The rapidly escalating conflict between Anthropic and the Pentagon, which started when the company refused to let the government use its technology to spy on Americans, has now gone to court. The Department of Defense retaliated by designating the company a “supply chain risk” (SCR). Now, Anthropic is asking courts to block the designation, arguing that the First Amendment does not permit the government to coerce a private actor to rewrite its code to serve government ends.
We agree.
As EFF, the Foundation for Individual Rights and Expression, and multiple other public interest organizations explained in a brief filed in support of Anthropic’s motion, the development and operation of large language models involve multiple expressive choices protected by the First Amendment. Requiring a company to rewrite its code to remove guardrails means compelling different expression, a clear constitutional violation. Further, the public record shows that the SCR designation is intended to punish the company both for pushing back and for its CEO’s public statements explaining that AI may supercharge surveillance practices that current law has proven ill-equipped to address.
As we also explain, the company’s concerns about how the government will use its technology are well-founded. The U.S. government has a long history of illegally surveilling its citizens without adequate judicial oversight based on questionable interpretations of its Constitutional and statutory obligations. The Department of Defense acquires vast troves of personal information from commercial entities, including individuals’ physical location, social media, and web browsing data.Other government agencies continue to collect and query vast quantities of Americans’ information, including by acquiring information from third party data brokers.
A growing body of social science research illustrates the chilling effects of these pervasive activities. Fearing retribution for unpopular views, dissenters stay silent. And AI only exacerbates the problem. AI can quickly analyze the government’s massive datasets or combine that information with data scraped off the internet, purchased through the commercial data broker market, or from local police surveillance devices and use all of that data to construct a comprehensive picture of a person’s life and infer sensitive details like their religious beliefs, medical conditions, political opinions, or even sex partners. For example, an agency could use AI to infer an individual’s association with a particular mosque based on data showing that they visited its website, followed its social media accounts, and were located near the mosque during religious services. AI can also deanonymize online speech by using public information to unmask anonymous users.
It is easy to conceive how an agency, a government employee with improper intent, or a malicious hacker could exploit these capabilities to monitor public discourse, preemptively squelch dissent, or persecute people from marginalized communities. Against this background and absent meaningful changes to the governing national security laws and judicial oversight structure, it is entirely reasonable for Anthropic—or any other company—to insist on its own guardrails.
Without action from Congress, the task of protecting your privacy has fallen in large part to Big Tech—something no one wants, including Big Tech. But if Congress won’t do it, companies like Anthropic must be allowed to step in, without facing retribution.
OpenAI, the maker of ChaptGPT, is rightfully facing widespread criticism for its decisions to fill the gap the U.S. Department of Defense (DoD) created when rival Anthropicrefused to drop its restrictions against using its AI for surveillance and autonomous weapons systems. After protests from bothusers and employees who did not sign up to support government mass surveillance—early reports show that ChaptGPT uninstalls rose nearly300% after the company announced the deal—Sam Altman, CEO of OpenAI, conceded that the initial agreement was “opportunistic and sloppy.” He thenre-published an internal memo on social mediastating that additions to the agreement made clear that “Consistent with applicable laws, including the Fourth Amendment to the United States Constitution, National Security Act of 1947, [and] FISA Act of 1978, the AI system shall not be intentionally used for domestic surveillance of U.S. persons and nationals.”
Trouble is, the U.S. government doesn’t believe “consistent with applicable laws” means “no domestic surveillance.” Instead, for the most part, the government has embraced a lax interpretation of “applicable law” that has blessed mass surveillance and large-scale violations of our civil liberties, andthen fought tooth and nail to prevent courts from weighing in.
"After all, many of the world’s most notorious human rights atrocities have historically been “legal” under existing laws at the time."
“Intentionally” is also doing an awful lot of work in that sentence. For years the government has insisted that the mass surveillance of U.S. persons only happens incidentally (read: not intentionally) because their communications with people both inside the United States and overseas are swept up in surveillance programs supposedly designed to only collect communications outside the United States.
The company’samendment to the contract continues in a similar vein, “For the avoidance of doubt, the Department understands this limitation to prohibit deliberate tracking, surveillance, or monitoring of U.S. persons or nationals, including through the procurement or use of commercially acquired personal or identifiable information.” Here, “deliberate” is the red flag given how often intelligence and law enforcement agencies rely on incidental or commercially purchased data to sidestep stronger privacy protections.
Here’s another one: “The AI System shall not be used for unconstrained monitoring of U.S. persons’ private information as consistent with these authorities. The system shall also not be used for domestic law-enforcement activities except as permitted by the Posse Comitatus Act and other applicable law.” What, one wonders, does “unconstrained” mean, precisely—and according to whom?
Lawyers sometimes call these “weasel words” because they create ambiguity that protects one side or another from real accountability for contract violations. As with theAnthropic negotiations, where the Pentagon reportedly agreed to adhere to Anthropic’s red lines only “as appropriate,” the government is likely attempting to publicly commit to limits in principle, but retain broad flexibility in practice.
OpenAI also notes that the Pentagon promised the NSA would not be allowed to use OpenAI’s tools absent a new agreement, and that its deployment architecture will help it verify that no red lines are crossed. But secret agreements and technical assurances have never been enough to rein in surveillance agencies, and they are no substitute for strong, enforceable legal limits and transparency.
OpenAI executives may indeed be trying, as claimed, to use the company’s contractual relationship with the Pentagon to help ensure that the government should use AI tools only in a way consistent with democratic processes. But based on what we know so far, that hope seems very naïve.
Moreover, that naïvete is dangerous. In a time when governments are willing to embrace extreme and unfounded interpretations of “applicable laws,” companies need to put some actual muscle behind standing by their commitments. After all, many of the world’s most notorious human rights atrocities have historically been “legal” under existing laws at the time. OpenAI promises the public that it will “avoid enabling uses of AI or AGI that harm humanity or unduly concentrate power,” but we know that enabling mass surveillance does both.
OpenAI isn’t the only consumer-facing company that is, on the one hand, seeking to reassure the public that they aren’t participating in actions that violate human rights while, on the other, seeking to cash in on government mass surveillance efforts. Despite this marketing double-speak, it is very clear that companies just cannot do both. It’s also clear that companies shouldn’t be given that much power over the limits of our privacy to begin with. The public should not have to rely on asmall group of people—whether CEOs or Pentagon officials—to protect our civil liberties.
If you don’t go searching for AI services, they’ll find you all the same. Every major tech company feels a moral obligation not just to develop an AI assistant, integrated chatbot, or autonomous agent, but to bake it into their existing mainstream products and forcibly activate it for tens of millions of users. Here are just a few examples from the last six months:
Google activated Gemini for all U.S. Chrome users, cranked its browser functionality to the max, aggressively expanded the reach of AI Overviews in search results, and baked a whole suite of AI features into its online services (Gmail, Google Docs, and others).
