AI training

How to stop LinkedIn from training AI on your data

AI models, AI training, AI training data, Artificial Intelligence, generative ai, LinkedIn, Policy / Kris Guyer / September 19, 2024

Better to beg for forgiveness than ask for permission? —

LinkedIn limits opt-outs to future training, warns AI models may spout personal data.

Ashley Belanger – Sep 19, 2024 10: 00 pm UTC

LinkedIn admitted Wednesday that it has been training its own AI on many users’ data without seeking consent. Now there’s no way for users to opt out of training that has already occurred, as LinkedIn limits opt-out to only future AI training.

In a blog detailing updates coming on November 20, LinkedIn general counsel Blake Lawit confirmed that LinkedIn’s user agreement and privacy policy will be changed to better explain how users’ personal data powers AI on the platform.

Under the new privacy policy, LinkedIn now informs users that “we may use your personal data… [to] develop and train artificial intelligence (AI) models, develop, provide, and personalize our Services, and gain insights with the help of AI, automated systems, and inferences, so that our Services can be more relevant and useful to you and others.”

An FAQ explained that the personal data could be collected any time a user interacts with generative AI or other AI features, as well as when a user composes a post, changes their preferences, provides feedback to LinkedIn, or uses the platform for any amount of time.

That data is then stored until the user deletes the AI-generated content. LinkedIn recommends that users use its data access tool if they want to delete or request to delete data collected about past LinkedIn activities.

LinkedIn’s AI models powering generative AI features “may be trained by LinkedIn or another provider,” such as Microsoft, which provides some AI models through its Azure OpenAI service, the FAQ said.

A potentially major privacy risk for users, LinkedIn’s FAQ noted, is that users who “provide personal data as an input to a generative AI powered feature” could end up seeing their “personal data being provided as an output.”

LinkedIn claims that it “seeks to minimize personal data in the data sets used to train the models,” relying on “privacy enhancing technologies to redact or remove personal data from the training dataset.”

While Lawit’s blog avoids clarifying if data already collected can be removed from AI training data sets, the FAQ affirmed that users who automatically opted in to sharing personal data for AI training can only opt out of the invasive data collection “going forward.”

Opting out “does not affect training that has already taken place,” the FAQ said.

A LinkedIn spokesperson told Ars that it “benefits all members” to be opted in to AI training “by default.”

“People can choose to opt out, but they come to LinkedIn to be found for jobs and networking and generative AI is part of how we are helping professionals with that change,” LinkedIn’s spokesperson said.

By allowing opt-outs of future AI training, LinkedIn’s spokesperson additionally claimed that the platform is giving “people using LinkedIn even more choice and control when it comes to how we use data to train our generative AI technology.”

How to opt out of AI training on LinkedIn

Users can opt out of AI training by navigating to the “Data privacy” section in their account settings, then turning off the option allowing collection of “data for generative AI improvement” that LinkedIn otherwise automatically turns on for most users.

The only exception is for users in the European Economic Area or Switzerland, who are protected by stricter privacy laws that either require consent from platforms to collect personal data or for platforms to justify the data collection as a legitimate interest. Those users will not see an option to opt out, because they were never opted in, LinkedIn repeatedly confirmed.

Additionally, users can “object to the use of their personal data for training” generative AI models not used to generate LinkedIn content—such as models used for personalization or content moderation purposes, The Verge noted—by submitting the LinkedIn Data Processing Objection Form.

Last year, LinkedIn shared AI principles, promising to take “meaningful steps to reduce the potential risks of AI.”

One risk that the updated user agreement specified is that using LinkedIn’s generative features to help populate a profile or generate suggestions when writing a post could generate content that “might be inaccurate, incomplete, delayed, misleading or not suitable for your purposes.”

Users are advised that they are responsible for avoiding sharing misleading information or otherwise spreading AI-generated content that may violate LinkedIn’s community guidelines. And users are additionally warned to be cautious when relying on any information shared on the platform.

“Like all content and other information on our Services, regardless of whether it’s labeled as created by ‘AI,’ be sure to carefully review before relying on it,” LinkedIn’s user agreement says.

