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How to poison the data that Big Tech uses to surveil you

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How to poison the data that Big Tech uses to surveil you


In a new paper being presented at the Association for Computing Machinery’s Fairness, Accountability, and Transparency conference next week, researchers including PhD students Nicholas Vincent and Hanlin Li propose three ways the public can exploit this to their advantage:

  • Data strikes, inspired by the idea of labor strikes, which involve withholding or deleting your data so a tech firm cannot use it—leaving a platform or installing privacy tools, for instance.
  • Data poisoning, which involves contributing meaningless or harmful data. AdNauseam, for example, is a browser extension that clicks on every single ad served to you, thus confusing Google’s ad-targeting algorithms.
  • Conscious data contribution, which involves giving meaningful data to the competitor of a platform you want to protest, such as by uploading your Facebook photos to Tumblr instead.

People already use many of these tactics to protect their own privacy. If you’ve ever used an ad blocker or another browser extension that modifies your search results to exclude certain websites, you’ve engaged in data striking and reclaimed some agency over the use of your data. But as Hill found, sporadic individual actions like these don’t do much to get tech giants to change their behaviors.

What if millions of people were to coordinate to poison a tech giant’s data well, though? That might just give them some leverage to assert their demands.

There may have already been a few examples of this. In January, millions of users deleted their WhatsApp accounts and moved to competitors like Signal and Telegram after Facebook announced that it would begin sharing WhatsApp data with the rest of the company. The exodus caused Facebook to delay its policy changes.

Just this week, Google also announced that it would stop tracking individuals across the web and targeting ads at them. While it’s unclear whether this is a real change or just a rebranding, says Vincent, it’s possible that the increased use of tools like AdNauseam contributed to that decision by degrading the effectiveness of the company’s algorithms. (Of course, it’s ultimately hard to tell. “The only person who really knows how effectively a data leverage movement impacted a system is the tech company,” he says.)

Vincent and Li think these campaigns can complement strategies such as policy advocacy and worker organizing in the movement to resist Big Tech.

“It’s exciting to see this kind of work,” says Ali Alkhatib, a research fellow at the University of San Francisco’s Center for Applied Data Ethics, who was not involved in the research. “It was really interesting to see them thinking about the collective or holistic view: we can mess with the well and make demands with that threat, because it is our data and it all goes into this well together.”

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The original startup behind Stable Diffusion has launched a generative AI for video

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The original startup behind Stable Diffusion has launched a generative AI for video


Set up in 2018, Runway has been developing AI-powered video-editing software for several years. Its tools are used by TikTokers and YouTubers as well as mainstream movie and TV studios. The makers of The Late Show with Stephen Colbert used Runway software to edit the show’s graphics; the visual effects team behind the hit movie Everything Everywhere All at Once used the company’s tech to help create certain scenes.  

In 2021, Runway collaborated with researchers at the University of Munich to build the first version of Stable Diffusion. Stability AI, a UK-based startup, then stepped in to pay the computing costs required to train the model on much more data. In 2022, Stability AI took Stable Diffusion mainstream, transforming it from a research project into a global phenomenon. 

But the two companies no longer collaborate. Getty is now taking legal action against Stability AI—claiming that the company used Getty’s images, which appear in Stable Diffusion’s training data, without permission—and Runway is keen to keep its distance.

Gen-1 represents a new start for Runway. It follows a smattering of text-to-video models revealed late last year, including Make-a-Video from Meta and Phenaki from Google, both of which can generate very short video clips from scratch. It is also similar to Dreamix, a generative AI from Google revealed last week, which can create new videos from existing ones by applying specified styles. But at least judging from Runway’s demo reel, Gen-1 appears to be a step up in video quality. Because it transforms existing footage, it can also produce much longer videos than most previous models. (The company says it will post technical details about Gen-1 on its website in the next few days.)   

Unlike Meta and Google, Runway has built its model with customers in mind. “This is one of the first models to be developed really closely with a community of video makers,” says Valenzuela. “It comes with years of insight about how filmmakers and VFX editors actually work on post-production.”

Gen-1, which runs on the cloud via Runway’s website, is being made available to a handful of invited users today and will be launched to everyone on the waitlist in a few weeks.

Last year’s explosion in generative AI was fueled by the millions of people who got their hands on powerful creative tools for the first time and shared what they made with them. Valenzuela hopes that putting Gen-1 into the hands of creative professionals will soon have a similar impact on video.

