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Tag: algorithms

  • My Memories Are Just Meta’s Training Data Now

    My Memories Are Just Meta’s Training Data Now

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    In R. C. Sherriff’s novel The Hopkins Manuscript, readers are transported to a world 800 years after a cataclysmic event ended Western civilization. In pursuit of clues about a blank spot in their planet’s history, scientists belonging to a new world order discover diary entries in a swamp-infested wasteland formerly known as England. For the inhabitants of this new empire, it is only through this record of a retired school teacher’s humdrum rural life, his petty vanities and attempts to breed prize-winning chickens, that they begin to learn about 20th-century Britain.

    If I were to teach futuristic beings about life on earth, I once believed I could produce a time capsule more profound than Sherriff’s small-minded protagonist, Edgar Hopkins. But scrolling through my decade-old Facebook posts this week, I was presented with the possibility that my legacy may be even more drab.

    Earlier this month, Meta announced that my teenage status updates were exactly the kind of content it wants to pass on to future generations of artificial intelligence. From June 26, old public posts, holiday photos, and even the names of millions of Facebook and Instagram users around the world would effectively be treated as a time capsule of humanity and transformed into training data.

    That means my mundane posts about university essay deadlines (“3 energy drinks down 1,000 words to go”) as well as unremarkable holiday snaps (one captures me slumped over my phone on a stationary ferry) are about to become part of that corpus. The fact that these memories are so dull, and also very personal, makes Meta’s interest more unsettling.

    The company says it is only interested in content that is already public: private messages, posts shared exclusively with friends, and Instagram Stories are out of bounds. Despite that, AI is suddenly feasting on personal artifacts that have, for years, been gathering dust in unvisited corners of the internet. For those reading from outside Europe, the deed is already done. The deadline announced by Meta applied only to Europeans. The posts of American Facebook and Instagram users have been training Meta AI models since 2023, according to company spokesperson Matthew Pollard.

    Meta is not the only company turning my online history into AI fodder. WIRED’s Reece Rogers recently discovered that Google’s AI search feature was copying his journalism. But finding out which personal remnants exactly are feeding future chatbots was not easy. Some sites I’ve contributed to over the years are hard to trace. Early social network Myspace was acquired by Time Inc. in 2016, which in turn was acquired by a company called Meredith Corporation two years later. When I asked Meredith about my old account, they replied that Myspace had since been spun off to an advertising firm, Viant Technology. An email to a company contact listed on its website was returned with a message that the address “couldn’t be found.”

    Asking companies still in business about my old accounts was more straightforward. Blogging platform Tumblr, owned by WordPress owner Automattic, said unless I’d opted out, the public posts I made as a teenager will be shared with “a small network of content and research partners, including those that train AI models” per a February announcement. YahooMail, which I used for years, told me that a sample of old emails—which have apparently been “anonymized” and “aggregated”—are being “utilized” by an AI model internally to do things like summarize messages. Microsoft-owned LinkedIn also said my public posts were being used to train AI although some “personal” details included in those posts were excluded, according to a company spokesperson, who did not specify what those personal details were.

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  • Neo-Nazis Are All-In On AI

    Neo-Nazis Are All-In On AI

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    Extremists across the US have weaponized artificial intelligence tools to help them spread hate speech more efficiently, recruit new members, and radicalize online supporters at an unprecedented speed and scale, according to a new report from the Middle East Media Research Institute (MEMRI), an American non-profit press monitoring organization.

    The report found that AI-generated content is now a mainstay of extremists’ output: They are developing their own extremist-infused AI models, and are already experimenting with novel ways to leverage the technology, including producing blueprints for 3D weapons and recipes for making bombs.

    Researchers at the Domestic Terrorism Threat Monitor, a group within the institute which specifically tracks US-based extremists, lay out in stark detail the scale and scope of the use of AI among domestic actors, including neo-Nazis, white supremacists, and anti-government extremists.