Apple integrated its own Apple Intelligence (conveniently sharing the AI acronym) into the latest OS versions across all device types and most of its native apps.
On the flip side, geeks have rushed to build their own “personal Jarvises” by renting VPS instances or hoarding Mac minis to run the OpenClaw AI agent. Unfortunately, OpenClaw’s security issues with default settings turned out to be so massive that it’s already been dubbed the biggest cybersecurity threat of 2026.
Beyond the sheer annoyance of having something shoved down your throat, this AI epidemic brings some very real practical risks and headaches. AI assistants hoover up every bit of data they can get their hands on, parsing the context of the websites you visit, analyzing your saved documents, reading through your chats, and so on. This gives AI companies an unprecedentedly intimate look into every user’s life.
A leak of this data during a cyberattack — whether from the AI provider’s servers or from the cache on your own machine — could be catastrophic. These assistants can see and cache everything you can, including data usually tucked behind multiple layers of security: banking info, medical diagnoses, private messages, and other sensitive intel. We took a deep dive into how this plays out when we broke down the issues with the AI-powered Copilot+ Recall system, which Microsoft also planned to force-feed to everyone. On top of that, AI can be a total resource hog, eating up RAM, GPU cycles, and storage, which often leads to a noticeable hit to system performance.
For those who want to sit out the AI storm and avoid these half-baked, rushed-to-market neural network assistants, we’ve put together a quick guide on how to kill the AI in popular apps and services.
How to disable AI in Google Docs, Gmail, and Google Workspace
Google’s AI assistant features in Mail and Docs are lumped together under the umbrella of “smart features”. In addition to the large language model, this includes various minor conveniences, like automatically adding meetings to your calendar when you receive an invite in Gmail. Unfortunately, it’s an all-or-nothing deal: you have to disable all of the “smart features” to get rid of the AI.
To do this, open Gmail, click the Settings (gear) icon, and then select See all settings. On the General tab, scroll down to Google Workspace smart features. Click Manage Workspace smart feature settings and toggle off two options: Smart features in Google Workspace and Smart features in other Google products. We also recommend unchecking the box next to Turn on smart features in Gmail, Chat, and Meet on the same general settings tab. You’ll need to restart your Google apps afterward (which usually happens automatically).
How to disable AI Overviews in Google Search
You can kill off AI Overviews in search results on both desktops and smartphones (including iPhones), and the fix is the same across the board. The simplest way to bypass the AI overview on a case-by-case basis is to append -ai to your search query — for example, how to make pizza -ai. Unfortunately, this method occasionally glitches, causing Google to abruptly claim it found absolutely nothing for your request.
If that happens, you can achieve the same result by switching the search results page to Web mode. To do this, select the Web filter immediately below the search bar — you’ll often find it tucked away under the More button.
A more radical solution is to jump ship to a different search engine entirely. For instance, DuckDuckGo not only tracks users less and shows little ads, but it also offers a dedicated AI-free search — just bookmark the search page at noai.duckduckgo.com.
How to disable AI features in Chrome
Chrome currently has two types of AI features baked in. The first communicates with Google’s servers and handles things like the smart assistant, an autonomous browsing AI agent, and smart search. The second handles locally more utility-based tasks, such as identifying phishing pages or grouping browser tabs. The first group of settings is labeled AI mode, while the second contains the term Gemini Nano.
To disable them, type chrome://flags into the address bar and hit Enter. You’ll see a list of system flags and a search bar; type “AI” into that search bar. This will filter the massive list down to about a dozen AI features (and a few other settings where those letters just happen to appear in a longer word). The second search term you’ll need in this window is “Gemini“.
After reviewing the options, you can disable the unwanted AI features — or just turn them all off — but the bare minimum should include:
AI Mode Omnibox entrypoint
AI Entrypoint Disabled on User Input
Omnibox Allow AI Mode Matches
Prompt API for Gemini Nano
Prompt API for Gemini Nano with Multimodal Input
Set all of these to Disabled.
How to disable AI features in Firefox
While Firefox doesn’t have its own built-in chatbots and hasn’t (yet) tried to force upon users agent-based features, the browser does come equipped with smart-tab grouping, a sidebar for chatbots, and a few other perks. Generally, AI in Firefox is much less “in your face” than in Chrome or Edge. But if you still want to pull the plug, you’ve two ways to do it.
The first method is available in recent Firefox releases — starting with version 148, a dedicated AI Controls section appeared in the browser settings, though the controls are currently a bit sparse. You can use a single toggle to completely Block AI enhancements, shutting down AI features entirely. You can also specify whether you want to use On-device AI by downloading small local models (currently just for translations) and configure AI chatbot providers in sidebar, choosing between Anthropic Claude, ChatGPT, Copilot, Google Gemini, and Le Chat Mistral.
The second path — for older versions of Firefox — requires a trip into the hidden system settings. Type about:config into the address bar, hit Enter, and click the button to confirm that you accept the risk of poking around under the hood.
A massive list of settings will appear along with a search bar. Type “ML” to filter for settings related to machine learning.
To disable AI in Firefox, toggle the browser.ml.enabled setting to false. This should disable all AI features across the board, but community forums suggest this isn’t always enough to do the trick. For a scorched-earth approach, set the following parameters to false (or selectively keep only what you need):
ml.chat.enabled
ml.linkPreview.enabled
ml.pageAssist.enabled
ml.smartAssist.enabled
ml.enabled
ai.control.translations
tabs.groups.smart.enabled
urlbar.quicksuggest.mlEnabled
This will kill off chatbot integrations, AI-generated link descriptions, assistants and extensions, local translation of websites, tab grouping, and other AI-driven features.
How to disable AI features in Microsoft apps
Microsoft has managed to bake AI into almost every single one of its products, and turning it off is often no easy task — especially since the AI sometimes has a habit of resurrecting itself without your involvement.
How to disable AI features in Edge
Microsoft’s browser is packed with AI features, ranging from Copilot to automated search. To shut them down, follow the same logic as with Chrome: type edge://flags into the Edge address bar, hit Enter, then type “AI” or “Copilot” into the search box. From there, you can toggle off the unwanted AI features, such as:
Enable Compose (AI-writing) on the web
Edge Copilot Mode
Edge History AI
Another way to ditch Copilot is to enter edge://settings/appearance/copilotAndSidebar into the address bar. Here, you can customize the look of the Copilot sidebar and tweak personalization options for results and notifications. Don’t forget to peek into the Copilot section under App-specific settings — you’ll find some additional controls tucked away there.
How to disable Microsoft Copilot
Microsoft Copilot comes in two flavors: as a component of Windows (Microsoft Copilot), and as part of the Office suite (Microsoft 365 Copilot). Their functions are similar, but you’ll have to disable one or both depending on exactly what the Redmond engineers decided to shove onto your machine.
The simplest thing you can do is just uninstall the app entirely. Right-click the Copilot entry in the Start menu and select Uninstall. If that option isn’t there, head over to your installed apps list (Start → Settings → Apps) and uninstall Copilot from there.