Back in 2023, LinkedIn claimed that it would always “seek to explain in clear and simple ways how our use of AI impacts people,” because users’ “understanding of AI starts with transparency.”

Legislation like the European Union’s AI Act and the GDPR—especially with its strong privacy protections—if enacted elsewhere, would lead to fewer shocks to unsuspecting users. That would put all companies and their users on equal footing when it comes to training AI models and result in fewer nasty surprises and angry customers.

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Nonprofit scrubs illegal content from controversial AI training dataset

AI training, AI training data, Artificial Intelligence, child sex abuse, child sexual abuse materials, csam, image generators, LAION, LAION-5b, Policy, stable diffusion 1.5 / Kris Guyer / August 30, 2024

After Stanford Internet Observatory researcher David Thiel found links to child sexual abuse materials (CSAM) in an AI training dataset tainting image generators, the controversial dataset was immediately taken down in 2023.

Now, the LAION (Large-scale Artificial Intelligence Open Network) team has released a scrubbed version of the LAION-5B dataset called Re-LAION-5B and claimed that it “is the first web-scale, text-link to images pair dataset to be thoroughly cleaned of known links to suspected CSAM.”

To scrub the dataset, LAION partnered with the Internet Watch Foundation (IWF) and the Canadian Center for Child Protection (C3P) to remove 2,236 links that matched with hashed images in the online safety organizations’ databases. Removals include all the links flagged by Thiel, as well as content flagged by LAION’s partners and other watchdogs, like Human Rights Watch, which warned of privacy issues after finding photos of real kids included in the dataset without their consent.

In his study, Thiel warned that “the inclusion of child abuse material in AI model training data teaches tools to associate children in illicit sexual activity and uses known child abuse images to generate new, potentially realistic child abuse content.”

Thiel urged LAION and other researchers scraping the Internet for AI training data that a new safety standard was needed to better filter out not just CSAM, but any explicit imagery that could be combined with photos of children to generate CSAM. (Recently, the US Department of Justice pointedly said that “CSAM generated by AI is still CSAM.”)

While LAION’s new dataset won’t alter models that were trained on the prior dataset, LAION claimed that Re-LAION-5B sets “a new safety standard for cleaning web-scale image-link datasets.” Where before illegal content “slipped through” LAION’s filters, the researchers have now developed an improved new system “for identifying and removing illegal content,” LAION’s blog said.

Thiel told Ars that he would agree that LAION has set a new safety standard with its latest release, but “there are absolutely ways to improve it.” However, “those methods would require possession of all original images or a brand new crawl,” and LAION’s post made clear that it only utilized image hashes and did not conduct a new crawl that could have risked pulling in more illegal or sensitive content. (On Threads, Thiel shared more in-depth impressions of LAION’s effort to clean the dataset.)

LAION warned that “current state-of-the-art filters alone are not reliable enough to guarantee protection from CSAM in web scale data composition scenarios.”

“To ensure better filtering, lists of hashes of suspected links or images created by expert organizations (in our case, IWF and C3P) are suitable choices,” LAION’s blog said. “We recommend research labs and any other organizations composing datasets from the public web to partner with organizations like IWF and C3P to obtain such hash lists and use those for filtering. In the longer term, a larger common initiative can be created that makes such hash lists available for the research community working on dataset composition from web.”

According to LAION, the bigger concern is that some links to known CSAM scraped into a 2022 dataset are still active more than a year later.

“It is a clear hint that law enforcement bodies have to intensify the efforts to take down domains that host such image content on public web following information and recommendations by organizations like IWF and C3P, making it a safer place, also for various kinds of research related activities,” LAION’s blog said.

HRW researcher Hye Jung Han praised LAION for removing sensitive data that she flagged, while also urging more interventions.

“LAION’s responsive removal of some children’s personal photos from their dataset is very welcome, and will help to protect these children from their likenesses being misused by AI systems,” Han told Ars. “It’s now up to governments to pass child data protection laws that would protect all children’s privacy online.”

Although LAION’s blog said that the content removals represented an “upper bound” of CSAM that existed in the initial dataset, AI specialist and Creative.AI co-founder Alex Champandard told Ars that he’s skeptical that all CSAM was removed.

