard to reason about for many machine learning practitioners. Will be interesting how that plays out.

gaius3• 8 years ago

Serious question: is there a web based application of ML that isn’t about exploiting or deceiving customers/users?

ML has its legit uses sure, but it always seems to be used to outwit people in practice

amarkov3• 8 years ago

Well, I mean, what counts as exploitation? If I learn a lot about what shoes people like in my job as a shoe salesman, and I use that experience when new people come in to make recommendations, I think everyone would call that just good customer service. But when Amazon sets up an ML model to do the same thing, many people argue they're exploiting your personal data for the sake of profit.

Off the top of my head, I can't think of a widespread web-based ML application that some people don't consider exploitative. But is that a property of the ML or the people?

gaius3• 8 years ago

I’d argue that “people who bought X also bought Y” is too simplistic to be considered ML. But say, charging a little extra for X to people who previously bought Y would be exploitative.

cimmanom• 8 years ago

Sure. How about the basic e-commerce use case of "based on your interest in product X, you may also be interested in product Y"?

stupidcar• 8 years ago

Catching fake / spamming / abusive users on social media.

People are (rightfully) upset by the amount of spam and harassment happening on platforms like Twitter. Machine learning is a good way to tackle this problem at scale, in an automated way, by training models to spot and auto-moderate bad posts.

Under GDPR, anyone can demand an explanation for why they were marked as spam / abusive. This will allow trolls and spammers to improve their workarounds, by demanding an explanation for why their posts were blocked, then tailoring their later posts to avoid these signifiers. It will also let them impose a tax on platforms that try to block them by flooding them with demands for explanations under GDPR.

drngdds• 8 years ago

Spotify uses ML to give music recommendations: https://hackernoon.com/spotifys-discover-weekly-how-machine-...

tzs• 8 years ago

In the case of things like being rejected by a machine learning algorithm for a mortgage, it should be pretty easy to automate coming up with an explanation for the applicant.

Run the application again with $5k higher income. Keep raising income by $5k until you find an income level that results in approval. If that income level is reasonable for people in general who were approved for similar mortgage amounts and whose other inputs are in the same ballpark as the rejected application, tell the applicant the rejection was for income too low.

You can do a similar thing with other parameters, such as credit rating, length of employment, and whatever else is input to your algorithm that is under the applicant's control. In some cases you might not be able to find a good single parameter tweak for approval and will have to resort to combinations. There will usually be many different ways then to tweak for approval. Use comparisons to similar applications that were approved to pick a reasonable combination of tweaks to base the rejection explanation on.

paulddraper• 8 years ago

The only reason I could see this wouldn't work is your explanation had to be "good" one.

E.g. if you lived 10 miles to the west, had the same income, had a mortgage before 2005, and didn't apply for credit between Sept 2010 and June 2011, then you would have been accepted. (There are other situations that would have resulted in acceptance; this is just one.)

petercooper2• 8 years ago

Rather than explaining how the machine learning system works as an "explanation", perhaps auditing could be added to machine learning algorithms so that you very much could produce a very long but accurate description of the process, a la pages full of "X was compared to Y, X was larger, and thus we move on to step 261". A bit like disassembling machine code.

Of course, in machine learning, the datasets backing up the comparisons could not be shared as they contain variations of confidential and personal data, but you might still end up with a legally tolerable record of the algorithmic steps involved in the decision making process even if they're not useful to see.

piqalq• 8 years ago

I see what you’re getting at, but this would be a very lengthy and arduous process. Not to mention, many ML and DL algorithms are incredibly mathematically complex that describing them with literal step-by-step detail sounds like hell.

petercooper2• 8 years ago

It would be if you had to be involved, but I'm suggesting algorithms could have some sort of instrumentation so such "explanations" could be automatically generated and thrown into a data warehouse for possible future use. (This is all a cynical attempt to meet legal requirements rather than anything actually useful for the user, of course.)

JepZ• 8 years ago

Nobody else has a problem with the mix up of 'Machine Learning' and 'Deep Learning' here?

Last time I heard of those terms, 'Machine Learning' was a much broader category than 'Deep Learning'. Its like someone says 'Next years Porsches will be illegal' and the next person calls 'Next year cars will be illegal'?!?

nmca• 8 years ago

For those interested in the cutting edge of interpretability, check out https://github.com/marcotcr/anchor I'm pretty sure this sort of approach will be how most businesses handle the problem.

emilfihlman2• 8 years ago

I find it extremely concerning that people, especially on this platform, don't care about what the law says and choose to just hand wave it as "it doesn't apply to us / they don't enforce it like that".

The letter of the law is even more important than the spirit of it because it allows governments, corporations and other entities to simply say "this is what the law says" and because you are small you don't have the money/power to challenge this rendition if the spirit of the law disagrees with them. Not to mention that laws are _laws_, they are not meant to be really interpreted (it is a byproduct of having to do so to have a functioning society).

It's an extremely slippery slope to not care what the law says. If we don't we'll have some dystopian laws that allow for anything quite soon.

Will GDPR Make Machine Learning Illegal?

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