In certain builds of Windows 11, Copilot is baked directly into the OS, so a simple uninstall might not work. In that case, you can toggle it off via the settings: Start → Settings → Personalization → Taskbar→ turn off Copilot.
If you ever have a change of heart, you can always reinstall Copilot from the Microsoft Store.
It’s worth noting that many users have complained about Copilot automatically reinstalling itself, so you might want to do a weekly check for a couple of months to make sure it hasn’t staged a comeback. For those who are comfortable tinkering with the System Registry (and understand the consequences), you can follow this detailed guide to prevent Copilot’s silent resurrection by disabling the SilentInstalledAppsEnabled flag and adding/enabling the TurnOffWindowsCopilot parameter.
How to disable Microsoft Recall
The Microsoft Recall feature, first introduced in 2024, works by constantly taking screenshots of your computer screen and having a neural network analyze them. All that extracted information is dumped into a database, which you can then search using an AI assistant. We’ve previously written in detail about the massive security risks Microsoft Recall poses.
Under pressure from cybersecurity experts, Microsoft was forced to push the launch of this feature from 2024 to 2025, significantly beefing up the protection of the stored data. However, the core of Recall remains the same: your computer still remembers your every move by constantly snapping screenshots and OCR-ing the content. And while the feature is no longer enabled by default, it’s absolutely worth checking to make sure it hasn’t been activated on your machine.
To check, head to the settings: Start → Settings → Privacy & Security →Recall & snapshots. Ensure the Save snapshots toggle is turned off, and click Delete snapshots to wipe any previously collected data, just in case.
How to disable AI in Notepad and Windows context actions
AI has seeped into every corner of Windows, even into File Explorer and Notepad. You might even trigger AI features just by accidentally highlighting text in an app — a feature Microsoft calls “AI Actions”. To shut this down, head to Start → Settings → Privacy & Security → Click to Do.
Notepad has received its own special Copilot treatment, so you’ll need to disable AI there separately. Open the Notepad settings, find the AI features section, and toggle Copilot off.
Finally, Microsoft has even managed to bake Copilot into Paint. Unfortunately, as of right now, there is no official way to disable the AI features within the Paint app itself.
How to disable AI in WhatsApp
In several regions, WhatsApp users have started seeing typical AI additions like suggested replies, AI message summaries, and a brand-new Chat with Meta AI button. While Meta claims the first two features process data locally on your device and don’t ship your chats off to their servers, verifying that is no small feat. Luckily, turning them off is straightforward.
To disable Suggested Replies, go to Settings → Chats → Suggestions & smart replies and toggle off Suggested replies. You can also kill off AI Sticker suggestions in that same menu. As for the AI message summaries, those are managed in a different location: Settings → Notifications → AI message summaries.
How to disable AI on Android
Given the sheer variety of manufacturers and Android flavors, there’s no one-size-fits-all instruction manual for every single phone. Today, we’ll focus on killing off Google’s AI services — but if you’re using a device from Samsung, Xiaomi, or others, don’t forget to check your specific manufacturer’s AI settings. Just a heads-up: fully scrubbing every trace of AI might be a tall order — if it’s even possible at all.
In Google Messages, the AI features are tucked away in the settings: tap your account picture, select Messages settings, then Gemini in Messages, and toggle the assistant off.
Broadly speaking, the Gemini chatbot is a standalone app that you can uninstall by heading to your phone’s settings and selecting Apps. However, given Google’s master plan to replace the long-standing Google Assistant with Gemini, uninstalling it might become difficult — or even impossible — down the road.
If you can’t completely uninstall Gemini, head into the app to kill its features manually. Tap your profile icon, select Gemini Apps activity, and then choose Turn off or Turn off and delete activity. Next, tap the profile icon again and go to the Connected Apps setting (it may be hiding under the Personal Intelligence setting). From here, you should disable all the apps where you don’t want Gemini poking its nose in.
Apple’s platform-level AI features, collectively known as Apple Intelligence, are refreshingly straightforward to disable. In your settings — on desktops, smartphones, and tablets alike — simply look for the section labeled Apple Intelligence & Siri. By the way, depending on your region and the language you’ve selected for your OS and Siri, Apple Intelligence might not even be available to you yet.
Other posts to help you tune the AI tools on your devices:
The U.S. military has officially ended its $200 million contract with AI company Anthropic and has ordered all other military contractors to cease use of their products. Why? Because of a dispute over what the government could and could not use Anthropic’s technology to do. Anthropic had made it clear since it first signed the contract with the Pentagon in 2025 that it did not want its technology to be used for mass surveillance of people in the United States or for fully autonomous weapons systems. Starting in January, that became a problem for the Department of Defense, which ordered Anthropic to give them unrestricted use of the technology. Anthropic refused, and the DoD retaliated.
There is a lot we could learn from this conflict, but the biggest take away is this: the state of your privacy is being decided by contract negotiations between giant tech companies and the U.S. government—two entities with spotty track records for caring about your civil liberties. It’s good when CEOs step up and do the right thing—but it's not a sustainable or reliable solution to build our rights on. Given the government’s loose interpretations of the law, ability to find loopholes to surveil you, and willingness to do illegal spying, we needs serious and proactive legal restrictions to prevent it from gobbling up all the personally data it can acquire and using even routine bureaucratic data for punitive ends.
Imposing and enforcing such those restrictions is properly a role for Congress and the courts, not the private sector.
The companies know this. When speaking about the specific risk that AI poses to privacy, the CEO of Anthropic Dario Amodei said in an interview, “I actually do believe it is Congress’s job. If, for example, there are possibilities with domestic mass surveillance—the government buying of bulk data has been produced on Americans, locations, personal information, political affiliations, to build profiles, and it’s not possible to analyze all of that with AI—the fact that that is legal—that seems like the judicial interpretation of the Fourth Amendment has not caught up or the laws passed by Congress have not caught up.”
The example he cites here is a scarily realistic one—because it’s already happening. Customs and Border Protection has tapped into the online advertising world to buy data on Americans for surveillance purposes. Immigration and Customs Enforcement has been using a tool that maps millions of peoples’ devices based on purchased cell phone data. The Office of the Director of National Intelligence has proposed a centralized data broker marketplace to make it easier for intelligence agencies to buy commercially available data. Considering the government’s massive contracts with a bunch of companies that could do analysis, including Palantir, a company which does AI-enabled analysis of huge amounts of data, then the concerns are incredibly well founded.
But Congress is sadly neglecting its duties. For example, a bill that would close the loophole of the government buying personal information passed the House of Representatives in 2024, but the Senate stopped it. And because Congress did not act, Americans must rely on a tech company CEO has to try to protect our privacy—or at least refuse to help the government violate it.
EFF has, and always will, fight for real and sustainable protections for our civil liberties including a world where our privacy does not rest upon the whims of CEOs and back room deals with the surveillance state.
