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.

Our gifting experts are obsessively searching for holiday gifts that are unique, thoughtful and suitable for any budget. 'So perfect, they are literally identical' - Your AI-polished resumes are recruiters' new top stressor Exclusive: A new Employ report shows how AI is both helping and hurting the hiring industry. Kayla Solino is a ZDNET Editor based in New York City and New Jersey. AI is both a benefit and an emerging hindrance for recruiters and organizations. A third of recruiters said AI-polished resumes are the top source of stress.

Christopher was sick of being ghosted by AI recruiters. So he unleashed ChatGPT on his robot interviewer. Christopher has become accustomed to jumping through the endless hoops of the modern job hunt. Like many government contractors, his work has dried up in the DOGE era . Over the past six months, he has applied to roughly 700 jobs; in the vast majority of cases, he's heard nothing back.

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.

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'I've had to Botox my CV': Are AI recruitment tools affecting women's careers? The fear of becoming surplus to requirements because of advancements in AI is one shared by millions of workers, but are AI recruitment tools posing a specific threat to women trying to return to employment? After a career spanning decades, with senior corporate roles in fashion, and a period of freelancing, 52-year-old Stacey Duguid decided to find one last job. What followed was 16 months of sending gazillions of CVs, with little more than a handful of automated replies, despite never previously struggling to find work. Feeling lonely and shameful and wondering if it was a me problem, she posted on social media asking whether other women her age were having the same experience.

Computer says no. Are AI interviews making it harder to get a job? It's brutal, says Bhuvana Chilukuri - a third-year business student who has applied for more than 100 jobs and has been rejected for every one. There are moments where I applied and I got a rejection less than two minutes later, which is really horrible, says the 20-year-old. She is convinced that very few, if any, of her applications are ever seen by a human as firms are increasingly using Artificial Intelligence (AI) to hire new staff. The first step is AI screening your CV.

Talent search is a cornerstone of modern recruitment systems, yet existing approaches often struggle to capture nuanced job-specific preferences, model recruiter behavior at a fine-grained level, and mitigate noise from subjective human judgments. We present a novel framework that enhances talent search effectiveness and delivers substantial business value through two key innovations: (i) leveraging LLMs to extract fine-grained recruitment signals from job descriptions and historical hiring data, and (ii) employing a role-aware multi-gate MoE network to capture behavioral differences across recruiter roles. To further reduce noise, we introduce a multi-task learning module that jointly optimizes click-through rate (CTR), conversion rate (CVR), and resume matching relevance. Experiments on real-world recruitment data and online A/B testing show relative AUC gains of 1.70% (CTR) and 5.97% (CVR), and a 17.29% lift in click-through conversion rate. These improvements reduce dependence on external sourcing channels, enabling an estimated annual cost saving of millions of CNY.

Avoiding bias and understanding the real-world consequences of AI-supported decision-making are critical to address fairness and assign accountability. Existing approaches often focus either on technical aspects, such as datasets and models, or on high-level socio-ethical considerations - rarely capturing how these elements interact in practice. In this paper, we apply an information flow-based modeling framework to a real-world recruitment process that integrates automated candidate matching with human decision-making. Through semi-structured stakeholder interviews and iterative modeling, we construct a multi-level representation of the recruitment pipeline, capturing how information is transformed, filtered, and interpreted across both algorithmic and human components. We identify where biases may emerge, how they can propagate through the system, and what downstream impacts they may have on candidates. This case study illustrates how information flow modeling can support structured analysis of fairness risks, providing transparency across complex socio-technical systems.

Artificial intelligence (AI) is increasingly used in recruitment, yet empirical evidence quantifying its impact on hiring efficiency and candidate selection remains limited. We randomly assign 37,000 applicants for a junior-developer position to either a traditional recruitment process (resume screening followed by human selection) or an AI-assisted recruitment pipeline incorporating an initial AI-driven structured video interview before human evaluation. Candidates advancing from either track faced the same final-stage human interview, with interviewers blind to the earlier selection method. In the AI-assisted pipeline, 54% of candidates passed the final interview compared with 34% from the traditional pipeline, yielding an average treatment effect of 20 percentage points (SE 12 pp.). Five months later, we collected LinkedIn profiles of top applicants from both groups and found that 18% (SE 1.1%) of applicants from the traditional track found new jobs compared with 23% (SE 2.3%) from the AI group, resulting in a 5.9 pp. (SE 2.6 pp.) difference in the probability of finding new employment between groups. The AI system tended to select younger applicants with less experience and fewer advanced credentials. We analyze AI-generated interview transcripts to examine the selection criteria and conversational dynamics. Our findings contribute to understanding how AI technologies affect decision making in recruitment and talent acquisition while highlighting some of their potential implications.