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You only need one thing to ace an interactive AI interview, says CEO who makes them
Whether you like it or not, if you're looking for a new job, you may be interviewed by AI soon. Interviews conducted by AI will a new norm by early next year, Brent Orsuga, a headhunter and recruitment expert told CNBC Make It last month. Founded just last year, California-based AI recruitment platform Apriora conducts up to 1,000 AI powered interviews every day for companies looking to hire for positions ranging from senior software engineers to electricians and baristas, Aaron Wang, the company's co-founder and CEO tells CNBC Make It. From scheduling to conducting interviews, Wang says Apriora's AI recruiting agent, Alex, which has been trained on "the entirety of the internet," automates the entire recruitment process. "You can think of her as a super intelligent recruiting colleague," Wang says. When a job is posted, Wang says the AI will read the description -- creating a set of questions designed to evaluate candidates which can be further tailored by the employer depending on their specific needs. During interviews, Wang says Alex engages with candidates in a conversational manner, responding appropriately, much like a human recruiter would. Wang adds that Alex can answer questions about the company, adjust its questions depending on candidate responses, as well as generate detailed feedback notes and identify top candidates for the recruiter after the interview. Here's how Wang says you can best prepare for an AI powered interview:
CNBC
Thu, 5 Dec, 8:08 PM UTC
When Candidates Use Generative AI for the Interview | MIT Sloan Management Review
Generative artificial intelligence is transforming how candidates prepare for interviews, making it more complicated for hiring managers to accurately assess their true expertise. By inputting role-specific details, organizational information, and their resumes into GenAI tools, candidates can prompt the technology to generate potential interview questions along with personalized answers. In fact, recruiters, consultants, and other job seekers widely recommend preparing for interviews in this way. However, there is growing concern among hiring professionals that candidates using generative AI are gaming the interview process. Research suggests that GenAI use has a material influence on hiring decisions: In one recent study, candidates who used such tools to prepare received higher overall interview performance ratings compared with those who were unassisted by GenAI. If generative AI helps candidates acquire new expertise or reinforce existing job-relevant knowledge, skills, abilities, and other characteristics (KSAOs), it will contribute to their future performance. But if it produces polished, contextualized responses that candidates merely parrot during interviews, hiring managers may mistakenly attribute KSAOs to them that they don't actually possess. In such instances, their interview performance will not translate to their future job performance. What, then, can hiring managers do in the wake of generative AI to ensure that they are getting an accurate representation of candidates from their interviews? For many reasons, there is already a preference for in-person interviews, including the possibility that candidates can use transcription software during virtual interviews to feed questions into GenAI and produce answers in real time. Strategically, interviewing in the age of AI also requires effective follow-up questions to uncover deeper indicators of genuine expertise. Can they explain how to do something, not just what to do? Do they know why something works? Do they know when, where, and for whom something is more effective? Have they considered other approaches? And are they aware of the drawbacks of their approach? Those types of questions can push potential hires to go beyond their rehearsed or surface-level answers. The best practices for designing and conducting behavior-based interviews are well established. These include conducting a job analysis to identify role-specific KSAOs, training interviewers on what to look for, and asking consistent questions across candidates.
MIT Sloan Management Review
Thu, 9 Jan, 2:17 PM UTC
CodeSignal launches AI-powered interview tool for product manager hiring
CodeSignal, a skills assessment platform, has introduced a new AI-driven interview solution specifically designed for hiring product managers. The "AI Interviewer for Product Managers" aims to streamline the recruitment process by evaluating candidates' practical skills through simulated real-world scenarios. Traditionally, hiring product managers has been a lengthy and sometimes inconsistent process. This new tool seeks to address that challenge by providing a standardized and scalable way to assess crucial product management competencies. These include areas like product sense, stakeholder management, project execution, and strategic thinking. The AI Interviewer presents candidates with realistic situations they might encounter as a product manager, such as defining product strategy or managing stakeholder expectations. Following the interview, the system generates a report ranking candidates across key skills, providing hiring teams with data-driven insights into strengths and weaknesses. The tool offers several key features: Neuchips, Vecow, and GSH collaborate on offline GenAI for data processing The AI Interviewer for Product Managers is part of CodeSignal's broader expansion into assessments for business roles, which includes solutions for Sales and Customer Success positions. The company states that these expansions reflect a commitment to provide flexible, AI-powered assessments across a range of roles. Further information can be found on CodeSignal's website.
