#ai and facial recognition

[kanika456]

Artificial Intelligence-Based Face Recognition

Current technology astounds people with incredible innovations that not only make life easier but also more pleasant. Face recognition has consistently shown to be the least intrusive and fastest form of biometric verification. To validate one's identification, the software compares a live image to a previously stored facial print using deep learning techniques. This technology's foundation is built around image processing and machine learning. Face recognition has gained significant interest from researchers as a result of human activity in many security applications such as airports, criminal detection, face tracking, forensics, and so on. Face biometrics, unlike palm prints, iris scans, fingerprints, and so on, can be non-intrusive.

They can be captured without the user's knowledge and then used for security-related applications such as criminal detection, face tracking, airport security, and forensic surveillance systems. Face recognition is extracting facial images from a video or surveillance camera. They are compared to the stored database. Face recognition entails training known photos, categorizing them with known classes, and then storing them in a database. When a test image is sent to the system, it is classed and compared to the stored database.

Face recognition

Face recognition with Artificial Intelligence (AI) is a computer vision technique that identifies a person or object in an image or video. It employs a combination of deep learning, computer vision algorithms, and image processing. These technologies allow a system to detect, recognize, and validate faces in digital photos or videos. The technology has grown in popularity across a wide range of applications, including smartphone unlocking, door unlocking, passport verification, security systems, medical applications, and so on. Some models can recognize emotions through facial expressions.

Difference between Face recognition & Face detection 

Face recognition is the act of identifying a person from an image or video stream, whereas face detection is the process of finding a face within an image or video feed. Face recognition is the process of recognizing and distinguishing people based on their facial characteristics. It uses more advanced processing techniques to determine a person's identity using feature point extraction and comparison algorithms. and can be employed in applications such as automatic attendance systems or security screenings. While face detection is a considerably easier procedure, it can be utilized for applications such as image labeling or changing the angle of a shot based on the recognized face. It is the first phase in the face recognition process and is a simpler method for identifying a face in an image or video feed.

Image Processing and Machine learning

Computer Vision is the process of processing images using computers. It focuses on a high-level understanding of digital images or movies. The requirement is to automate operations that human visual systems can complete. so, a computer should be able to distinguish items like a human face, a lamppost, or even a statue.

OpenCV is a Python package created to handle computer vision problems. OpenCV was developed by Intel in 1999 and later sponsored by Willow Garage.

Machine learning

Every Machine Learning algorithm accepts a dataset as input and learns from it, which essentially implies that the algorithm is learned from the input and output data. It recognizes patterns in the input and generates the desired algorithm. For example, to determine whose face is present in a given photograph, various factors might be considered as a pattern: The facial height and width. Height and width measurements may be unreliable since the image could be rescaled to a smaller face or grid. However, even after rescaling, the ratios stay unchanged: the ratio of the face's height to its width will not alter. Color of the face. Width of other elements of the face, such as the nose, etc

There is a pattern: different faces, such as those seen above, have varied dimensions. comparable faces share comparable dimensions. Machine Learning algorithms can only grasp numbers, making the task difficult. This numerical representation of a "face" (or an element from the training set) is known as a feature vector. A feature vector is made up of various numbers arranged in a specified order. As a simple example, we can map a "face" into a feature vector that can contain multiple features such as: Height of the face (in cm) Width of the face in centimeters Average hue of the face (R, G, B). Lip width (centimeters) Height of the nose (cm)

Essentially, given a picture, we may turn it into a feature vector as follows: Height of the face (in cm) Width of the face in centimeters Average hue of the face (RGB). Lip width (centimeters) Height of the nose (cm)

There could be numerous other features obtained from the photograph, such as hair color, facial hair, spectacles, and so on. 1. Face recognition technology relies on machine learning for two primary functions. These are listed below. Deriving the feature vector: It is impossible to manually enumerate all of the features because there are so many. Many of these features can be intelligently labeled by a machine learning system. For example, a complicated feature could be the ratio of nose height to forehead width. 2. Matching algorithms: Once the feature vectors have been produced, a Machine Learning algorithm must match a new image to the collection of feature vectors included in the corpus.

3. Face Recognition Operations

Face Recognition Operations

Facial recognition technology may differ depending on the system. Different software uses various ways and means to achieve face recognition. The stepwise procedure is as follows: Face Detection: To begin, the camera will detect and identify a face. The face is best recognized when the subject looks squarely at the camera, as this allows for easy facial identification. With technological improvements, this has advanced to the point that the face may be identified with a minor difference in posture when facing the camera.