“They only filter out previously identified CSAM, which is only a partial solution,” Champandard told Ars. “Statistically speaking, most instances of CSAM have likely never been reported nor investigated by C3P or IWF. A more reasonable estimate of the problem is about 25,000 instances of things you’d never want to train generative models on—maybe even 50,000.”

Champandard agreed with Han that more regulations are needed to protect people from AI harms when training data is scraped from the web.

“There’s room for improvement on all fronts: privacy, copyright, illegal content, etc.,” Champandard said. Because “there are too many data rights being broken with such web-scraped datasets,” Champandard suggested that datasets like LAION’s won’t “stand the test of time.”

“LAION is simply operating in the regulatory gap and lag in the judiciary system until policymakers realize the magnitude of the problem,” Champandard said.

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People game AIs via game theory

AI, AI training, Behavioral science, Computer science, Human behavior, Science / Kris Guyer / August 9, 2024

Games inside games —

They reject more of the AI’s offers, probably to get it to be more generous.

John Timmer – Aug 9, 2024 8: 13 pm UTC

A judge's gavel near a pile of small change. — Enlarge / In the experiments, people had to judge what constituted a fair monetary offer.

In many cases, AIs are trained on material that’s either made or curated by humans. As a result, it can become a significant challenge to keep the AI from replicating the biases of those humans and the society they belong to. And the stakes are high, given we’re using AIs to make medical and financial decisions.

But some researchers at Washington University in St. Louis have found an additional wrinkle in these challenges: The people doing the training may potentially change their behavior when they know it can influence the future choices made by an AI. And, in at least some cases, they carry the changed behaviors into situations that don’t involve AI training.

Would you like to play a game?

The work involved getting volunteers to participate in a simple form of game theory. Testers gave two participants a pot of money—$10, in this case. One of the two was then asked to offer some fraction of that money to the other, who could choose to accept or reject the offer. If the offer was rejected, nobody got any money.

From a purely rational economic perspective, people should accept anything they’re offered, since they’ll end up with more money than they would have otherwise. But in reality, people tend to reject offers that deviate too much from a 50/50 split, as they have a sense that a highly imbalanced split is unfair. Their rejection allows them to punish the person who made the unfair offer. While there are some cultural differences in terms of where the split becomes unfair, this effect has been replicated many times, including in the current work.

The twist with the new work, performed by Lauren Treimana, Chien-Ju Hoa, and Wouter Kool, is that they told some of the participants that their partner was an AI, and the results of their interactions with it would be fed back into the system to train its future performance.

This takes something that’s implicit in a purely game-theory-focused setup—that rejecting offers can help partners figure out what sorts of offers are fair—and makes it highly explicit. Participants, or at least the subset involved in the experimental group that are being told they’re training an AI, could readily infer that their actions would influence the AI’s future offers.

The question the researchers were curious about was whether this would influence the behavior of the human participants. They compared this to the behavior of a control group who just participated in the standard game theory test.

Training fairness

Treimana, Hoa, and Kool had pre-registered a number of multivariate analyses that they planned to perform with the data. But these didn’t always produce consistent results between experiments, possibly because there weren’t enough participants to tease out relatively subtle effects with any statistical confidence and possibly because the relatively large number of tests would mean that a few positive results would turn up by chance.

So, we’ll focus on the simplest question that was addressed: Did being told that you were training an AI alter someone’s behavior? This question was asked through a number of experiments that were very similar. (One of the key differences between them was whether the information regarding AI training was displayed with a camera icon, since people will sometimes change their behavior if they’re aware they’re being observed.)

The answer to the question is a clear yes: people will in fact change their behavior when they think they’re training an AI. Through a number of experiments, participants were more likely to reject unfair offers if they were told that their sessions would be used to train an AI. In a few of the experiments, they were also more likely to reject what were considered fair offers (in US populations, the rejection rate goes up dramatically once someone proposes a 70/30 split, meaning $7 goes to the person making the proposal in these experiments). The researchers suspect this is due to people being more likely to reject borderline “fair” offers such as a 60/40 split.

This happened even though rejecting any offer exacts an economic cost on the participants. And people persisted in this behavior even when they were told that they wouldn’t ever interact with the AI after training was complete, meaning they wouldn’t personally benefit from any changes in the AI’s behavior. So here, it appeared that people would make a financial sacrifice to train the AI in a way that would benefit others.