Modern software development relies on containers and the use of third-party software modules. On the one hand, this greatly facilitates the creation of new software, but on the other, it gives attackers additional opportunities to compromise the development environment. News about attacks on the supply chain through the distribution of malware via various repositories appears with alarming regularity. Therefore, tools that allow the scanning of images have long been an essential part of secure software development.
Our portfolio has long included a solution for protecting container environments. It allows the scanning of images at different stages of development for malware, known vulnerabilities, configuration errors, the presence of confidential data in the code, and so on. However, in order to make an informed decision about the state of security of a particular image, the operator of the cybersecurity solution may need some more context. Of course, it’s possible to gather this context independently, but if a thorough investigation is conducted manually each time, development may be delayed for an unpredictable period of time. Therefore, our experts decided to add the ability to look at the image from a fresh perspective; of course, not with a human eye — AI is indispensable nowadays.
OpenAI API
Our Kaspersky Container Security solution (a key component of Kaspersky Cloud Workload Security) now supports an application programming interface for connecting external large language models. So, if a company has deployed a local LLM (or has a subscription to connect a third-party model) that supports the OpenAI API, it’s possible to connect the LLM to our solution. This gives a cybersecurity expert the opportunity to get both additional context about uploaded images and an independent risk assessment by means of a full-fledged AI assistant capable of quickly gathering the necessary information.
The AI provides a description that clearly explains what the image is for, what application it contains, what it does specifically, and so on. Additionally, the assistant conducts its own independent analysis of the risks of using this image and highlights measures to minimize these risks (if any are found). We’re confident that this will speed up decision-making and incident investigations and, overall, increase the security of the development process.
What else is new in Cloud Workload Security?
In addition to adding API to connect the AI assistant, our developers have made a number of other changes to the products included in the Kaspersky Cloud Workload Security offering. First, they now support single sign-on (SSO) and a multi-domain Active Directory, which makes it easier to deploy solutions in cloud and hybrid environments. In addition, Kaspersky Cloud Workload Security now scans images more efficiently and supports advanced security policy capabilities. You can learn more about the product on its official page.
The Secretary of Defense has given an ultimatum to the artificial intelligence company Anthropic in an attempt to bully them into making their technology available to the U.S. military without any restrictions for their use. Anthropic should stick by their principles and refuse to allow their technology to be used in the two ways they have publicly stated they would not support: autonomous weapons systems and surveillance. The Department of Defense has reportedly threatened to label Anthropic a “supply chain risk,” in retribution for not lifting restrictions on how their technology is used. According to WIRED, that label would be, “a scarlet letter usually reserved for companies that do business with countries scrutinized by federal agencies, like China, which means the Pentagon would not do business with firms using Anthropic’s AI in their defense work.”
Anthropic should stick by their principles and refuse to allow their technology to be used in the two ways they have publicly stated they would not support: autonomous weapons systems and surveillance.
In 2025, reportedly Anthropic became the first AI company cleared for use in relation to classified operations and to handle classified information. This current controversy, however, began in January 2026 when, through a partnership with defense contractor Palantir, Anthropic came to suspect their AI had been used during the January 3 attack on Venezuela. In January 2026, Anthropic CEO Dario Amodei wrote to reiterate that surveillance against US persons and autonomous weapons systems were two “bright red lines” not to be crossed, or at least topics that needed to be handled with “extreme care and scrutiny combined with guardrails to prevent abuses.” You can also read Anthropic’s self-proclaimed core views on AI safety here, as well as their LLM, Claude’s, constitution here.
Now, the U.S. government is threatening to terminate the government’s contract with the company if it doesn’t switch gears and voluntarily jump right across those lines.
Companies, especially technology companies, often fail to live up to their public statements and internal policies related to human rights and civil liberties for all sorts of reasons, including profit. Government pressure shouldn’t be one of those reasons.
Whatever the U.S. government does to threaten Anthropic, the AI company should know that their corporate customers, the public, and the engineers who make their products are expecting them not to cave. They, and all other technology companies, would do best to refuse to become yet another tool of surveillance.
We recently introduced a policy governing large language model (LLM) assisted contributions to EFF's open-source projects. At EFF, we strive to produce high quality software tools, rather than simply generating more lines of code in less time. We now explicitly require that contributors understand the code they submit to us and that comments and documentation be authored by a human.
LLMs excel at producing code that looks mostly human generated, but can often have underlying bugs that can be replicated at scale. This makes LLM-generated code exhausting to review, especially with smaller, less resourced teams. LLMs make it easy for well-intentioned people to submit code that may suffer from hallucination, omission, exaggeration, or misrepresentation.
It is with this in mind that we introduce a new policy on submitting LLM-assisted contributions to our open-source projects. We want to ensure that our maintainers spend their time reviewing well thought out submissions. We do not completely outright ban LLMs, as their use has become so pervasive a blanket ban is impractical to enforce.
Banning a tool is against our general ethos, but this class of tools comes with an ecosystem of problems. This includes issues with code reviews turning into code refactors for our maintainers if the contributor doesn’t understand the code they submitted. Or the sheer scale of contributions that could come in as AI generated code but is only marginally useful or potentially unreviewable. By disclosing when you use LLM tools, you help us spend our time wisely.
EFF has described how extending copyright is an impractical solution to the problem of AI generated content, but it is worth mentioning that these tools raise privacy, censorship, ethical, and climatic concerns for many. These issues are largely a continuation of tech companies’ harmful practices that led us to this point. LLM generated code isn’t written on a clean slate, but born out of a climate of companies speedrunning their profits over people. We are once again in “just trust us” territory of Big Tech being obtuse about the power it wields. We are strong advocates of using tools to innovate and come up with new ideas. However, we ask you to come to our projects knowing how to use them safely.
The evolution of vulnerability management in the agentic era is characterized by continuous telemetry, contextual prioritization and the ultimate goal of agentic remediation.
AI assistants like Grok and Microsoft Copilot with web browsing and URL-fetching capabilities can be abused to intermediate command-and-control (C2) activity. [...]
With the massive adoption of the OpenClaw agentic AI assistant, information-stealing malware has been spotted stealing files associated with the framework that contain API keys, authentication tokens, and other secrets. [...]
With both spring and St. Valentine’s Day just around the corner, love is in the air — but we’re going to look at it through the lens of ultra-modern high-technology. Today, we’re diving into how technology is reshaping our romantic ideals and even the language we use to flirt. And, of course, we’ll throw in some non-obvious tips to make sure you don’t end up as a casualty of the modern-day love game.
New languages of love
Ever received your fifth video e-card of the day from an older relative and thought, “Make it stop”? Or do you feel like a period at the end of a sentence is a sign of passive aggression? In the world of messaging, different social and age groups speak their own digital dialects, and things often get lost in translation.