Face Analysis: A snapshot of the face is taken and evaluated. Most facial recognition uses 2D photos rather than 3D since they are easier to compare to a database. Facial recognition software measures the distance between your eyes and the curve of your cheekbones. Image to Data Conversion: The face traits are now transformed to a mathematical formula and represented as integers. This numerical code is referred to as a face print. Every person has a unique fingerprint, just as they all have a distinct face print.

Match Finding: Next, the code is compared to a database of other face prints. This database contains photographs with identification that may be compared. The system then finds a match for your specific features in the database. It returns a match with connected information such as a name and address, or it depends on the information kept in an individual's database.

Conclusion In conclusion, the evolution of facial recognition technology powered by artificial intelligence has paved the way for ground breaking innovations in various industries. From enhancing security measures to enabling seamless user experiences, AI-based face recognition has proven to be a versatile and invaluable tool.

[mostlysignssomeportents]

Hypothetical AI election disinformation risks vs real AI harms

I'm on tour with my new novel The Bezzle! Catch me TONIGHT (Feb 27) in Portland at Powell's. Then, onto Phoenix (Changing Hands, Feb 29), Tucson (Mar 9-12), and more!

You can barely turn around these days without encountering a think-piece warning of the impending risk of AI disinformation in the coming elections. But a recent episode of This Machine Kills podcast reminds us that these are hypothetical risks, and there is no shortage of real AI harms:

https://soundcloud.com/thismachinekillspod/311-selling-pickaxes-for-the-ai-gold-rush

The algorithmic decision-making systems that increasingly run the back-ends to our lives are really, truly very bad at doing their jobs, and worse, these systems constitute a form of "empiricism-washing": if the computer says it's true, it must be true. There's no such thing as racist math, you SJW snowflake!

https://slate.com/news-and-politics/2019/02/aoc-algorithms-racist-bias.html

Nearly 1,000 British postmasters were wrongly convicted of fraud by Horizon, the faulty AI fraud-hunting system that Fujitsu provided to the Royal Mail. They had their lives ruined by this faulty AI, many went to prison, and at least four of the AI's victims killed themselves:

https://en.wikipedia.org/wiki/British_Post_Office_scandal

Tenants across America have seen their rents skyrocket thanks to Realpage's landlord price-fixing algorithm, which deployed the time-honored defense: "It's not a crime if we commit it with an app":

https://www.propublica.org/article/doj-backs-tenants-price-fixing-case-big-landlords-real-estate-tech

Housing, you'll recall, is pretty foundational in the human hierarchy of needs. Losing your home – or being forced to choose between paying rent or buying groceries or gas for your car or clothes for your kid – is a non-hypothetical, widespread, urgent problem that can be traced straight to AI.

Then there's predictive policing: cities across America and the world have bought systems that purport to tell the cops where to look for crime. Of course, these systems are trained on policing data from forces that are seeking to correct racial bias in their practices by using an algorithm to create "fairness." You feed this algorithm a data-set of where the police had detected crime in previous years, and it predicts where you'll find crime in the years to come.

But you only find crime where you look for it. If the cops only ever stop-and-frisk Black and brown kids, or pull over Black and brown drivers, then every knife, baggie or gun they find in someone's trunk or pockets will be found in a Black or brown person's trunk or pocket. A predictive policing algorithm will naively ingest this data and confidently assert that future crimes can be foiled by looking for more Black and brown people and searching them and pulling them over.

Obviously, this is bad for Black and brown people in low-income neighborhoods, whose baseline risk of an encounter with a cop turning violent or even lethal. But it's also bad for affluent people in affluent neighborhoods – because they are underpoliced as a result of these algorithmic biases. For example, domestic abuse that occurs in full detached single-family homes is systematically underrepresented in crime data, because the majority of domestic abuse calls originate with neighbors who can hear the abuse take place through a shared wall.

