This week on my podcast, I read my recent @medium column, "How To Think About #Scraping: In #privacy and #labor fights, copyright is a clumsy tool at best," which proposes ways to retain the benefits of scraping without the privacy and labor harms that sometimes accompany it:
If you'd like an essay-formatted version of this thread to read or share, here's a link to it on pluralistic.net, my surveillance-free, ad-free, tracker-free blog:
What are those benefits from scraping? Well, take #ComputationalLinguistics, a relatively new discipline that is producing the first accounts of how informal language works. Historically, linguists overstudied written language (because it was easy to analyze) and underanalyzed speech (because you had to record speakers and then get grad students to transcribe their dialog).
The thing is, very few of us produce formal, written work, whereas we all engage in casual dialog. But then the internet came along, and for the first time, we had a species of mass-scale, informal dialog that also written, and which was born in machine-readable form.
This ushered in a new era in linguistic study, one that is enthusiastically analyzing and codifying the rules of informal speech, the spread of vernacular, and the regional, racial and class markers of different kinds of speech:
The people whose speech is scraped and analyzed this way are often unreachable (anonymous or pseudonymous) or impractical to reach (because there's millions of them).
The linguists who study this speech will go through #InstitutionalReviewBoard approvals to make sure that as they produce aggregate accounts of speech, they don't compromise the privacy or integrity of their subjects.
Computational linguistics is an unalloyed good, and while the speakers whose words are scraped to produce the raw material that these scholars study, they probably wouldn't object, either.
But what about entities that explicitly object to being scraped? Sometimes, it's good to scrape them, too.
Since 1996, the @internetarchive has scraped every website it could find, storing snapshots of every page it found in a giant, searchable database called #TheWaybackMachine. Many of us have used the Wayback Machine to retrieve some long-deleted text, sound, image or video from the internet's memory hole.
For the most part, the Internet Archive limits its scraping to websites that permit it. The #RobotsExclusionProtocol (AKA #robots.txt) makes it easy for webmasters to tell different kinds of crawlers whether or not they are welcome. If your site has a robots.txt file that tells the Archive's crawler to buzz off, it'll go elsewhere.
Since 2017, the Archive has started ignoring robots.txt files for news services; whether or not the news site wants to be crawled, the Archive crawls it and makes copies of the different versions of the articles the site publishes. That's because news sites - even the so-called "paper of record" - have a nasty habit of making sweeping edits to published material without noting it.
I'm not talking about fixing a typo or a formatting error: I'm talking about making a massive change to a piece, one that completely reverses its meaning, and pretending that it was that way all along:
This is canonical #FairUse, the kind of copying that almost always involves overriding the objections of the site's proprietor. Not all adversarial scraping is good, but this sure is.
There's an argument that scraping the news-sites without permission might piss them off, but it doesn't bring them any real harm. But even when scraping harms the scrapee, it is sometimes legitimate - and necessary.
Austrian technologist @badlogic used the #API from country's super-concentrated grocery giants to prove that they were colluding to rig prices. By assembling a longitudinal data-set, Zechner exposed the raft of dirty tricks the grocers used to rip off the people of Austria.
From #shrinkflation to deceptive price-cycling that disguised price hikes as discounts:
Zechner feared publishing his results at first. The companies whose thefts he'd discovered have enormous power and whole kennelsful of vicious attack-lawyers they can sic on him. But he eventually got the Austrian competition bureaucracy interested in his work, and they published a report that validated his claims and praised his work:
Zechner's on a roll, but the grocery cartel could shut him down with a keystroke, simply by blocking his API access. If they do, Zechner could switch to scraping their sites - but only if he can be protected from legal liability for nonconsensually scraping commercially sensitive data in a way that undermines the profits of a powerful corporation.
Zechner's work comes at a crucial time, as grocers around the world turn the screws on both their suppliers and their customers, disguising their #greedflation as #inflation. In Canada, the grocery cartel - led by the guillotine-friendly hereditary grocery monopolilst #GalenWeston - pulled the most Les Mis-ass caper imaginable when they illegally conspired to rig the price of bread:
We should scrape all of these looting bastards, even though it will harm their economic interests. We should scrape them because it will harm their economic interests. Scrape 'em and scrape 'em and scrape 'em.
Now, it's one thing to scrape text for scholarly purposes, or for journalistic accountability, or to uncover criminal corporate conspiracies. But what about scraping to train a #LargeLanguageModel?
Yes, there are socially beneficial - even vital - uses for #LLMs.
HRDAG's latest project is its most ambitious yet. Working with partner org #Dejusticia, they've just released the largest data-set in human rights history:
What's in that dataset? It's a merger and analysis of more than 100 databases of killings, #ChildSoldier recruitments and other crimes during the Colombian civil war.
Using a LLM, HRDAG was able to produce an analysis of each killing in each database, estimating the probability that it appeared in more than one database, and the probability that it was carried out by a right-wing militia, by government forces, or by FARC guerrillas.
This work forms the core of ongoing Colombian Truth and Reconciliation proceedings, and has been instrumental in demonstrating that the majority of war crimes were carried out by right-wing militias who operated with the direction and knowledge of the richest, most powerful people in the country. It also showed that the majority of child soldier recruitment was carried out by these CIA-backed, US-funded militias.
This is important work, and it was carried out at a scale and with a precision that would have been impossible without an LLM. As with all of HRDAG's work, this report and the subsequent testimony draw on cutting-edge statistical techniques and skilled science communication to bring technical rigor to some of the most important justice questions in our world.
LLMs need large bodies of text to train them - text that, inevitably, is scraped.