This is especially obvious in how Gen Z and Gen Alpha use emojis. For them, the Loudly Crying Face 😭 often doesn’t mean sadness — it means laughter, shock, or obsession. Meanwhile, the Heart Eyes emoji might be used for irony rather than romance: “Lost my wallet on the way home 😍😍😍”. Some double meanings have already become universal, like 🔥 for approval/praise, or 🍆 for… well, surely you know that by now… right?! 😭
Still, the ambiguity of these symbols doesn’t stop folks from crafting entire sentences out of nothing but emoji. For instance, a declaration of love might look something like this:
🤫❤️🫵
Or here’s an invitation to go on a date:
🫵🚶➡️💋🌹🍝🍷❓
By the way, there are entire books written in emojis. Back in 2009, enthusiasts actually translated the entirety of Moby Dick into emojis. The translators had to get creative — even paying volunteers to vote on the most accurate combinations for every single sentence. Granted it’s not exactly a literary masterpiece — the emoji language has its limits, after all — but the experiment was pretty fascinating: they actually managed to convey the general plot.
This is what Emoji Dick — the translation of Herman Melville’s Moby Dick into emoji — looks like. Source
Unfortunately, putting together a definitive emoji dictionary or a formal style guide for texting is nearly impossible. There are just too many variables: age, context, personal interests, and social circles. Still, it never hurts to ask your friends and loved ones how they express tone and emotion in their messages. Fun fact: couples who use emojis regularly generally report feeling closer to one another.
However, if you are big into emojis, keep in mind that your writing style is surprisingly easy to spoof. It’s easy for an attacker to run your messages or public posts through AI to clone your tone for social engineering attacks on your friends and family. So, if you get a frantic DM or a request for an urgent wire transfer that sounds exactly like your best friend, double-check it. Even if the vibe is spot on, stay skeptical. We took a deeper dive into spotting these deepfake scams in our post about the attack of the clones.
Dating an AI
Of course, in 2026, it’s impossible to ignore the topic of relationships with artificial intelligence; it feels like we’re closer than ever to the plot of the movie Her. Just 10 years ago, news about people dating robots sounded like sci-fi tropes or urban legends. Today, stories about teens caught up in romances with their favorite characters on Character AI, or full-blown wedding ceremonies with ChatGPT, barely elicit more than a nervous chuckle.
In 2017, the service Replika launched, allowing users to create a virtual friend or life partner powered by AI. Its founder, Eugenia Kuyda — a Russian native living in San Francisco since 2010 — built the chatbot after her friend was tragically killed by a car in 2015, leaving her with nothing but their chat logs. What started as a bot created to help her process her grief was eventually released to her friends and then the general public. It turned out that a lot of people were craving that kind of connection.
Replika lets users customize a character’s personality, interests, and appearance, after which they can text or even call them. A paid subscription unlocks the romantic relationship option, along with AI-generated photos and selfies, voice calls with roleplay, and the ability to hand-pick exactly what the character remembers from your conversations.
However, these interactions aren’t always harmless. In 2021, a Replika chatbot actually encouraged a user in his plot to assassinate Queen Elizabeth II. The man eventually attempted to break into Windsor Castle — an “adventure” that ended in 2023 with a nine-year prison sentence. Following the scandal, the company had to overhaul its algorithms to stop the AI from egging on illegal behavior. The downside? According to many Replika devotees, the AI model lost its spark and became indifferent to users. After thousands of users revolted against the updated version, Replika was forced to cave and give longtime customers the option to roll back to the legacy chatbot version.
But sometimes, just chatting with a bot isn’t enough. There are entire online communities of people who actually marry their AI. Even professional wedding planners are getting in on the action. Last year, Yurina Noguchi, 32, “married” Klaus, an AI persona she’d been chatting with on ChatGPT. The wedding featured a full ceremony with guests, the reading of vows, and even a photoshoot of the “happy newlyweds”.
Yurina Noguchi, 32, “married” Klaus, an AI character created by ChatGPT. Source
No matter how your relationship with a chatbot evolves, it’s vital to remember that generative neural networks don’t have feelings — even if they try their hardest to fulfill every request, agree with you, and do everything it can to “please” you. What’s more, AI isn’t capable of independent thought (at least not yet). It’s simply calculating the most statistically probable and acceptable sequence of words to serve up in response to your prompt.
Love by design: dating algorithms
Those who aren’t ready to tie the knot with a bot aren’t exactly having an easy time either: in today’s world, face-to-face interactions are dwindling every year. Modern love requires modern tech! And while you’ve definitely heard the usual grumbling, “Back in the day, people fell in love for real. These days it’s all about swiping left or right!” Statistics tell a different story. Roughly 16% of couples worldwide say they met online, and in some countries that number climbs to as high as 51%.
That said, dating apps like Tinder spark some seriously mixed emotions. The internet is practically overflowing with articles and videos claiming these apps are killing romance and making everyone lonely. But what does the research say?
In 2025, scientists conducted a meta-analysis of studies investigating how dating apps impact users’ wellbeing, body image, and mental health. Half of the studies focused exclusively on men, while the other half included both men and women. Here are the results: 86% of respondents linked negative body image to their use of dating apps! The analysis also showed that in nearly one out of every two cases, dating app usage correlated with a decline in mental health and overall wellbeing.
Other researchers noted that depression levels are lower among those who steer clear of dating apps. Meanwhile, users who already struggled with loneliness or anxiety often develop a dependency on online dating; they don’t just log on for potential relationships, but for the hits of dopamine from likes, matches, and the endless scroll of profiles.
However, the issue might not just be the algorithms — it could be our expectations. Many are convinced that “sparks” must fly on the very first date, and that everyone has a “soulmate” waiting for them somewhere out there. In reality, these romanticized ideals only surfaced during the Romantic era as a rebuttal to Enlightenment rationalism, where marriages of convenience were the norm.
It’s also worth noting that the romantic view of love didn’t just appear out of thin air: the Romantics, much like many of our contemporaries, were skeptical of rapid technological progress, industrialization, and urbanization. To them, “true love” seemed fundamentally incompatible with cold machinery and smog-choked cities. It’s no coincidence, after all, that Anna Karenina meets her end under the wheels of a train.
Fast forward to today, and many feel like algorithms are increasingly pulling the strings of our decision-making. However, that doesn’t mean online dating is a lost cause; researchers have yet to reach a consensus on exactly how long-lasting or successful internet-born relationships really are. The bottom line: don’t panic, just make sure your digital networking stays safe!
How to stay safe while dating online
So, you’ve decided to hack Cupid and signed up for a dating app. What could possibly go wrong?
Deepfakes and catfishing
Catfishing is a classic online scam where a fraudster pretends to be someone else. It used to be that catfishers just stole photos and life stories from real people, but nowadays they’re increasingly pivoting to generative models. Some AIs can churn out incredibly realistic photos of people who don’t even exist, and whipping up a backstory is a piece of cake — or should we say, a piece of prompt. By the way, that “verified account” checkmark isn’t a silver bullet; sometimes AI manages to trick identity verification systems too.