Strikingly, in two of the three experiments that did follow up testing, participants continued to reject offers at a higher rate two days after their participation in the AI training, even when they were told that their actions were no longer being used to train the AI. So, to some extent, participating in AI training seems to have caused them to train themselves to behave differently.

Obviously, this won’t affect every sort of AI training, and a lot of the work that goes into producing material that’s used in training something like a Large Language Model won’t have been done with any awareness that it might be used to train an AI. Still, there’s plenty of cases where humans do get more directly involved in training, so it’s worthwhile being aware that this is another route that can allow biases to creep in.

PNAS, 2024. DOI: 10.1073/pnas.2408731121 (About DOIs).

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AI trains on kids’ photos even when parents use strict privacy settings

AI, ai deepfakes, AI training, Artificial Intelligence, australia, child online safety, children's online privacy, generative ai, Human rights, image generators, large language models, Policy, youtube / Kris Guyer / July 2, 2024

“Outrageous” —

Even unlisted YouTube videos are used to train AI, watchdog warns.

Ashley Belanger – Jul 2, 2024 7: 37 pm UTC

Human Rights Watch (HRW) continues to reveal how photos of real children casually posted online years ago are being used to train AI models powering image generators—even when platforms prohibit scraping and families use strict privacy settings.

Last month, HRW researcher Hye Jung Han found 170 photos of Brazilian kids that were linked in LAION-5B, a popular AI dataset built from Common Crawl snapshots of the public web. Now, she has released a second report, flagging 190 photos of children from all of Australia’s states and territories, including indigenous children who may be particularly vulnerable to harms.

These photos are linked in the dataset “without the knowledge or consent of the children or their families.” They span the entirety of childhood, making it possible for AI image generators to generate realistic deepfakes of real Australian children, Han’s report said. Perhaps even more concerning, the URLs in the dataset sometimes reveal identifying information about children, including their names and locations where photos were shot, making it easy to track down children whose images might not otherwise be discoverable online.

That puts children in danger of privacy and safety risks, Han said, and some parents thinking they’ve protected their kids’ privacy online may not realize that these risks exist.

From a single link to one photo that showed “two boys, ages 3 and 4, grinning from ear to ear as they hold paintbrushes in front of a colorful mural,” Han could trace “both children’s full names and ages, and the name of the preschool they attend in Perth, in Western Australia.” And perhaps most disturbingly, “information about these children does not appear to exist anywhere else on the Internet”—suggesting that families were particularly cautious in shielding these boys’ identities online.

Stricter privacy settings were used in another image that Han found linked in the dataset. The photo showed “a close-up of two boys making funny faces, captured from a video posted on YouTube of teenagers celebrating” during the week after their final exams, Han reported. Whoever posted that YouTube video adjusted privacy settings so that it would be “unlisted” and would not appear in searches.

Only someone with a link to the video was supposed to have access, but that didn’t stop Common Crawl from archiving the image, nor did YouTube policies prohibiting AI scraping or harvesting of identifying information.

Reached for comment, YouTube’s spokesperson, Jack Malon, told Ars that YouTube has “been clear that the unauthorized scraping of YouTube content is a violation of our Terms of Service, and we continue to take action against this type of abuse.” But Han worries that even if YouTube did join efforts to remove images of children from the dataset, the damage has been done, since AI tools have already trained on them. That’s why—even more than parents need tech companies to up their game blocking AI training—kids need regulators to intervene and stop training before it happens, Han’s report said.

Han’s report comes a month before Australia is expected to release a reformed draft of the country’s Privacy Act. Those reforms include a draft of Australia’s first child data protection law, known as the Children’s Online Privacy Code, but Han told Ars that even people involved in long-running discussions about reforms aren’t “actually sure how much the government is going to announce in August.”

“Children in Australia are waiting with bated breath to see if the government will adopt protections for them,” Han said, emphasizing in her report that “children should not have to live in fear that their photos might be stolen and weaponized against them.”

