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.

LinkedIn may or may not be the perfect Twitter replacement, but one thing is for sure: It's a profoundly weird place. Staying active on the platform is basically required for today's knowledge workers to find employment, which is odd. Also, it's a place where a lot of people spend time pretending recruiters and hiring managers are fun, interesting people by reacting and replying to their posts. Then there are the LinkedIn influencers, all of whom sounded like ChatGPT long before ChatGPT was a thing. But perhaps the oddest thing about LinkedIn is how transparent it is about its surveillance features.

LinkedIn is adding a new, AI-powered perk for its premium subscribers: a built-in job coach that uses AI and LinkedIn data to help job seekers find, research and apply for roles. The new feature arrives as the company announced its user base has grown to 1 billion members as it looks to ramp up its investment in AI-driven features. The Microsoft-owned company has increasingly been experimenting with AI features for its paying members. Earlier this year, it introduced the ability to use generative AI to write better profile descriptions and messages to hiring managers. But the latest AI perks aim to provide an even more personalized experience.

CyberGuy shows you how to manage your online presence. The job search landscape has transformed dramatically in just a few years. Gone are the days when applying for jobs was a part-time endeavor. Nowadays, it's practically a full-time job, especially if you're out of work and have to document your efforts to claim unemployment benefits. The experience can be overwhelming, but fortunately, technology--particularly artificial intelligence (AI)--is here to help streamline the process.

In this paper, we present a novel method for detecting fake and Large Language Model (LLM)-generated profiles in the LinkedIn Online Social Network immediately upon registration and before establishing connections. Early fake profile identification is crucial to maintaining the platform's integrity since it prevents imposters from acquiring the private and sensitive information of legitimate users and from gaining an opportunity to increase their credibility for future phishing and scamming activities. This work uses textual information provided in LinkedIn profiles and introduces the Section and Subsection Tag Embedding (SSTE) method to enhance the discriminative characteristics of these data for distinguishing between legitimate profiles and those created by imposters manually or by using an LLM. Additionally, the dearth of a large publicly available LinkedIn dataset motivated us to collect 3600 LinkedIn profiles for our research. We will release our dataset publicly for research purposes. This is, to the best of our knowledge, the first large publicly available LinkedIn dataset for fake LinkedIn account detection. Within our paradigm, we assess static and contextualized word embeddings, including GloVe, Flair, BERT, and RoBERTa. We show that the suggested method can distinguish between legitimate and fake profiles with an accuracy of about 95% across all word embeddings. In addition, we show that SSTE has a promising accuracy for identifying LLM-generated profiles, despite the fact that no LLM-generated profiles were employed during the training phase, and can achieve an accuracy of approximately 90% when only 20 LLM-generated profiles are added to the training set. It is a significant finding since the proliferation of several LLMs in the near future makes it extremely challenging to design a single system that can identify profiles created with various LLMs.

Generative AI is already looking like the major tech trend of 2023. The ability to generate fresh content via algorithms has been thrust into the public consciousness by the likes of ChatGPT, a chatbot-style technology trained on large language models (LLMs) capable of producing essays, poems, lyrics, news articles and even computer programs. Then there's DALL-E, from the same Microsoft-backed OpenAI that spawned ChatGPT, which serves a similar purpose but for visual creations instead. While some have argued that ChatGPT signals AI's arrival into the mainstream, the truth of the matter is that we're just at the start of a new era of AI-powered applications that will transform just about every facet of industry, from consumer search and stock photography to real estate and content marketing. It's against that backdrop that a fledgling startup called Tavus is looking to make its mark by enabling companies to create "unique" videos tailored to a specific individual, but based entirely on a single initial recording.

Earlier this month, when LinkedIn started seeding "AI-powered conversation starters" in people's news feeds to boost engagement on its platform, the move saw more than little engagement of its own, none of it too positive. But the truth of the matter with LinkedIn is that it's been using a lot of AI and other kinds of automation across different aspects of its platform for years, primarily behind the scenes with how it builds and operates its network. Now, with its owner Microsoft going all-in on OpenAI, it looks like it's becoming a more prominent part of the strategy for LinkedIn on the front end, too -- with the latest coming today in the areas of LinkedIn profiles, recruitment and LinkedIn Learning. The company is today introducing AI-powered writing suggestions, which will initially be offered to people to spruce up their LinkedIn profiles, and to recruiters writing job descriptions. Both are built on advanced GPT models, said Tomer Cohen, LinkedIn's chief product officer.

Earlier this month, when LinkedIn started seeding "AI-powered conversation starters" in people's news feeds to boost engagement on its platform, the move saw more than little engagement of its own, none of it too positive. But the truth of the matter with LinkedIn is that it's been using a lot of AI and other kinds of automation across different aspects of its platform for years, primarily behind the scenes with how it builds and operates its network. Now, with its owner Microsoft going all-in on OpenAI, it looks like it's becoming a more prominent part of the strategy for LinkedIn on the front end, too -- with the latest coming today in the areas of LinkedIn profiles, recruitment and LinkedIn Learning. The company is today introducing AI-powered writing suggestions, which will initially be offered to people to spruce up their LinkedIn profiles, and to recruiters writing job descriptions. Both are built on advanced GPT models, said Tomer Cohen, LinkedIn's chief product officer.

LinkedIn is the latest platform to hop on the generative AI bandwagon. The company is adding AI-powered "writing suggestions" and job descriptions to its service as it looks for new ways to infuse AI into its platform. The writing suggestions are meant to make it easier to fill out key profile fields that LinkedIn says can otherwise feel "daunting" to complete: the "about" and "headline" sections near the top of each profile. Now, with the new "enhance" tool, LinkedIn Premium subscribers can generate descriptions based on their experience. The company says the tool, which uses the same OpenAI models that power ChatGPT, is meant to preserve "your unique voice and style" and will draw from your job experience and skills, as well as LinkedIn's own "insights" into what makes a good profile.

This conversation delves into the whitespaces in Content Marketing that AI enables. Sramana Mitra: Let's start by introducing our audience to yourself as well as to Grapevine6. Mike Orr: I'm the CEO and one of the co-founders of Grapevine6. It's a content engagement platform to enable salespeople, entrepreneurs, and anyone trying to engage their networks in social and digital channels with relevant content. Sramana Mitra: Talk to me with a bit of a use case so that we get to a more visceral understanding of what you do and how you do what you do. Mike Orr: We found that in a number of our customers, Grapevine6 enables their salespeople to engage their networks in social channels and use it to either drive sales or drive relationships. For entrepreneurs, it helps to build their networks. The biggest challenge that they have in driving adoption in those programs is that there is not enough relevant content coming from marketing to help them grow and to start