    “There initially was a bit of hesitation around this technology and we saw a lot of debate and discussion among [extremists] online about whether this technology could be used for their purposes,” Simon Purdue, director of the Domestic Terrorism Threat Monitor at MEMRI, told reporters in a briefing earlier this week. “In the last few years we’ve gone from seeing occasional AI content to AI being a significant portion of hateful propaganda content online, particularly when it comes to video and visual propaganda. So as this technology develops, we’ll see extremists use it more.”

    As the US election approaches, Purdue’s team is tracking a number of troubling developments in extremists’ use of AI technology, including the widespread adoption of AI video tools.

    “The biggest trend we’ve noticed [in 2024] is the rise of video,” says Purdue. “Last year, AI-generated video content was very basic. This year, with the release of OpenAI’s Sora, and other video generation or manipulation platforms, we’ve seen extremists using these as a means of producing video content. We’ve seen a lot of excitement about this as well, a lot of individuals are talking about how this could allow them to produce feature length films.”

    Extremists have already used this technology to create videos featuring a President Joe Biden using racial slurs during a speech and actress Emma Watson reading aloud Mein Kampf while dressed in a Nazi uniform.

    Last year, WIRED reported on how extremists linked to Hamas and Hezbollah were leveraging generative AI tools to undermine the hash-sharing database that allows Big Tech platforms to quickly remove terrorist content in a coordinated fashion, and there is currently no available solution to this problem

    Adam Hadley, the executive director of Tech Against Terrorism, says he and his colleagues have already archived tens of thousands of AI-generated images created by far-right extremists.

    “This technology is being utilized in two primary ways,” Hadley tells WIRED. “Firstly, generative AI is used to create and manage bots that operate fake accounts, and secondly, just as generative AI is revolutionizing productivity, it is also being used to generate text, images, and videos through open-source tools. Both these uses illustrate the significant risk that terrorist and violent content can be produced and disseminated on a large scale.”

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  • Adobe Says It Won’t Train AI Using Artists’ Work. Creatives Aren’t Convinced

    Adobe Says It Won’t Train AI Using Artists’ Work. Creatives Aren’t Convinced

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    When users first found out about Adobe’s new terms of service (which were quietly updated in February), there was an uproar. Adobe told users it could access their content “through both automated and manual methods” and use “techniques such as machine learning in order to improve [Adobe’s] Services and Software.” Many understood the update as the company forcing users to grant unlimited access to their work, for purposes of training Adobe’s generative AI: Firefly.

    Late on Tuesday, Adobe issued a clarification: In an updated version of its terms of service agreement, it pledged not to train AI on its user content stored locally or in the cloud and gave users the option to opt-out of content analytics.

    Caught in the crossfire of intellectual property lawsuits, the ambiguous language used to previously update the terms shed light on a climate of acute skepticism among artists, many of whom over rely on Adobe for their work. “They already broke our trust,” says Jon Lam, a senior storyboard artist at Riot Games, referring to how award-winning artist Brian Kesinger discovered generated images in the style of his art being sold under his name on their stock image site, without his consent. Earlier this month, the estate of late photographer Ansel Adams publicly scolded Adobe for allegedly selling generative AI imitations of his work.

    Scott Belsky, Adobe’s Chief Strategy Officer, had tried to assuage concerns when artists started protesting, clarifying that machine learning refers to the company’s non-generative AI tools—Photoshop’s “Content Aware Fill” tool, which allows users to seamlessly remove objects in an image, is one of the many tools done through machine learning. But while Adobe insists that the updated terms does not give the company content ownership and that they will never use user content to train Firefly, the misunderstanding triggered a bigger discussion about the company’s market monopoly and how a change like this could threaten livelihoods of artists at any point. Lam is among the artists that still believes that, despite Adobe’s clarification, the company will use work created on its platform to train Firefly without the creator’s consent.

    The nervousness over non-consensual use and monetization of copyrighted work by generative AI models is not new. Early last year, artist Karla Ortiz was able to prompt images of her work using her name on various generative AI models; an offense that gave rise to a class action lawsuit against Midjourney, DeviantArt, and Stability AI. Ortiz was not alone—Polish fantasy artist Greg Rutkowski found that his name was one of the most commonly-used prompts in Stable Diffusion when the tool first launched in 2022.