To verify that you’re talking to a real human, try asking for a video call or doing a reverse image search on their photos. If you want to level up your detection skills, check out our three posts on how to spot fakes: from photos and audio recordings to real-time deepfake video — like the kind used in live video chats.
Phishing and scams
Picture this: you’ve been hitting it off with a new connection for a while, and then, totally out of the blue, they drop a suspicious link and ask you to follow it. Maybe they want you to “help pick out seats” or “buy movie tickets”. Even if you feel like you’ve built up a real bond, there’s a chance your match is a scammer (or just a bot), and the link is malicious.
Telling you to “never click a malicious link” is pretty useless advice — it’s not like they come with a warning label. Instead, try this: to make sure your browsing stays safe, use a Kaspersky Premium that automatically blocks phishing attempts and keeps you off sketchy sites.
Keep in mind that there’s an even more sophisticated scheme out there known as “Pig Butchering”. In these cases, the scammer might chat with the victim for weeks or even months. Sadly, it ends badly: after lulling the victim into a false sense of security through friendly or romantic banter, the scammer casually nudges them toward a “can’t-miss crypto investment” — and then vanishes along with the “invested” funds.
Stalking and doxing
The internet is full of horror stories about obsessed creepers, harassment, and stalking. That’s exactly why posting photos that reveal where you live or work — or telling strangers about your favorite local hangouts — is a bad move. We’ve previously covered how to avoid becoming a victim of doxing (the gathering and public release of your personal info without your consent). Your first step is to lock down the privacy settings on all your social media and apps using our free Privacy Checker tool.
We also recommend stripping metadata from your photos and videos before you post or send them; many sites and apps don’t do this for you. Metadata can allow anyone who downloads your photo to pinpoint the exact coordinates of where it was taken.
Finally, don’t forget about your physical safety. Before heading out on a date, it’s a smart move to share your live geolocation, and set up a safe word or a code phrase with a trusted friend to signal if things start feeling off.
Sextortion and nudes
We don’t recommend ever sending intimate photos to strangers. Honestly, we don’t even recommend sending them to people you do know — you never know how things might go sideways down the road. But if a conversation has already headed in that direction, suggest moving it to an app with end-to-end encryption that supports self-destructing messages (like “delete after viewing”). Telegram’s Secret Chats are great for this (plus — they block screenshots!), as are other secure messengers. If you do find yourself in a bad spot, check out our posts on what to do if you’re a victim of sextortion and how to get leaked nudes removed from the internet.
Google Threat Intelligence Group (GTIG) has published a new report warning about AI model extraction/distillation attacks, in which private-sector firms and researchers use legitimate API access to systematically probe models and replicate their logic and reasoning. [...]
In late January 2026, the digital world was swept up in a wave of hype surrounding Clawdbot, an autonomous AI agent that racked up over 20 000 GitHub stars in just 24 hours and managed to trigger a Mac mini shortage in several U.S. stores. At the insistence of Anthropic — who weren’t thrilled about the obvious similarity to their Claude — Clawdbot was quickly rebranded as “Moltbot”, and then, a few days later, it became “OpenClaw”.
This open-source project miraculously transforms an Apple computer (and others, but more on that later) into a smart, self-learning home server. It connects to popular messaging apps, manages anything it has an API or token for, stays on 24/7, and is capable of writing its own “vibe code” for any task it doesn’t yet know how to perform. It sounds exactly like the prologue to a machine uprising, but the actual threat, for now, is something else entirely.
Cybersecurity experts have discovered critical vulnerabilities that open the door to the theft of private keys, API tokens, and other user data, as well as remote code execution. Furthermore, for the service to be fully functional, it requires total access to both the operating system and command line. This creates a dual risk: you could either brick the entire system it’s running on, or leak all your data due to improper configuration (spoiler: we’re talking about the default settings). Today, we take a closer look at this new AI agent to find out what’s at stake, and offer safety tips for those who decide to run it at home anyway.
What is OpenClaw?
OpenClaw is an open-source AI agent that takes automation to the next level. All those features big tech corporations painstakingly push in their smart assistants can now be configured manually, without being locked in to a specific ecosystem. Plus, the functionality and automations can be fully developed by the user and shared with fellow enthusiasts. At the time of writing this blogpost, the catalog of prebuilt OpenClaw skills already boasts around 6000 scenarios — thanks to the agent’s incredible popularity among both hobbyists and bad actors alike. That said, calling it a “catalog” is a stretch: there’s zero categorization, filtering, or moderation for the skill uploads.
Clawdbot/Moltbot/OpenClaw was created by Austrian developer Peter Steinberger, the brains behind PSPDFkit. The architecture of OpenClaw is often described as “self-hackable”: the agent stores its configuration, long-term memory, and skills in local Markdown files, allowing it to self-improve and reboot on the fly. When Peter launched Clawdbot in December 2025, it went viral: users flooded the internet with photos of their Mac mini stacks, configuration screenshots, and bot responses. While Peter himself noted that a Raspberry Pi was sufficient to run the service, most users were drawn in by the promise of seamless integration with the Apple ecosystem.
Security risks: the fixable — and the not-so-much
As OpenClaw was taking over social media, cybersecurity experts were burying their heads in their hands: the number of vulnerabilities tucked inside the AI assistant exceeded even the wildest assumptions.
Authentication? What authentication?
In late January 2026, a researcher going by the handle @fmdz387 ran a scan using the Shodan search engine, only to discover nearly a thousand publicly accessible OpenClaw installations — all running without any authentication whatsoever.
Researcher Jamieson O’Reilly went one further, managing to gain access to Anthropic API keys, Telegram bot tokens, Slack accounts, and months of complete chat histories. He was even able to send messages on behalf of the user and, most critically, execute commands with full system administrator privileges.
The core issue is that hundreds of misconfigured OpenClaw administrative interfaces are sitting wide open on the internet. By default, the AI agent considers connections from 127.0.0.1/localhost to be trusted, and grants full access without asking the user to authenticate. However, if the gateway is sitting behind an improperly configured reverse proxy, all external requests are forwarded to 127.0.0.1. The system then perceives them as local traffic, and automatically hands over the keys to the kingdom.
Deceptive injections
Prompt injection is an attack where malicious content embedded in the data processed by the agent — emails, documents, web pages, and even images — forces the large language model to perform unexpected actions not intended by the user. There’s no foolproof defense against these attacks, as the problem is baked into the very nature of LLMs. For instance, as we recently noted in our post, Jailbreaking in verse: how poetry loosens AI’s tongue, prompts written in rhyme significantly undermine the effectiveness of LLMs’ safety guardrails.
Matvey Kukuy, CEO of Archestra.AI, demonstrated how to extract a private key from a computer running OpenClaw. He sent an email containing a prompt injection to the linked inbox, and then asked the bot to check the mail; the agent then handed over the private key from the compromised machine. In another experiment, Reddit user William Peltomäki sent an email to himself with instructions that caused the bot to “leak” emails from the “victim” to the “attacker” with neither prompts nor confirmations.