But the majority of algorithmic harms are inflicted on poor, racialized and/or working class people. Even if you escape a predictive policing algorithm, a facial recognition algorithm may wrongly accuse you of a crime, and even if you were far away from the site of the crime, the cops will still arrest you, because computers don't lie:

https://www.cbsnews.com/sacramento/news/texas-macys-sunglass-hut-facial-recognition-software-wrongful-arrest-sacramento-alibi/

Trying to get a low-waged service job? Be prepared for endless, nonsensical AI "personality tests" that make Scientology look like NASA:

https://futurism.com/mandatory-ai-hiring-tests

Service workers' schedules are at the mercy of shift-allocation algorithms that assign them hours that ensure that they fall just short of qualifying for health and other benefits. These algorithms push workers into "clopening" – where you close the store after midnight and then open it again the next morning before 5AM. And if you try to unionize, another algorithm – that spies on you and your fellow workers' social media activity – targets you for reprisals and your store for closure.

If you're driving an Amazon delivery van, algorithm watches your eyeballs and tells your boss that you're a bad driver if it doesn't like what it sees. If you're working in an Amazon warehouse, an algorithm decides if you've taken too many pee-breaks and automatically dings you:

https://pluralistic.net/2022/04/17/revenge-of-the-chickenized-reverse-centaurs/

If this disgusts you and you're hoping to use your ballot to elect lawmakers who will take up your cause, an algorithm stands in your way again. "AI" tools for purging voter rolls are especially harmful to racialized people – for example, they assume that two "Juan Gomez"es with a shared birthday in two different states must be the same person and remove one or both from the voter rolls:

https://www.cbsnews.com/news/eligible-voters-swept-up-conservative-activists-purge-voter-rolls/

Hoping to get a solid education, the sort that will keep you out of AI-supervised, precarious, low-waged work? Sorry, kiddo: the ed-tech system is riddled with algorithms. There's the grifty "remote invigilation" industry that watches you take tests via webcam and accuses you of cheating if your facial expressions fail its high-tech phrenology standards:

https://pluralistic.net/2022/02/16/unauthorized-paper/#cheating-anticheat

All of these are non-hypothetical, real risks from AI. The AI industry has proven itself incredibly adept at deflecting interest from real harms to hypothetical ones, like the "risk" that the spicy autocomplete will become conscious and take over the world in order to convert us all to paperclips:

https://pluralistic.net/2023/11/27/10-types-of-people/#taking-up-a-lot-of-space

Whenever you hear AI bosses talking about how seriously they're taking a hypothetical risk, that's the moment when you should check in on whether they're doing anything about all these longstanding, real risks. And even as AI bosses promise to fight hypothetical election disinformation, they continue to downplay or ignore the non-hypothetical, here-and-now harms of AI.

There's something unseemly – and even perverse – about worrying so much about AI and election disinformation. It plays into the narrative that kicked off in earnest in 2016, that the reason the electorate votes for manifestly unqualified candidates who run on a platform of bald-faced lies is that they are gullible and easily led astray.

But there's another explanation: the reason people accept conspiratorial accounts of how our institutions are run is because the institutions that are supposed to be defending us are corrupt and captured by actual conspiracies:

https://memex.craphound.com/2019/09/21/republic-of-lies-the-rise-of-conspiratorial-thinking-and-the-actual-conspiracies-that-fuel-it/

The party line on conspiratorial accounts is that these institutions are good, actually. Think of the rebuttal offered to anti-vaxxers who claimed that pharma giants were run by murderous sociopath billionaires who were in league with their regulators to kill us for a buck: "no, I think you'll find pharma companies are great and superbly regulated":

https://pluralistic.net/2023/09/05/not-that-naomi/#if-the-naomi-be-klein-youre-doing-just-fine

Institutions are profoundly important to a high-tech society. No one is capable of assessing all the life-or-death choices we make every day, from whether to trust the firmware in your car's anti-lock brakes, the alloys used in the structural members of your home, or the food-safety standards for the meal you're about to eat. We must rely on well-regulated experts to make these calls for us, and when the institutions fail us, we are thrown into a state of epistemological chaos. We must make decisions about whether to trust these technological systems, but we can't make informed choices because the one thing we're sure of is that our institutions aren't trustworthy.

Ironically, the long list of AI harms that we live with every day are the most important contributor to disinformation campaigns. It's these harms that provide the evidence for belief in conspiratorial accounts of the world, because each one is proof that the system can't be trusted. The election disinformation discourse focuses on the lies told – and not why those lies are credible.

That's because the subtext of election disinformation concerns is usually that the electorate is credulous, fools waiting to be suckered in. By refusing to contemplate the institutional failures that sit upstream of conspiracism, we can smugly locate the blame with the peddlers of lies and assume the mantle of paternalistic protectors of the easily gulled electorate.