AI uniquely harms Australian kids

To hunt down the photos of Australian kids, Han “reviewed fewer than 0.0001 percent of the 5.85 billion images and captions contained in the data set.” Because her sample was so small, Han expects that her findings represent a significant undercount of how many children could be impacted by the AI scraping.

“It’s astonishing that out of a random sample size of about 5,000 photos, I immediately fell into 190 photos of Australian children,” Han told Ars. “You would expect that there would be more photos of cats than there are personal photos of children,” since LAION-5B is a “reflection of the entire Internet.”

LAION is working with HRW to remove links to all the images flagged, but cleaning up the dataset does not seem to be a fast process. Han told Ars that based on her most recent exchange with the German nonprofit, LAION had not yet removed links to photos of Brazilian kids that she reported a month ago.

LAION declined Ars’ request for comment.

In June, LAION’s spokesperson, Nathan Tyler, told Ars that, “as a nonprofit, volunteer organization,” LAION is committed to doing its part to help with the “larger and very concerning issue” of misuse of children’s data online. But removing links from the LAION-5B dataset does not remove the images online, Tyler noted, where they can still be referenced and used in other AI datasets, particularly those relying on Common Crawl. And Han pointed out that removing the links from the dataset doesn’t change AI models that have already trained on them.

“Current AI models cannot forget data they were trained on, even if the data was later removed from the training data set,” Han’s report said.

Kids whose images are used to train AI models are exposed to a variety of harms, Han reported, including a risk that image generators could more convincingly create harmful or explicit deepfakes. In Australia last month, “about 50 girls from Melbourne reported that photos from their social media profiles were taken and manipulated using AI to create sexually explicit deepfakes of them, which were then circulated online,” Han reported.

For First Nations children—”including those identified in captions as being from the Anangu, Arrernte, Pitjantjatjara, Pintupi, Tiwi, and Warlpiri peoples”—the inclusion of links to photos threatens unique harms. Because culturally, First Nations peoples “restrict the reproduction of photos of deceased people during periods of mourning,” Han said the AI training could perpetuate harms by making it harder to control when images are reproduced.

Once an AI model trains on the images, there are other obvious privacy risks, including a concern that AI models are “notorious for leaking private information,” Han said. Guardrails added to image generators do not always prevent these leaks, with some tools “repeatedly broken,” Han reported.

LAION recommends that, if troubled by the privacy risks, parents remove images of kids online as the most effective way to prevent abuse. But Han told Ars that’s “not just unrealistic, but frankly, outrageous.”

“The answer is not to call for children and parents to remove wonderful photos of kids online,” Han said. “The call should be [for] some sort of legal protections for these photos, so that kids don’t have to always wonder if their selfie is going to be abused.”

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Adobe to update vague AI terms after users threaten to cancel subscriptions

adobe, Adobe Firefly, AI training, firefly ai, generative ai, Illustrator, InDesign, Photoshop, Policy / Paul Patrick / June 11, 2024

Adobe has promised to update its terms of service to make it “abundantly clear” that the company will “never” train generative AI on creators’ content after days of customer backlash, with some saying they would cancel Adobe subscriptions over its vague terms.

Users got upset last week when an Adobe pop-up informed them of updates to terms of use that seemed to give Adobe broad permissions to access user content, take ownership of that content, or train AI on that content. The pop-up forced users to agree to these terms to access Adobe apps, disrupting access to creatives’ projects unless they immediately accepted them.

For any users unwilling to accept, canceling annual plans could trigger fees amounting to 50 percent of their remaining subscription cost. Adobe justifies collecting these fees because a “yearly subscription comes with a significant discount.”

On X (formerly Twitter), YouTuber Sasha Yanshin wrote that he canceled his Adobe license “after many years as a customer,” arguing that “no creator in their right mind can accept” Adobe’s terms that seemed to seize a “worldwide royalty-free license to reproduce, display, distribute” or “do whatever they want with any content” produced using their software.

“This is beyond insane,” Yanshin wrote on X. “You pay a huge monthly subscription, and they want to own your content and your entire business as well. Going to have to learn some new tools.”

