Trending Now Say More Look Up In My Bag Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Mashable @Home Mashable Selects Top creators, ranked Gift Ideas For Everyone On Your List Creator Playbook AI at School Safety Net All Series Job seekers are sending AI clones to interviews, speaking gibberish, and testing just how much these bots can handle. Olivia Tauber is the deputy editor of digital culture, covering creators, media, movies, beauty, and more. Based in New York, her work has appeared in The New York Times, Vanity Fair, The Cut, Teen Vogue, Complex, and Interview Magazine. She holds a Master's degree in Journalism from NYU and a Bachelor's from the University of Michigan. She also runs Fan Mail, a weekly pop-culture newsletter.

The job interview question you don't have to answer To play this video you need to enable JavaScript in your browser. Figure caption, Watch: How much is your salary? Prepare all you like, but there is often still one job interview question that leaves you feeling uncomfortable. While employers in the UK are within their rights to ask you how much you get paid in your current role, applicants are not obliged to tell them. Recruiters have been encouraged to stop posing questions about salary history and, in the EU, new rules will prevent them doing so.

Job seekers are trying to game the application process using AI. It's not working, and not for the reasons you might think. Jodi Beggs' résumé got dinged for being two pages long. She used her middle initial on one document but not another, which also cost her points. If she changed "percent" to the % sign, her score went up. Beggs, a data scientist and job seeker, got this feedback from an online system that compares a job description with an applicant's materials to assess how well they match up.

It Should Be Harder to Apply for a Job. Thanks to a dwindling supply of open roles, "one-click" applications, and the rise of artificial intelligence, it's easier than ever to apply for a job. Three years ago, Andrew Stockwell had his hiring routine down to a science. As head of people for the software-buying company Vendr, Stockwell posted a listing, waited a few days, and then reviewed a few dozen applications--up to 100, if he was lucky. Recruiters identified other potential candidates, adding a few more applicants to the mix.

'Companies in the US are increasingly turning to AI to make faster and more efficient workplace decisions, often under the guise of objectivity.' 'Companies in the US are increasingly turning to AI to make faster and more efficient workplace decisions, often under the guise of objectivity.' Will AI give you the job? F or the last four years, Erin Kistler has applied for thousands of jobs at companies like Paypal, Microsoft and Netflix, only to find her résumé disappear into a black hole. A product manager with nearly 20 years of experience, Kistler believes she was qualified for every role, yet she never received a single interview.

An AI interview is increasingly the first step of a hiring process. Since there's no human on the other end, candidates are scheduling them whenever--even deep into the night. When Tim Millard received an email about interviewing for a media relations job in January, six months into his job hunt, he assumed he'd speak to a human. When he was instead instructed to record himself answering a series of questions on camera, he felt a familiar pang of resignation--one that had followed him throughout the job hunt . "I figured, I have to do this if I want to be considered for the job," Millard tells WIRED.

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Randomized experiments are the preferred approach for evaluating the effects of interventions, but they are costly and often yield estimates with substantial uncertainty. On the other hand, in silico experiments leveraging foundation models offer a cost-effective alternative that can potentially attain higher statistical precision. However, the benefits of in silico experiments come with a significant risk: statistical inferences are not valid if the models fail to accurately predict experimental responses to interventions. In this paper, we propose a novel approach that integrates the predictions from multiple foundation models with experimental data while preserving valid statistical inference. Our estimator is consistent and asymptotically normal, with asymptotic variance no larger than the standard estimator based on experimental data alone. Importantly, these statistical properties hold even when model predictions are arbitrarily biased. Empirical results across several randomized experiments show that our estimator offers substantial precision gains, equivalent to a reduction of up to 20% in the sample size needed to match the same precision as the standard estimator based on experimental data alone.

When applicants get rejected by a high-stakes algorithmic decision system, recourse explanations provide actionable suggestions for applicants on how to change their input features to get a positive evaluation. A crucial yet overlooked phenomenon is that recourse explanations are performative: When many applicants act according to their recommendations, their collective behavior may shift the data distribution and, once the model is refitted, also the decision boundary. Consequently, the recourse algorithm may render its own recommendations invalid, such that applicants who make the effort of implementing their recommendations may be rejected again when they reapply. In this work, we formally characterize the conditions under which recourse explanations remain valid under their own performative effects. In particular, we prove that recourse actions may become invalid if they are influenced by or if they intervene on non-causal variables. Based on this analysis, we caution against the use of standard counterfactual explanation and causal recourse methods, and instead advocate for recourse methods that recommend actions exclusively on causal variables.

Large Language Model (LLM)-based multi-agent systems show promise for automating real-world tasks but struggle to transfer across domains due to their domain-specific nature. Current approaches face two critical shortcomings: they require complete architectural redesign and full retraining of all components when applied to new domains. We introduce WORKFORCE, a hierarchical multi-agent framework that decouples strategic planning from specialized execution through a modular architecture comprising: (i) a domain-agnostic Planner for task decomposition, (ii) a Coordinator for subtask management, and (iii) specialized Workers with domain-specific tool-calling capabilities.