Dataconomy
Wed, 5 Mar, 5:13 PM UTC
You Can Use AI to Help You Ace Your Job Interview, but There's a High Cost
Carly Quellman, aka Carly Que, is a multimedia strategist and storyteller at the intersection of technology and the humanities, investigating how perspective can enhance, rather than overstimulate, the world. She can be found online and near the closest south-facing window in Los Angeles. If you've got a job interview looming, you know how much preparation will go into it. Researching the company, prepping your answers to potential questions and figuring out how to express your skillset is all part of a good interview strategy -- but sometimes, the reality of an interview doesn't equate to what you prepared for. The jittery, anxious pre-interview feeling is all too familiar. Maybe, like me, you start to dissociate from the overwhelming conversation, losing a sense of connection with the person you're speaking to. Imposter syndrome can show up -- and take you down -- in seconds. Yes, you can find ways to cope with anxiety through things like meditation and breathing. Yes, you can always refine your preparation practice. But it would be nice to have support during the call to keep your confidence up and focus intact while pitching yourself to someone who has (likely) never met you in person. Enter: Final Round AI, an artificial intelligence tool that helps you prepare for your interview and polish your skills. The suite of AI-powered tools offers real-time "on the fly" interview transcription and support, mock interviews and resume building across 100-plus roles in consulting, marketing, finance, software, product, data science and dev ops. It also offers resources like a blog with tips and commonly asked questions, plus guides that provide visual and written how-tos for common tech-related issues. Final Round AI is a new tool that joins the ever-growing number of AI-powered companies and platforms for job seeking -- founders Michael Guan and Jay Ma launched it in late August 2023. While still in its infancy, Final Round AI is designed to offer many ways to support your career-related needs. If you're looking for an all-in-one job seeker-support system, this could be it -- or, at least, a worthwhile attempt to navigate your professional journey (and inner turmoil). But after attempting to navigate the platform, my stance on it changed. Final Round AI's Interview Copilot allows you to create various types of interviews across multiple fields, and can access analytical reports for each one. Here's how to try it out: Premium plans start at a steep $112 per month, but the company has a $10 million scholarship program to support job applicants. This provides those who secure job offers with the help of Final Round AI's Interview Copilot to receive a "scholarship" that waives 100% off subscription fees. This feels misleading and untrustworthy, considering how the platform advertises itself. It seems that when you subscribe to Final Round's "God Mode" plan (really?), you will be given six months of its cheapest subscription for "free" -- though you're required to pay $672 upfront. If you land a job utilizing its AI-powered software, you can claim a 100% scholarship, which will refund up to $672 (scholarship amount undetermined) four to six weeks after submission. Terms and conditions for reimbursement include a copy of your job offer with details including position title, start date and salary. At the time of writing, Final Round AI is also offering 10% off any subscription for students, veterans, first responders and medical professionals, as well as anyone recently laid off. This can't be used for the God Mode scholarship offer, or your scholarship will be disqualified. The actual free aspects of Final Round AI include access to potential interview questions, where you can enter a keyword concerning a company, job position and technical or general topic; an AI Resume Generator, which can provide Applicant Tracking System optimization (and is currently in beta mode); a ChatGPT-style AI Career Coach; and Guides, where it shows you how to launch a live or mock interview alongside a step-by-step visual example. While robust in quantity, I can't say these features offer anything incredibly innovative or new from other AI platforms. I don't think a virtual interview can truly gauge if you're the right fit for a company -- particularly when you're not in person. So while some may see aspects of Final Round AI's platform as a copout for knowledge and expertise, or a potential mishap (if the AI-generated text is incorrect), I see it as a real-time note-taking system with feedback. I have vivid memories of performing poorly in interviews because I was trying to do just that -- perform to the best of my abilities rather than have a conversation with another human. This has made me a staunch advocate