But the group of people who are demonstrably being tricked by AI is the people who buy the horrifically flawed AI-based algorithmic systems and put them into use despite their manifest failures.

As I've written many times, "we're nowhere near a place where bots can steal your job, but we're certainly at the point where your boss can be suckered into firing you and replacing you with a bot that fails at doing your job"

https://pluralistic.net/2024/01/15/passive-income-brainworms/#four-hour-work-week

The most visible victims of AI disinformation are the people who are putting AI in charge of the life-chances of millions of the rest of us. Tackle that AI disinformation and its harms, and we'll make conspiratorial claims about our institutions being corrupt far less credible.

If you'd like an essay-formatted version of this post to read or share, here's a link to it on pluralistic.net, my surveillance-free, ad-free, tracker-free blog:

https://pluralistic.net/2024/02/27/ai-conspiracies/#epistemological-collapse

Image: Cryteria (modified) https://commons.wikimedia.org/wiki/File:HAL9000.svg

CC BY 3.0 https://creativecommons.org/licenses/by/3.0/deed.en

[melyzard]

Been traveling a lot lately and I love how, in US TSA security lines, they always make sure that the big sign saying the facial recognition photo is optional is always turned sideways or set so the spanish-translation side is facing the line and the English-translation side is facing a wall or something.

Anyway, TSA facial recognition photos are 100% not mandatory and if you don't feel like helping a company develop its facial recognition AI software (like, say, Clearview AI), you can just politely tell the TSA agent that you don't want to participate in the photo and instead show an ID or your boarding pass. Like we've been doing for years and years.

[news4dzhozhar]

[allthegeopolitics]

Microsoft has reaffirmed its ban on U.S. police departments from using generative AI for facial recognition through Azure OpenAI Service, the company’s fully managed, enterprise-focused wrapper around OpenAI tech. Language added Wednesday to the terms of service for Azure OpenAI Service more clearly prohibits integrations with Azure OpenAI Service from being used “by or for” police departments for facial recognition in the U.S., including integrations with OpenAI’s current — and possibly future — image-analyzing models. A separate new bullet point covers “any law enforcement globally,” and explicitly bars the use of “real-time facial recognition technology” on mobile cameras, like body cameras and dashcams, to attempt to identify a person in “uncontrolled, in-the-wild” environments.

Continue Reading.

[cyberneurotism]

the article

world is a boring dystopia

[tieflingkisser]

Facial Recognition That Tracks Suspicious Friendliness Is Coming to a Store Near You

Coresight AI has released a new product that sends alerts to store security when customers and staff have anomalous interactions.

A brand new way of being surveilled could be coming to a store near you—a facial recognition system designed to detect when retail workers have anomalous interactions with customers. About a month ago, Israel-based Corsight AI began offering its global clients access to a new service aimed at rooting out what the retail industry calls “sweethearting,”—instances of store employees giving people they know discounts or free items. Traditional facial recognition systems, which have proliferated in the retail industry thanks to companies like Corsight, flag people entering stores who are on designated blacklists of shoplifters. The new sweethearting detection system takes the monitoring a step further by tracking how each customer interacts with different employees over long periods of time.

btw "Israel-based" means yet another piece of dystopian horror tech tried and tested on captive genocide victims

[thefreethoughtprojectcom]

Gone are the days when “Robocop” and “Skynet” were just dystopian ideas for Hollywood blockbusters. The dark, distant future those films portrayed is now a present reality.

Read More: https://thefreethoughtproject.com/government-surveillance/growing-the-surveillance-state-drones-facial-recognition-ai-enlisted-to-fight-crime-but-at-what-cost

#TheFreeThoughtProject

[monetizeme]

The technology, which marries Meta’s smart Ray Ban glasses with the facial recognition service Pimeyes and some other tools, lets someone automatically go from face, to name, to phone number, and home address.

[weirdsociology]

i actually think celebrities should be able to mail a bill to your house for $500 a minute if you bother them in public

[dailybehbeh]

Behbeh

[mostlysignssomeportents]

Podcasting "How To Think About Scraping"

On September 27, I'll be at Chevalier's Books in Los Angeles with Brian Merchant for a joint launch for my new book The Internet Con and his new book, Blood in the Machine. On October 2, I'll be in Boise to host an event with VE Schwab.