“We’re really close to having full feature films being generated,” he says. “We’re close to a place where most of the content you’ll see online will be generated.”

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When my dad was sick, I started Googling grief. Then I couldn’t escape it.

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The Download: trapped by grief algorithms, and image AI privacy issues


I am a mostly visual thinker, and thoughts pose as scenes in the theater of my mind. When my many supportive family members, friends, and colleagues asked how I was doing, I’d see myself on a cliff, transfixed by an omniscient fog just past its edge. I’m there on the brink, with my parents and sisters, searching for a way down. In the scene, there is no sound or urgency and I am waiting for it to swallow me. I’m searching for shapes and navigational clues, but it’s so huge and gray and boundless. 

I wanted to take that fog and put it under a microscope. I started Googling the stages of grief, and books and academic research about loss, from the app on my iPhone, perusing personal disaster while I waited for coffee or watched Netflix. How will it feel? How will I manage it?

I started, intentionally and unintentionally, consuming people’s experiences of grief and tragedy through Instagram videos, various newsfeeds, and Twitter testimonials. It was as if the internet secretly teamed up with my compulsions and started indulging my own worst fantasies; the algorithms were a sort of priest, offering confession and communion. 

Yet with every search and click, I inadvertently created a sticky web of digital grief. Ultimately, it would prove nearly impossible to untangle myself. My mournful digital life was preserved in amber by the pernicious personalized algorithms that had deftly observed my mental preoccupations and offered me ever more cancer and loss. 

I got out—eventually. But why is it so hard to unsubscribe from and opt out of content that we don’t want, even when it’s harmful to us? 

I’m well aware of the power of algorithms—I’ve written about the mental-health impact of Instagram filters, the polarizing effect of Big Tech’s infatuation with engagement, and the strange ways that advertisers target specific audiences. But in my haze of panic and searching, I initially felt that my algorithms were a force for good. (Yes, I’m calling them “my” algorithms, because while I realize the code is uniform, the output is so intensely personal that they feel like mine.) They seemed to be working with me, helping me find stories of people managing tragedy, making me feel less alone and more capable. 

In my haze of panic and searching, I initially felt that my algorithms were a force for good. They seemed to be working with me, making me feel less alone and more capable. 

In reality, I was intimately and intensely experiencing the effects of an advertising-driven internet, which Ethan Zuckerman, the renowned internet ethicist and professor of public policy, information, and communication at the University of Massachusetts at Amherst, famously called “the Internet’s Original Sin” in a 2014 Atlantic piece. In the story, he explained the advertising model that brings revenue to content sites that are most equipped to target the right audience at the right time and at scale. This, of course, requires “moving deeper into the world of surveillance,” he wrote. This incentive structure is now known as “surveillance capitalism.” 

Understanding how exactly to maximize the engagement of each user on a platform is the formula for revenue, and it’s the foundation for the current economic model of the web. 

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The Download: trapped by grief algorithms, and image AI privacy issues

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The Download: trapped by grief algorithms, and image AI privacy issues


—Tate Ryan-Mosley, senior tech policy reporter

I’ve always been a super-Googler, coping with uncertainty by trying to learn as much as I can about whatever might be coming. That included my father’s throat cancer.

I started Googling the stages of grief, and books and academic research about loss, from the app on my iPhone, intentionally and unintentionally consuming people’s experiences of grief and tragedy through Instagram videos, various newsfeeds, and Twitter testimonials.

Yet with every search and click, I inadvertently created a sticky web of digital grief. Ultimately, it would prove nearly impossible to untangle myself from what the algorithms were serving me. I got out—eventually. But why is it so hard to unsubscribe from and opt out of content that we don’t want, even when it’s harmful to us? Read the full story.

AI models spit out photos of real people and copyrighted images

The news: Image generation models can be prompted to produce identifiable photos of real people, medical images, and copyrighted work by artists, according to new research. 

How they did it: Researchers prompted Stable Diffusion and Google’s Imagen with captions for images, such as a person’s name, many times. Then they analyzed whether any of the generated images matched original images in the model’s database. The group managed to extract over 100 replicas of images in the AI’s training set.

Why it matters: The finding could strengthen the case for artists who are currently suing AI companies for copyright violations, and could potentially threaten the human subjects’ privacy. It could also have implications for startups wanting to use generative AI models in health care, as it shows that these systems risk leaking sensitive private information. Read the full story.

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