    As the owner of Photoshop and creator of PDFs, Adobe has reigned as the industry standard for over 30 years, powering the majority of the creative class. An attempt to acquire product design company Figma was blocked and abandoned in 2023 for antitrust concerns attesting to its size.

    Adobe specifies that Firefly is “ethically trained” on Adobe Stock, but Eric Urquhart, long-time stock image contributor, insists that “there was nothing ethical about how Adobe trained the AI for Firefly,” pointing out that Adobe does not own the rights to any images from individual contributors. Urquhart originally put his images up on Fotolia, a stock image site, where he agreed to licensing terms that did not specify any uses for generative AI. Fotolia was then acquired by Adobe in 2015, which rolled out silent terms of service updates that later allowed the company to train Firefly using Eric’s photos without his explicit consent: “the language in the current change of TOS, it’s very similar to what I saw in the Adobe Stock TOS.”



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  • Reduce AI Hallucinations With This Neat Software Trick

    Reduce AI Hallucinations With This Neat Software Trick

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    To start off, not all RAGs are of the same caliber. The accuracy of the content in the custom database is critical for solid outputs, but that isn’t the only variable. “It’s not just the quality of the content itself,” says Joel Hron, a global head of AI at Thomson Reuters. “It’s the quality of the search, and retrieval of the right content based on the question.” Mastering each step in the process is critical, since one misstep can throw the model completely off.

    “Any lawyer who’s ever tried to use a natural language search within one of the research engines will see that there are often instances where semantic similarity leads you to completely irrelevant materials,” says Daniel Ho, a Stanford professor and senior fellow at the institute for Human-Centered AI. Ho’s research into AI legal tools that rely on RAG found a higher rate of mistakes in outputs than the companies building the models found.

    Which brings us to the thorniest question in the discussion: how do you define hallucinations within a RAG implementation? Is it only when the chatbot generates a citation-less output and makes up information? Is it also when the tool may overlook relevant data or misinterpret aspects of a citation?

    According to Lewis, hallucinations in a RAG system boil down to whether the output is consistent with what’s found by the model during data retrieval. Though, the Stanford research into AI tools for lawyers broadens this definition a bit by examining whether the output is grounded in the provided data as well as whether it’s factually correct—a high bar for legal professionals who are often parsing complicated cases and navigating complex hierarchies of precedent.

    While a RAG system attuned to legal issues is clearly better at answering questions on case law than OpenAI’s ChatGPT or Google’s Gemini, it can still overlook the finer details and make random mistakes. All of the AI experts I spoke with emphasized the continued need for thoughtful, human interaction throughout the process to double check citations and verify the overall accuracy of the results.

    Law is an area where there’s a lot of activity around RAG-based AI tools, but the process’s potential is not limited to a single, white collar job. “Take any profession or any business. You need to get answers that are anchored on real documents,” says Arredondo. “So, I think RAG is going to become the staple that is used across basically every professional application, at least in the near to mid-term.” Risk-averse executives seem excited about the prospect of using AI tools to better understand their proprietary data, without having to upload sensitive info to a standard, public chatbot.

    It’s critical, though, for users to understand the limitations of these tools, and for AI-focused companies to refrain from overpromising the accuracy of their answers. Anyone using an AI tool should still avoid trusting the output entirely, and they should approach its answers with a healthy sense of skepticism even if the answer is improved through RAG.

    “Hallucinations are here to stay,” says Ho. “We do not yet have ready ways to really eliminate hallucinations.” Even when RAG reduces the prevalence of errors, human judgment reigns paramount. And that’s no lie.

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  • The Fight Against AI Comes to a Foundational Data Set

    The Fight Against AI Comes to a Foundational Data Set

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    Danish media outlets have demanded that the nonprofit web archive Common Crawl remove copies of their articles from past datasets and stop crawling their websites immediately. This request was issued amid growing outrage over how artificial intelligence companies like OpenAI are using copyrighted materials.

    Common Crawl plans to comply with the request, first issued on Monday. Executive director Rich Skrenta says the organization is “not equipped” to fight media companies and publishers in court.