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:

https://doctorow.medium.com/how-to-think-about-scraping-2db6f69a7e3d?sk=4a1d687171de1a3f3751433bffbb5a96

What are those benefits from scraping? Well, take computational linguistics, 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:

https://memex.craphound.com/2019/07/24/because-internet-the-new-linguistics-of-informal-english/

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 institutional review board 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 Internet Archive has scraped every website it could find, storing snapshots of every page it found in a giant, searchable database called the Wayback Machine. 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 robots exclusion protocol (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.

Mostly.

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:

https://medium.com/@brokenravioli/proof-that-the-new-york-times-isn-t-feeling-the-bern-c74e1109cdf6

This happens all the time, with major news sites from all around the world:

http://newsdiffs.org/examples/

By scraping these sites and retaining the different versions of their article, the Archive both detects and prevents journalistic malpractice. This is canonical fair use, 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 Mario Zechner 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:

https://mastodon.gamedev.place/@badlogic/111071627182734180

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:

https://mastodon.gamedev.place/@badlogic/111071673594791946

Emboldened, Zechner open-sourced his monitoring tool, and attracted developers from other countries. Soon, they were documenting ripoffs in Germany and Slovenia, too:

https://mastodon.gamedev.place/@badlogic/111071485142332765

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 Galen Weston – pulled the most Les Mis-ass caper imaginable when they illegally conspired to rig the price of bread:

https://en.wikipedia.org/wiki/Bread_price-fixing_in_Canada

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 Large Language Model?

Yes, there are socially beneficial – even vital – uses for LLMs.

Take HRDAG's work on truth and reconciliation in Colombia. The Human Rights Data Analysis Group is a tiny nonprofit that makes an outsized contribution to human rights, by using statistical methods to reveal the full scope of the human rights crimes that take place in the shadows, from East Timor to Serbia, South Africa to the USA:

https://hrdag.org/

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:

https://hrdag.org/jep-cev-colombia/

What's in that dataset? It's a merger and analysis of more than 100 databases of killings, child soldier 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.

But.

Scraping to violate peoples' privacy is very bad. Take Clearview AI, the grifty, sleazy facial recognition company that scraped billions of photos in order to train a system that they sell to cops, corporations and authoritarian governments:

https://pluralistic.net/2023/09/20/steal-your-face/#hoan-ton-that

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 screenplays, 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 Rebecca Giblin and I write in our book Chokepoint Capitalism, 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:

https://chokepointcapitalism.com/

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:

https://www.eff.org/deeplinks/2023/04/ai-art-generators-and-online-image-market

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.

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 now required to start their sessions by stating that they assign the rights to use their voice to train a deepfake model:

https://www.vice.com/en/article/5d37za/voice-actors-sign-away-rights-to-artificial-intelligence

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:

https://pluralistic.net/2023/08/20/everything-made-by-an-ai-is-in-the-public-domain/

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:

https://locusmag.com/2019/05/cory-doctorow-steering-with-the-windshield-wipers/

Likewise, the problem with Clearview AI isn't that it infringes on photographers' copyrights. 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. It will put Clearview and all of its competitors out of business, instantly and forever:

https://www.eff.org/deeplinks/2019/01/you-should-have-right-sue-companies-violate-your-privacy

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."

Facial recognition companies say, "You can't use copyright to ban scraping without creating a lot of collateral damage." They're right too – 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 the 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:

https://pluralistic.net/2023/02/26/united-we-stand/

Here's a link to the podcast:

https://craphound.com/news/2023/09/24/how-to-think-about-scraping/

And here's a direct link to the MP3 (hosting courtesy of the Internet Archive; they'll host your stuff for free, forever):

https://archive.org/download/Cory_Doctorow_Podcast_450/Cory_Doctorow_Podcast_450_-_How_To_Think_About_Scraping.mp3

And here's the RSS feed for my podcast:

http://feeds.feedburner.com/doctorow_podcast

If you'd like an essay-formatted version of this post to read or share, here's a link to it on pluralistic.net, my surveillance-free, ad-free, tracker-free blog:

https://pluralistic.net/2023/09/25/deep-scrape/#steering-with-the-windshield-wipers

Image: syvwlch (modified) https://commons.wikimedia.org/wiki/File:Print_Scraper_(5856642549).jpg

CC BY-SA 2.0 https://creativecommons.org/licenses/by/2.0/deed.en

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EU passes artificial intelligence act