Scraping to produce LLMs isn't intrinsically harmful, and neither are LLMs. Admittedly, nonprofits using LLMs to build war crimes databases do not justify even 0.0001% of the valuations that AI hypesters ascribe to the field, but that's their problem.
Scraping is good, sometimes - even when it's done against the wishes of the scraped, even when it harms their interests, and even when it's used to train an LLM.
Scraping to violate peoples' privacy is very bad. Take #ClearviewAI, the grifty, sleazy #FacialRecognition company that scraped billions of photos in order to train a system that they sell to cops, corporations and authoritarian governments:
Likewise: scraping to alienate creative workers' labor is very bad. Creators' bosses are ferociously committed to firing us all and replacing us with "generative AI."
Like all self-declared "job creators," they constantly fantasize about destroying all of our jobs. Like all capitalists, they hate capitalism, and dream of earning rents from owning things, not from doing things.
The work these AI tools sucks, but that doesn't mean our bosses won't try to fire us and replace us with them.
After all, prompting an LLM may produce bad wrting, but at least the LLM doesn't give you lip when you order to it give you "ET but the hero is a dog, and there's a love story in the second act and a big shootout in the climax." Studio execs already talk to screenwriters like they're LLMs.
That's true of art directors, newspaper owners, and all the other job-destroyers who can't believe that creative workers want to have a say in the work they do - and worse, get paid for it.
So how do we resolve these conundra? After all, the people who scrape in disgusting, depraved ways insist that we have to take the good with the bad. If you want accountability for newspaper sites, you have to tolerate facial recognition, too.
When critics of these companies repeat these claims, they are doing the companies' work for them. It's not true. There's no reason we couldn't permit scraping for one purpose and ban it for another.
The problem comes when you try to use #copyright to manage this nuance. Copyright is a terrible tool for sorting out these uses; the limitations and exceptions to copyright (like fair use) are broad and varied, but so "fact intensive" that it's nearly impossible to say whether a use is or isn't fair before you've gone to court to defend it.
But copyright has become the de facto regulatory default for the internet.
When I found someone impersonating me on a dating site and luring people out to dates, the site advised me to make a copyright claim over the profile photo - that was their only tool for dealing with this potentially dangerous behavior.
The reasons that copyright has become our default tool for solving every internet problem are complex and historically contingent, but one important point here is that copyright is alienable, which means you can bargain it away.
For that reason, corporations love copyright, because it means that they can force people who have less power than the company to sign away their copyrights.
This is how we got to a place where, after 40 years of expanding copyright (scope, duration, penalties), we have an entertainment sector that's larger and more profitable than ever, even as creative workers' share of the revenues their copyrights generate has fallen, both proportionally and in real terms.
As #RebeccaGiblin and I write in our book #ChokepointCapitalism, in a market with five giant publishers, four studios, three labels, two app platforms and one ebook/audiobook company, giving creative workers more copyright is like giving your bullied kid extra lunch money. The more money you give that kid, the more money the bullies will take:
Many creative workers are suing the AI companies for copyright infringement for scraping their data and using it to train a model. If those cases go to trial, it's likely the creators will lose. The questions of whether making temporary copies or subjecting them to mathematical analysis infringe copyright are well-settled:
I'm pretty sure that the lawyers who organized these cases know this, and they're betting that the AI companies did so much sleazy shit while scraping that they'll settle rather than go to court and have it all come out. Which is fine - I relish the thought of hundreds of millions in investor capital being transferred from these giant AI companies to creative workers. But it doesn't actually solve the problem.
That's already happening: voice actors who record for video games are required to start their sessions by stating that they assign the rights to use their voice to train a #deepfake model:
But that doesn't mean we have to let the hyperconcentrated entertainment sector alienate creative workers from their labor. As the #WGA has shown us, creative workers aren't just LLCs with MFAs, bargaining business-to-business with corporations - they're workers:
Because if we do end up changing copyright law - or the daily practice of the copyright sector - to create exclusive rights over scraping and training, it's not going to get creators paid. If we give individual creators new rights to bargain with, we're just giving them new rights to bargain away.
That's already happening: voice actors who record for video games are required to start their sessions by stating that they assign the rights to use their voice to train a #deepfake model:
But that doesn't mean we have to let the hyperconcentrated entertainment sector alienate creative workers from their labor. As the #WGA has shown us, creative workers aren't just LLCs with MFAs, bargaining business-to-business with corporations - they're workers:
Workers get a better deal with labor law, not copyright law. Copyright law can augment certain labor disputes, but just as often, it benefits corporations, not workers:
If I took a thousand pictures of you and sold them to Clearview AI to train its model, no copyright infringement would take place - and you'd still be screwed. Clearview has a #privacy problem, not a copyright problem.
Giving us pseudocopyrights over our faces won't stop Clearview and its competitors from destroying our lives. Creating and enforcing a federal privacy law with a private right action will.
AI companies say, "You can't use copyright to fix the problems with AI without creating a lot of collateral damage." They're right. But what they fail to mention is, "You can use labor law to ban certain uses of AI without creating that collateral damage."
FR companies say, "You can't use copyright to ban scraping without creating a lot of collateral damage." They're right - but what they don't say is, "On the other hand, a privacy law would put us out of business and leave all the good scraping intact."
Taking entertainment companies and AI vendors and facial recognition creeps at their word is helping them. It's letting them divide and conquer people who value the beneficial elements and those who can't tolerate the harms.
We can have benefits without the harms. We just have to stop thinking about labor and privacy issues as individual matters and treat them as the collective endeavors they really are:
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