    The Danish Rights Alliance (DRA), an association representing copyright holders in Denmark, spearheaded the campaign. It made the request on behalf of four media outlets, including Berlingske Media and the daily newspaper Jyllands-Posten. The New York Times made a similar request of Common Crawl last year, prior to filing a lawsuit against OpenAI for using its work without permission. In its complaint, the New York Times highlighted how Common Crawl’s data was the most “highly weighted dataset” in GPT-3.

    Thomas Heldrup, the DRA’s head of content protection and enforcement, says that this new effort was inspired by the Times. “Common Crawl is unique in the sense that we’re seeing so many big AI companies using their data,” Heldrup says. He sees its corpus as a threat to media companies attempting to negotiate with AI titans.

    Although Common Crawl has been essential to the development of many text-based generative AI tools, it was not designed with AI in mind. Founded in 2007, the San Francisco-based organization was best known prior to the AI boom for its value as a research tool. “Common Crawl is caught up in this conflict about copyright and generative AI,” says Stefan Baack, a data analyst at the Mozilla Foundation who recently published a report on Common Crawl’s role in AI training. “For many years it was a small niche project that almost nobody knew about.”

    Prior to 2023, Common Crawl did not receive a single request to redact data. Now, in addition to the requests from the New York Times and this group of Danish publishers, it’s also fielding an uptick of requests that have not been made public.

    In addition to this sharp rise in demands to redact data, Common Crawl’s web crawler, CCBot, is also increasingly thwarted from accumulating new data from publishers. According to the AI detection startup Originality AI, which often tracks the use of web crawlers, over 44 percent of the top global news and media sites block CCBot. Apart from Buzzfeed, which began blocking it in 2018, most of the prominent outlets it analyzed—including Reuters, The Washington Post, and the CBC—only spurned the crawler in the last year. “They’re being blocked more and more,” Baack says.

    Common Crawl’s quick compliance with this kind of request is driven by the realities of keeping a small nonprofit afloat. Compliance does not equate to ideological agreement, though. Skrenta sees this push to remove archival materials from data repositories like Common Crawl as nothing short of an affront to the internet as we know it. “It’s an existential threat,” he says. “They’ll kill the open web.”

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  • AI Tools Are Secretly Training on Real Images of Children

    AI Tools Are Secretly Training on Real Images of Children

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    Over 170 images and personal details of children from Brazil have been scraped by an open-source dataset without their knowledge or consent, and used to train AI, claims a new report from Human Rights Watch released Monday.

    The images have been scraped from content posted as recently as 2023 and as far back as the mid-1990s, according to the report, long before any internet user might anticipate that their content might be used to train AI. Human Rights Watch claims that personal details of these children, alongside links to their photographs, were included in LAION-5B, a dataset that has been a popular source of training data for AI startups.

    “Their privacy is violated in the first instance when their photo is scraped and swept into these datasets. And then these AI tools are trained on this data and therefore can create realistic imagery of children,” says Hye Jung Han, children’s rights and technology researcher at Human Rights Watch and the researcher who found these images. “The technology is developed in such a way that any child who has any photo or video of themselves online is now at risk because any malicious actor could take that photo, and then use these tools to manipulate them however they want.”

    LAION-5B is based on Common Crawl—a repository of data that was created by scraping the web and made available to researchers—and has been used to train several AI models, including Stability AI’s Stable Diffusion image generation tool. Created by the German nonprofit organization LAION, the dataset is openly accessible and now includes more than 5.85 billion pairs of images and captions, according to its website.

    The images of children that researchers found came from mommy blogs and other personal, maternity, or parenting blogs, as well as stills from YouTube videos with small view counts, seemingly uploaded to be shared with family and friends.

    “Just looking at the context of where they were posted, they enjoyed an expectation and a measure of privacy,” Hye says. “Most of these images were not possible to find online through a reverse image search.”