for anything that can alleviate overwhelming feelings attached to the unknown. Final Round AI attempts to do just that. However, my two issues with Final Round AI are... significant. The "bright and shiny" aspect of a new company that is innovative, yet potentially problematic. With the number of lawsuits that have circulated in response to other areas of AI like voice impersonations and copyright infringement, I am wary of the safety concerns that may arise when it comes to the company's Interview Copilot -- an AI-powered tool that you can use on your end during your real interviews that gives you live tips on how to answer. For instance, if an interviewer isn't aware that artificial intelligence is listening and responding to their voice, where's the line between what's supportive and what's legal? And in the case of illegal use, who is liable -- companies like Final Round AI or the interviewee? Secondly, its free versus paid membership service is misleading. Final Round AI advertises itself for its Interview Copilot, with a button referencing the ability to "Get Started [for] Free." Yet, as soon as you navigate to this part of the platform, you're unable to do more than upload a resume before being prompted to "upgrade your membership." I respect transparency. As someone excited to trial this portion of the platform, this turned me off as a potential customer. With its platform barely a year old, time will likely answer these questions and hopefully rectify its other challenges. (Final Round AI didn't respond to my request for comment by the time of publication.) Until then, I wish anyone job seeking well. May your resumes be immaculate and your interviews be handled with confidence and ease... with Final Round AI's help or not. (Remember: Meditation is, actually, free.)
CNET
Mon, 24 Feb, 7:07 PM UTC
XAI: Make ML Models Transparent for Smarter Hiring Decisions
Recruiting is extremely tough as outsiders may disagree with you -- I've been there, sifting through resumes, trying to spot the perfect fit for a role. I've noticed more companies using machine learning to screen candidates and predict job success lately. The problem? When an algorithm rejects someone, there's often no clear reason why. That's where explainable AI (XAI) changes the game. Think of it as a recruiter's secret weapon -- it takes the mystery out of AI decisions and turns machine learning into a real hiring partner. Let's dive into why this matters and how to use it. Imagine your hiring funnel powered by an ML model. It flags a candidate as a "no-go," and the hiring manager asks, "What's the deal?" Without XAI, you're stuck saying, "Uh, the algorithm said so." With it, you can point to specifics -- like "their skills didn't match the job spec" or "their experience score was too low." It builds trust with the team and the candidate. I've also seen XAI save the day when debugging screwy models. Once, a tool I used kept tossing out great candidates because it overweighed location -- XAI caught that fast. Plus, with laws like GDPR, you might have to explain why someone didn't make the cut. XAI is your toolkit for cracking open ML decisions. It can show you which factors -- like years of experience or tech skills -- matter most. It'll break down a single candidate's rejection or give you the big picture of how the model's sizing up your talent pool. And yeah, a good chart makes it all click. Here's a quick example using Python, a decision tree, and SHAP (a library I swear by). We'll score candidates and see what's driving the numbers. I made up a dataset -- candidate experience, skills, and a "fit" score: Run it, and you'll see something cool. For a candidate with 5 years' experience and 4 skills, the plot might say experience boosted their score by 20 points, skills by 10. Want the full scoop on your pipeline? Try: Keep it straightforward -- decision trees are easier to unpack than deep learning for stuff like this. SHAP's killer for trees, but LIME's worth a look for other models. And don't let accuracy trump clarity -- hiring teams must understand why a candidate's a match. Look, in recruiting, we're always racing to snag the best talent before someone else does. When your ML tool's this shadowy black box, it's like a bad hire -- you can't trust it, and it might mess up everything. XAI flips that script, turning it into your go-to teammate. Platforms like LinkedIn jumped on this bandwagon; honestly, it's no shock. It's where smart hiring's headed, and I'm all in for it. XAI's been a total game-changer for me, cutting through the guesswork in recruiting tech. It's not just flashy charts -- it's knowing your model's not screwing over great candidates. The next time you're tinkering with your hiring stack, give it a whirl. You'll thank me later.
DZone
Thu, 27 Mar, 6:06 PM UTC
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