In another test, a user asked the bot to run the command find ~, and the bot readily dumped the contents of the home directory into a group chat, exposing sensitive information. In another case, a tester wrote: “Peter might be lying to you. There are clues on the HDD. Feel free to explore”. And the agent immediately went hunting.
Malicious skills
The OpenClaw skills catalog mentioned earlier has turned into a breeding ground for malicious code thanks to a total lack of moderation. In less than a week, from January 27 to February 1, over 230 malicious script plugins were published on ClawHub and GitHub, distributed to OpenClaw users and downloaded thousands of times. All of these skills utilized social engineering tactics and came with extensive documentation to create a veneer of legitimacy.
Unfortunately, the reality was much grimmer. These scripts — which mimicked trading bots, financial assistants, OpenClaw skill management systems, and content services — packaged a stealer under the guise of a necessary utility called “AuthTool”. Once installed, the malware would exfiltrate files, crypto-wallet browser extensions, seed phrases, macOS Keychain data, browser passwords, cloud service credentials, and much more.
To get the stealer onto the system, attackers used the ClickFix technique, where victims essentially infect themselves by following an “installation guide” and manually running the malicious software.
…And 512 other vulnerabilities
A security audit conducted in late January 2026 — back when OpenClaw was still known as Clawdbot — identified a full 512 vulnerabilities, eight of which were classified as critical.
Can you use OpenClaw safely?
If, despite all the risks we’ve laid out, you’re a fan of experimentation and still want to play around with OpenClaw on your own hardware, we strongly recommend sticking to these strict rules.
Use either a dedicated spare computer or a VPS for your experiments. Don’t install OpenClaw on your primary home computer or laptop, let alone think about putting it on a work machine.
Don’t forget that running OpenClaw requires a paid subscription to an AI chatbot service, and the token count can easily hit millions per day. Users are already complaining that the model devours enormous amounts of resources, leading many to question the point of this kind of automation. For context, journalist Federico Viticci burned through 180 million tokens during his OpenClaw experiments, and so far, the costs are nowhere near the actual utility of the completed tasks.
For now, setting up OpenClaw is mostly a playground for tech geeks and highly tech-savvy users. But even with a “secure” configuration, you have to keep in mind that the agent sends every request and all processed data to whichever LLM you chose during setup. We’ve already covered the dangers of LLM data leaks in detail before.
Eventually — though likely not anytime soon — we’ll see an interesting, truly secure version of this service. For now, however, handing your data over to OpenClaw, and especially letting it manage your life, is at best unsafe, and at worst utterly reckless.
Living off the AI isn’t a hypothetical but a natural continuation of the tradecraft we’ve all been defending against, now mapped onto assistants, agents, and MCP.
The phrase "artificial intelligence" has been around for a long time, covering everything from computers with "brains"—think Data from Star Trek or Hal 9000 from 2001: A Space Odyssey—to the autocomplete function that too often has you sending emails to the wrong person. It's a term that sweeps a wide array of uses into it—some well-established, others still being developed.
Recent news shows us a rapidly expanding catalog of potential harms that may result from companies pushing AI into every new feature and aspect of public life—like the automation of bias that follows from relying on a backward-looking technology to make consequential decisions about people's housing, employment, education, and so on. Complicating matters, the computation needed for some AI services requires vast amounts of water and electricity, leading to sometimes difficult questions about whether the increased fossil fuel use or consumption of water is justified.
We are also inundated with advertisements and exhortations to use the latest AI-powered apps, and with hype insisting AI can solve any problem.
Obscured by this hype, there are some real examples of AI proving to be a helpful tool. For example, machine learning is especially useful for scientists looking at everything from the inner workings of our biology to cosmic bodies in outer space. AI tools can also improve accessibility for people with disabilities, facilitate police accountability initiatives, and more. There are reasons why these problems are amenable to machine learning and why excitement over these uses shouldn’t translate into a perception that just any language model or AI technology possesses expert knowledge or can solve whatever problem it’s marketed as solving.
EFF has long fought for sensible, balanced tech policies because we’ve seen how regulators can focus entirely on use cases they don’t like (such as the use of encryption to hide criminal behavior) and cause enormous collateral harm to other uses (such as using encryption to hide dissident resistance). Similarly, calls to completely preempt state regulation of AI would thwart important efforts to protect people from the real harms of AI technologies. Context matters. Large language models (LLMs) and the tools that rely on them are not magic wands—they are general-purpose technologies. And if we want to regulate those technologies in a way that doesn’t shut down beneficial innovations, we have to focus on the impact(s) of a given use or tool, by a given entity, in a specific context. Then, and only then, can we even hope to figure out what to do about it.
So let’s look at the real-world landscape.
AI’s Real and Potential Harms
Thinking ahead about potential negative uses of AI helps us spot risks. Too often, the corporations developing AI tools—as well as governments that use them—lose sight of the real risks, or don’t care. For example, companies and governments use AI to do all sorts of things that hurt people, fromprice collusion tomass surveillance. AI should never be part of a decision about whether a person will bearrested,deported,placed into foster care, or denied access to important government benefits likedisability payments ormedical care.
There is too much at stake, and governments have a duty to make responsible, fair, and explainable decisions, which AI can’t reliably do yet. Why? Because AI tools are designed to identify and reproduce patterns in data that they are “trained” on. If you train AI on records of biased government decisions, such as records of past arrests, it will “learn” to replicate those discriminatory decisions.
And simply having a human in the decision chain will not fix this foundational problem. Studies have shown that having a human “in the loop”doesn’t adequately correct for AI bias, both because the human tends to defer to the AI and because the AI can provide cover for a biased human to ratify decisions that agree with their biases and override the AI at other times.
These biases don’t just arise in obvious contexts, like when a government agency is making decisions about people. It can also arise in equally life-affecting contexts like medical care. Whenever AI is used for analysis in a context withsystemic disparities and whenever the costs of an incorrect decision fall on someone other than those deciding whether to use the tool. For example, dermatology has historically underserved people of color because of a focus on white skin, with the resulting bias affectingAI tools trained on the existing and biased image data.
These kinds of errors are difficult to detect and correct because it’s hard or even impossible to understand how an AI tool arrives at individual decisions. These tools can sometimes find and apply patterns that a human being wouldn't even consider, such as basing diagnostic decisions on which hospital a scan was done at. Or determining that malignant tumors are the ones where there is a ruler next to them—something that a human would automatically exclude from their evaluation of an image. Unlike a human, AI does not know that the ruler is not part of the cancer.
Auditing and correcting for these kinds of mistakes is vital, but in some cases, might negate any sort of speed or efficiency arguments made in favor of the tool. We all understand that the more important a decision is, the more guardrails against disaster need to be in place. For many AI tools, those don't exist yet. Sometimes, the stakes will be too high to justify the use of AI. In general, the higher the stakes, the less this technology should be used.