    LAION spokesperson Nate Tyler says the organization has already taken action. “LAION-5B were taken down in response to a Stanford report that found links in the dataset pointing to illegal content on the public web,” he says, adding that the organization is currently working with “Internet Watch Foundation, the Canadian Centre for Child Protection, Stanford, and Human Rights Watch to remove all known references to illegal content.”

    YouTube’s terms of service do not allow scraping except under certain circumstances; these instances seem to run afoul of those policies. “We’ve been clear that the unauthorized scraping of YouTube content is a violation of our Terms of Service,” says YouTube spokesperson Jack Maon, “and we continue to take action against this type of abuse.”

    In December, researchers at Stanford University found that AI training data collected by LAION-5B contained child sexual abuse material. The problem of explicit deepfakes is on the rise even among students in US schools, where they are being used to bully classmates, especially girls. Hye worries that, beyond using children’s photos to generate CSAM, that the database could reveal potentially sensitive information, such as locations or medical data. In 2022, a US-based artist found her own image in the LAION dataset, and realized it was from her private medical records.

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  • OpenAI Offers a Peek Inside the Guts of ChatGPT

    OpenAI Offers a Peek Inside the Guts of ChatGPT

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    ChatGPT developer OpenAI’s approach to building artificial intelligence came under fire this week from former employees who accuse the company of taking unnecessary risks with technology that could become harmful.

    Today OpenAI released a new research paper apparently aimed at showing it is serious about tackling AI risk by making its models more explainable. In the paper, researchers from the company lay out a way to peer inside the AI model that powers ChatGPT. They devised a way to identify how it stores certain concepts—including those that might perhaps cause an AI system to misbehave.

    Although the research makes OpenAI’s work on keeping AI in check more visible, it also highlights recent turmoil at the company. The new research was performed by the recently disbanded “superalignment” team at OpenAI that was dedicated to studying the long-term risks posed by the technology.

    The former group’s coleads Ilya Sutskever and Jan Leike, both of whom have left the OpenAI, are named as coauthors. Sutskever, a cofounder of the company and formerly chief scientist, was among the board members who voted to fire OpenAI CEO Sam Altman last November, triggering a chaotic few days that culminated in Altman’s return as leader.

    ChatGPT is powered by a family of so-called large language models called GPT, based on an approach to machine learning known as artificial neural networks. These mathematical networks have shown great power to learn useful tasks by analyzing example data but their workings cannot be easily scrutinized as conventional computer programs can. The complex interplay between the layers of “neurons” within an artificial neural network makes reverse engineering why a system like ChatGPT came up with a particular response hugely challenging.

    “Unlike with most human creations, we don’t really understand the inner workings of neural networks,” the researchers behind the work write in an accompanying blog post. Some prominent AI researchers believe that the most powerful AI models including ChatGPT could perhaps be used to design chemical or biological weapons and coordinate cyber attacks. A longer-term concern is that AI models may choose to hide information or act in harmful ways in order to achieve their goals.

    OpenAI’s new paper outlines a technique that lessens the mystery a little, by identifying patterns that represent specific concepts inside a machine learning system with help from an additional machine learning model. The key innovation is refining the network used to peer inside the system of interest by identifying concepts, to make it more efficient.

    OpenAI proved out the approach by identifying patterns that represent concepts inside GPT-4, one of its largest AI models. The company released code related to the interpretability work and a visualization tool that can be used to see how the words in different sentences activate concepts including profanity and erotic content in GPT-4 and another model. Knowing how a model represents certain concepts could be a step towards being able to dial down those associated with unwanted behavior, to keep an AI system on the rails. It could also make it possible to tune an AI system to favor certain topics or ideas.

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  • Chatbot Teamwork Makes the AI Dream Work

    Chatbot Teamwork Makes the AI Dream Work

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    Turning to a friend or coworker can make tricky problems easier to tackle. Now it looks like having AI chatbots team up with each other can make them more effective.

    I’ve been playing this week with AutoGen, an open source software framework for AI agent collaboration developed by researchers at Microsoft and academics at Pennsylvania State University, the University of Washington, and Xidian University in China. The software taps OpenAI’s large language model GPT-4 to let you create multiple AI agents with different personas, roles, and objectives that can be prompted to solve specific problems.