Adobe’s design leader Scott Belsky replied, telling Yanshin that Adobe had clarified the update in a blog post and noting that Adobe’s terms for licensing content are typical for every cloud content company. But he acknowledged that those terms were written about 11 years ago and that the language could be plainer, writing that “modern terms of service in the current climate of customer concerns should evolve to address modern day concerns directly.”

Yanshin has so far not been encouraged by any of Adobe’s attempts to clarify its terms, writing that he gives “precisely zero f*cks about Adobe’s clarifications or blog posts.”

“You forced people to sign new Terms,” Yanshin told Belsky on X. “Legally, they are the only thing that matters.”

Another user in the thread using an anonymous X account also pushed back, writing, “Point to where it says in the terms that you won’t use our content for LLM or AI training? And state unequivocally that you do not have the right to use our work beyond storing it. That would go a long way.”

“Stay tuned,” Belsky wrote on X. “Unfortunately, it takes a process to update a TOS,” but “we are working on incorporating these clarifications.”

Belsky co-authored the blog this week announcing that Adobe’s terms would be updated by June 18 after a week of fielding feedback from users.

“We’ve never trained generative AI on customer content, taken ownership of a customer’s work, or allowed access to customer content beyond legal requirements,” Adobe’s blog said. “Nor were we considering any of those practices as part of the recent Terms of Use update. That said, we agree that evolving our Terms of Use to reflect our commitments to our community is the right thing to do.”

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Slack users horrified to discover messages used for AI training

AI, AI training, AI training data, Artificial Intelligence, chat app, machine learning, Policy, slack, slack ai / Kris Guyer / May 19, 2024

After launching Slack AI in February, Slack appears to be digging its heels in, defending its vague policy that by default sucks up customers’ data—including messages, content, and files—to train Slack’s global AI models.

According to Slack engineer Aaron Maurer, Slack has explained in a blog that the Salesforce-owned chat service does not train its large language models (LLMs) on customer data. But Slack’s policy may need updating “to explain more carefully how these privacy principles play with Slack AI,” Maurer wrote on Threads, partly because the policy “was originally written about the search/recommendation work we’ve been doing for years prior to Slack AI.”

Maurer was responding to a Threads post from engineer and writer Gergely Orosz, who called for companies to opt out of data sharing until the policy is clarified, not by a blog, but in the actual policy language.

“An ML engineer at Slack says they don’t use messages to train LLM models,” Orosz wrote. “My response is that the current terms allow them to do so. I’ll believe this is the policy when it’s in the policy. A blog post is not the privacy policy: every serious company knows this.”

The tension for users becomes clearer if you compare Slack’s privacy principles with how the company touts Slack AI.

Slack’s privacy principles specifically say that “Machine Learning (ML) and Artificial Intelligence (AI) are useful tools that we use in limited ways to enhance our product mission. To develop AI/ML models, our systems analyze Customer Data (e.g. messages, content, and files) submitted to Slack as well as other information (including usage information) as defined in our privacy policy and in your customer agreement.”

Meanwhile, Slack AI’s page says, “Work without worry. Your data is your data. We don’t use it to train Slack AI.”

Because of this incongruity, users called on Slack to update the privacy principles to make it clear how data is used for Slack AI or any future AI updates. According to a Salesforce spokesperson, the company has agreed an update is needed.

“Yesterday, some Slack community members asked for more clarity regarding our privacy principles,” Salesforce’s spokesperson told Ars. “We’ll be updating those principles today to better explain the relationship between customer data and generative AI in Slack.”

The spokesperson told Ars that the policy updates will clarify that Slack does not “develop LLMs or other generative models using customer data,” “use customer data to train third-party LLMs” or “build or train these models in such a way that they could learn, memorize, or be able to reproduce customer data.” The update will also clarify that “Slack AI uses off-the-shelf LLMs where the models don’t retain customer data,” ensuring that “customer data never leaves Slack’s trust boundary, and the providers of the LLM never have any access to the customer data.”

These changes, however, do not seem to address a key concern for users who never explicitly consented to sharing chats and other Slack content for use in AI training.

Users opting out of sharing chats with Slack

This controversial policy is not new. Wired warned about it in April, and TechCrunch reported that the policy has been in place since at least September 2023.