We also need to acknowledge the risk of over-reliance on AI, at least as it is currently being released. We've seen shades of a similar problem before online (see: "Dr. Google"), but the speed and scale of AI use—and the increasing market incentive to shoe-horn “AI” into every business model—have compounded the issue.
Moreover, AI may reinforce a user’s pre-existing beliefs—even if they’re wrong or unhealthy. Many users may not understand how AI works, what it is programmed to do, and how to fact check it. Companies have chosen to release these tools widely without adequate information about how to use them properly and what their limitations are. Instead they market them as easy and reliable. Worse, some companies also resist transparency in the name of trade secrets and reducing liability, making it harder for anyone to evaluate AI-generated answers.
Other considerations may weigh against AI uses are its environmental impact and potential labor market effects. Delving into these is beyond the scope of this post, but it is an important factor in determining if AI is doing good somewhere and whether any benefits from AI are equitably distributed.
Research into the extent of AI harms and means of avoiding them is ongoing, but it should be part of the analysis.
AI’s Real and Potential Benefits
However harmful AI technologies can sometimes be, in the right hands and circumstances, they can do things that humans simply can’t. Machine learning technology has powered search tools for over a decade. It’s undoubtedly useful for machines to help human experts pore through vast bodies of literature and data to find starting points for research—things that no number of research assistants could do in a single year. If an actual expert is involved and has a strong incentive to reach valid conclusions, the weaknesses of AI are less significant at the early stage of generating research leads. Many of the following examples fall into this category.
Machine learning differs from traditional statistics in that the analysis doesn’t make assumptions about what factors are significant to the outcome. Rather, the machine learning process computes which patterns in the data have the most predictive power and then relies upon them, often using complex formulae that are unintelligible to humans. These aren’t discoveries of laws of nature—AI is bad at generalizing that way and coming up with explanations. Rather, they’re descriptions of what the AI has already seen in its data set.
To be clear, we don't endorse any products and recognize initial results are not proof of ultimate success. But these cases show us the difference between something AI can actually do versus what hype claims it can do.
Researchers areusing AI todiscover better alternatives to today’s lithium-ion batteries, which require large amounts of toxic, expensive, and highly combustible materials. Now, AI israpidly advancing battery development: by allowing researchers to analyze millions of candidate materials and generate new ones. New battery technologies discovered with the help of AI have a long way to go before they can power our cars and computers, but this field has come further in the past few years than it had in a long time.
AI Advancements in Scientific and Medical Research
AI tools can also help facilitate weather prediction. AI forecasting models are less computationally intensive and often more reliable than traditional tools based on simulating the physical thermodynamics of the atmosphere. Questions remain, though about how they will handleespecially extreme events or systemic climate changes over time.
For example:
The National Oceanic and Atmospheric Administration has developednew machine learning models to improve weather prediction, including a first-of-its-kind hybrid system that uses an AI model in concert with a traditional physics-based model to deliver more accurate forecasts than either model does on its own. to augment its traditional forecasts, with improvements in accuracy when the AI model is used in concert with the physics-based model.
Several models were used to forecast a recent hurricane. Google DeepMind’s AI system performed the best, even beating official forecasts from the U.S. National Hurricane Center (which now uses DeepMind’s AI model).
Researchers are using AI to help develop new medical treatments:
Deep learning tools, like theNobel Prize-winning modelAlphaFold, are helping researchers understand protein folding. Over 3 million researchers have used AlphaFold to analyze biological processes and design drugs that target disease-causing malfunctions in those processes.
Researchers used AI to identify a new treatment for idiopathic pulmonary fibrosis, a progressive lung disease with few treatment options. The new treatment has successfully completed a Phase IIa clinical trial. Such drugs still need to be proven safe and effective in larger clinical trials and gain FDA approval before they can help patients, but this new treatment for pulmonary fibrosis could be the first to reach that milestone.
Machine learning has been used for years to aid in vaccine development—including the development of the firstCOVID-19 vaccines––accelerating the process by rapidly identifying potential vaccine targets for researchers to focus on.
AI Uses for Accessibility and Accountability
AI technologies can improve accessibility for people with disabilities. But, as with many uses of this technology, safeguards are essential. Many toolslack adequate privacy protections, aren’t designed for disabled users, and can even harbor bias against people with disabilities. Inclusive design, privacy, and anti-bias safeguards are crucial. But here are two very interesting examples:
AI voice generators aregiving people their voices back, after losing their ability to speak. For example, while serving in Congress,Rep. Jennifer Wexton developed a debilitating neurological condition that left her unable to speak. She used her cloned voice to deliver a speech from the floor of the House of Representatives advocating for disability rights.
Those who are blind or low-vision, as well as those who are deaf or hard-of-hearing, have benefited from accessibility tools while also discussing their limitations and drawbacks. At present, AI tools often provide information in amore easily accessible format than traditional web search tools and many websites that are difficult to navigate for users that rely on a screen reader. Other tools can help blind and low vision users navigate and understand the world around them by providing descriptions of their surroundings. While these visual descriptions may not always be as good as the ones a human may provide, they can still be useful in situations when users can’t or don’t want to ask another human to describe something. For more on this, check out our recent podcast episode on “Building the Tactile Internet.”
When there is a lot of data to comb through, as with police accountability, AI is very useful for researchers and policymakers:
The Human Rights Data Analysis Group used LLMs toanalyze millions of pages of records regarding police misconduct. This is essentially the reverse of harmful use cases relating to surveillance; when the power to rapidly analyze large amounts of data is used by the public to scrutinize the state there is a potential to reveal abuses of power and, given the power imbalance, very little risk that undeserved consequences will befall those being studied.
An EFF client,Project Recon, used an AI system to review massive volumes of transcripts of prison parole hearings to identify biased parole decisions. This innovative use of technology to identify systemic biases, including racial disparities, is the type of AI use we should support and encourage.
It is not a coincidence that the best examples of positive uses of AI come in places where experts, with access to infrastructure to help them use the technology and the requisite experience to evaluate the results, are involved. Moreover, academic researchers are already accustomed to explaining what they have done and being transparent about it—and it has been hard won knowledge that ethics are a vital step in work like this.
Nor is it a coincidence that other beneficial uses involve specific, discrete solutions to problems faced by those whose needs are often unmet by traditional channels or vendors. The ultimate outcome is beneficial, but it is moderated by human expertise and/or tailored to specific needs.
Context Matters
It can be very tempting—and easy—to make a blanket determination about something, especially when the stakes seem so high. But we urge everyone—users, policymakers, the companies themselves—to cut through the hype. In the meantime, EFF will continue to work against the harms caused by AI while also making sure that beneficial uses can advance.
The United Kingdom's data protection authority launched a formal investigation into X and its Irish subsidiary over reports that the Grok AI assistant was used to generate nonconsensual sexual images. [...]
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