    To put the idea of AI collaboration to the test, I had two AI agents work together on a plan for how to write about AI collaboration.

    By modifying AutoGen’s code I created a “reporter” and “editor” that discussed writing about AI agent collaboration. After talking about the importance of “showcasing how industries such as health care, transportation, retail, and more are using multi-agent AI,” the pair agreed that the proposed piece should dive into the “ethical dilemmas” posed by the technology.

    It’s too early to write much about any of those suggested topics—the concept of multi-agent AI collaboration is mostly at the research phase. But the experiment demonstrated a strategy that can amplify the power of AI chatbots.

    The large language models like those behind ChatGPT often stumble over math problems because they work by providing statistically plausible text rather than rigorous logical reasoning. In a paper presented at an academic workshop in May, the researchers behind AutoGen show that having AI agents collaborate can mitigate that weakness.

    They found that two to four agents working together could solve fifth-grade math problems more reliably than one agent on its own. In their tests, teams were also able to reason out chess problems by talking them through, and they were able to analyze and refine computer code by talking to one another.

    Others have shown similar benefits when several different AI models—even those offered by corporate rivals—team up. In a project presented at the same workshop at a major AI conference called ICLR, a group from MIT and Google got OpenAI’s ChatGPT and Google’s Bard to work together by discussing and debating problems. They found that the duo were more likely to converge on a correct solution to problems together than when the bots worked solo. Another recent paper from researchers at UC Berkeley and the University of Michigan showed that having one AI agent review and critique the work of another could allow the supervising bot to upgrade the other agent’s code, improving its ability to use a computer’s web browser.

    Teams of LLMs can also be prompted to behave in surprisingly humanlike ways. A group from Google, Zhejiang University in China, and the National University of Singapore, found that assigning AI agents distinct personality traits, such as “easy-going” or “overconfident,” can fine-tune their collaborative performance, either positively or negatively.

    And a recent article in The Economist rounds up several multi-agent projects, including one commissioned by the Pentagon’s Defense Advanced Research Projects Agency. In that experiment, a team of AI agents was tasked with searching for bombs hidden within a labyrinth of virtual rooms. While the multi-AI team was better at finding the imaginary bombs than a lone agent, the researchers also found that the group spontaneously developed an internal hierarchy. One agent ended up bossing the others around as they went about their mission.

    Graham Neubig, an associate professor at Carnegie Mellon University, who organized the ICRL workshop, is experimenting with multi-agent collaboration for coding. He says that the collaborative approach can be powerful but also can lead to new kinds of errors, because it adds more complexity. “It’s possible that multi-agent systems are the way to go, but it’s not a foregone conclusion,” Neubig says.

    People are already adapting the open source AutoGen framework in interesting ways, for instance creating simulated writers’ rooms to generate fiction ideas, and a virtual “business-in-a-box” with agents that take on different corporate roles. Perhaps it won’t be too long until the assignment my AI agents came up with needs to be written.

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  • Learning to Live With Google’s AI Overviews

    Learning to Live With Google’s AI Overviews

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    Google has spent the past year lustily rolling out AI features across its platforms. But with each launch, it is becoming more clear that some of these so-called enhancements should have simmered a little longer. The latest update to stoke equal parts excitement and ridicule is AI Overviews, the new auto-generated summary boxes that appear at the top of some Google search results.

    In theory, AI Overviews are meant to answer questions and neatly summarize key information about people’s search queries, offering links to the sources the summaries were pulled from and making search more immediately useful. In reality, these AI Overviews have been kinda messy. The information the summary confidently displays can be simply, and sometimes comically, wrong. Even when the AI Overview is correct, it typically only offers a slim account of the topic without the added context—or attribution—contained in the web pages it’s pulling from. The resulting criticisms have forced Google to reportedly dial back the number of search queries that trigger AI Overviews, and they are now being seen less frequently than they were at launch.

    This week, we talk with WIRED writers Kate Knibbs and Reece Rogers about the rollout, how Google has been managing it, and what it’s like to watch our journalism get gobbled up by these hungry, hungry infobots.