But widespread backlash began swelling last night on Hacker News, where Slack users called out the chat service for seemingly failing to notify users about the policy change, instead quietly opting them in by default. To critics, it felt like there was no benefit to opting in for anyone but Slack.

From there, the backlash spread to social media, where SlackHQ hastened to clarify Slack’s terms with explanations that did not seem to address all the criticism.

“I’m sorry Slack, you’re doing fucking WHAT with user DMs, messages, files, etc?” Corey Quinn, the chief cloud economist for a cost management company called Duckbill Group, posted on X. “I’m positive I’m not reading this correctly.”

SlackHQ responded to Quinn after the economist declared, “I hate this so much,” and confirmed that he had opted out of data sharing in his paid workspace.

“To clarify, Slack has platform-level machine-learning models for things like channel and emoji recommendations and search results,” SlackHQ posted. “And yes, customers can exclude their data from helping train those (non-generative) ML models. Customer data belongs to the customer.”

Later in the thread, SlackHQ noted, “Slack AI—which is our generative AI experience natively built in Slack—[and] is a separately purchased add-on that uses Large Language Models (LLMs) but does not train those LLMs on customer data.”

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Sony Music opts out of AI training for its entire catalog

AI, AI training, llm, music, Sony, syndication / Mike M. / May 17, 2024

Taking a hard line —

Music group contacts more than 700 companies to prohibit use of content

Daniel Thomas, Financial Times – May 17, 2024 1: 16 pm UTC

picture of Beyonce who is a Sony artist — Enlarge / The Sony Music letter expressly prohibits artificial intelligence developers from using its music — which includes artists such as Beyoncé.

Kevin Mazur/WireImage for Parkwood via Getty Images

Sony Music is sending warning letters to more than 700 artificial intelligence developers and music streaming services globally in the latest salvo in the music industry’s battle against tech groups ripping off artists.

The Sony Music letter, which has been seen by the Financial Times, expressly prohibits AI developers from using its music—which includes artists such as Harry Styles, Adele and Beyoncé—and opts out of any text and data mining of any of its content for any purposes such as training, developing or commercializing any AI system.

Sony Music is sending the letter to companies developing AI systems including OpenAI, Microsoft, Google, Suno, and Udio, according to those close to the group.

The world’s second-largest music group is also sending separate letters to streaming platforms, including Spotify and Apple, asking them to adopt “best practice” measures to protect artists and songwriters and their music from scraping, mining and training by AI developers without consent or compensation. It has asked them to update their terms of service, making it clear that mining and training on its content is not permitted.

Sony Music declined to comment further.

The letter, which is being sent to tech companies around the world this week, marks an escalation of the music group’s attempts to stop the melodies, lyrics and images from copyrighted songs and artists being used by tech companies to produce new versions or to train systems to create their own music.

The letter says that Sony Music and its artists “recognize the significant potential and advancement of artificial intelligence” but adds that “unauthorized use . . . in the training, development or commercialization of AI systems deprives [Sony] of control over and appropriate compensation.”

It says: “This letter serves to put you on notice directly, and reiterate, that [Sony’s labels] expressly prohibit any use of [their] content.”

Executives at the New York-based group are concerned that their music has already been ripped off, and want to set out a clearly defined legal position that would be the first step to taking action against any developer of AI systems it considers to have exploited its music. They argue that Sony Music would be open to doing deals with AI developers to license the music, but want to reach a fair price for doing so.

The letter says: “Due to the nature of your operations and published information about your AI systems, we have reason to believe that you and/or your affiliates may already have made unauthorized uses [of Sony content] in relation to the training, development or commercialization of AI systems.”

Sony Music has asked developers to provide details of all content used by next week.

The letter also reflects concerns over the fragmented approach to AI regulation around the world. Global regulations over AI vary widely, with some regions moving forward with new rules and legal frameworks to cover the training and use of such systems but others leaving it to creative industries companies to work out relationships with developers.

In many countries around the world, particularly in the EU, copyright owners are advised to state publicly that content is not available for data mining and training for AI.

The letter says the prohibition includes using any bot, spider, scraper or automated program, tool, algorithm, code, process or methodology, as well as any “automated analytical techniques aimed at analyzing text and data in digital form to generate information, including patterns, trends, and correlations.”

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