    Show Notes

    Read Kate’s story about Google trimming the frequency of its AI Overviews. Read Reece’s story about how Google’s AI Overviews copied his original work. Read Lauren’s story about the end of Google Search as we know it.

    Recommendations

    Kate recommends Token Supremacy by Zachary Small. Reece recommends the game Balatro. Lauren recommends the poetry book Technelegy by Sasha Stiles. Mike recommends the book Neu Klang: The Definitive History of Krautrock by Christoph Dallach.

    Kate Knibbs can be found on social media @Knibbs (X) or @extremeknibbs (Threads/IG). Reece Rogers is @reece___rogers. Lauren Goode is @LaurenGoode. Michael Calore is @snackfight. Bling the main hotline at @GadgetLab. The show is produced by Boone Ashworth (@booneashworth). Our theme music is by Solar Keys.

    How to Listen

    You can always listen to this week’s podcast through the audio player on this page, but if you want to subscribe for free to get every episode, here’s how:

    If you’re on an iPhone or iPad, open the app called Podcasts, or just tap this link. You can also download an app like Overcast or Pocket Casts, and search for Gadget Lab. If you use Android, you can find us in the Google Podcasts app just by tapping here. We’re on Spotify too. And in case you really need it, here’s the RSS feed.



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  • An AI Cartoon May Interview You for Your Next Job

    An AI Cartoon May Interview You for Your Next Job

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    More than 100,000 people have gone through micro1’s screenings with hopes of being added to its marketplace of engineers, and it lists companies like Monday.com and AI company DoNotPay among those who have used its system to screen or hired engineers from its marketplace. Ansari says companies are using micro1 to screen as many as 30,000 candidates a month.

    Asynchronous video interviews have become more common, with companies turning to pre-recorded responses in automated systems to handle screening interviews. This task has become more onerous after a series of layoffs in the past two years have whittled down the number of positions available, and recruiters who post open roles on sites like LinkedIn can receive hundreds or thousands of applicants. And generative AI tools have made it easier for those seeking jobs to bulk apply, creating more applications for recruiters and hiring managers to review—some with little relevance to the role. But while AI is becoming more common on the hiring side, too, some recruiters are wary of the biases it may have, and have steered clear of employing the tools in their decisions.

    Of course there’s still bias with AI tools, Ansari says. “Of course there’s also bias with humans. The goal with the AI system is to make it much less biased than humans.” With AI, Ansari explains, the AI interviewer on micro1 won’t pass or fail a candidate; instead, it places them into categories like inexperienced, mid-level, and senior. Then, Ansari says, it’s on the hiring manager or recruiter to decide if the candidate is a good fit for the role. They can also listen to audio recordings of the responses rather than relying solely on the AI interviewer to interpret them.

    Zahira Jaser, an associate professor at the University of Sussex Business School, says a lot remains unknown about the impact of AI and asynchronous interviewing—including how the tech affects candidates. Recording oneself can be awkward, and there are no human cues to pick up on from an AI interviewer. After being told to act naturally and put their best foot forward in the already nerve-wracking process of human job interviews throughout their career, people may not know how to show their best self to a chatbot, particularly when they’re up against opaque, built-in biases of AI.

    “In the real world, humans are biased. But there are techniques to which we can overcome this human bias,” Jaser says. “In an algorithm-driven bias, this Is likely to be very systematic.” For example, some AI hiring tools are trained on profiles of past successful employees, raising concerns that they will repeat past biased hiring practices.

    For now, these AI tools don’t have the final say in who gets hired. But they increasingly have sway over which applicants get face time with a real human, and that can have a massive impact on what the workforce looks like going forward.

    But if you ask Ansari, there is an alternative path for interviews in the future: He believes job seekers may also use AI-driven avatars to interview for jobs with AI interviewers, relegating the painful, tedious parts of initial job searchers to computers entirely. AI could make “really good matches” between job seekers and companies, Ansari says. “And then the company and the candidate can spend their actual time on a Zoom call or in-person interview.”

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