Previously, I’ve written about how the world of recruitment has evolved.
It seems that using AI could consign fantastical or over-optimised resumes to the dustbin of history, along with the Rolodex and fax machines.
But how do we go about selecting the perfect (or as close to perfect as possible) candidates from AI-created shortlists?
It should be so easy to learn how to conduct an AI interview that adds the human element to the AI selection process. The web is awash with opportunities to earn recruitment qualifications from a variety of bodies, both respected and dubious, especially focusing on AI interview techniques. There are so many manuals, guides, and blog posts on the best ways of interviewing, including the integration of AI for interviews. People have been interviewing people for hundreds of years, but now AI is adding a new dimension to this age-old practice.
And yet…
Why are there no perfect interviews?
We’ve all heard about bizarre interview questions (no explanation needed). We’ve felt the pain of people caught up in interview nightmares (from both sides of the desk). And we’ve scratched our heads and noses over the blogs on body language in face-to-face interviews(bias klaxon).
Even without the extremes, people have tales to tell. Did you ever come away from an interview for your ideal job, where something just felt wrong?
It’s clear that adding human interaction to the recruitment process is by no means straightforward. Highlighting these recurring problems doesn’t solve the underlying question, which is:
“We’ve used an algorithm to better identify suitable candidates. How do we ensure that adding the crucial human part of hiring doesn’t re-introduce the very biases that the algorithm filtered out?”
Start by asking better questions
Searching for “Perfect interview Questions” gives 167,000,000 results. Many of them include the Perfect Answers to match. So it’s not simply about asking questions that, once upon a time, were reckoned to extract truthful and useful responses.
Instead we want questions that will make the best of that human interaction, building on and exploring the reasons the algorithm put these candidates on the list. Our questions need to help us achieve the ultimate goal of the interview: finding a candidate who can do the job, fit with the company culture AND stay for a meaningful period of time.
It’s generally agreed that we get better interview answers by asking open questions. I’d expand on that. They should ideally be questions that don’t relate specifically to the candidate’s resume, or only at the highest level, to get an in-depth understanding.
We should try to avoid using leading questions that will give an astute candidate any clues to the answers we’re looking for. And we should probably steer clear of most, if not all, of the questions that appear on those lists of ‘Perfect Interview Questions’, knowing that some candidates will reach for a well-practised ‘Perfect Answer’. We want them to display their understanding of the question and knowledge of the subject matter. Not their ability to recall a pre-rehearsed answer.
And so, we need to remember that we’re looking for the substance of the answers we get, not the candidate’s ability to weave the flimsiest material into an enchanting story.
Five new interview questions to ask your AI-shortlisted candidates
So, here are some possible questions to get you thinking.
- We use an algorithm as part of our hiring process to ensure we’re not missing the right candidates. Considering everything you know about us and this rlie, why do you think the algorithm has suggested you might be a good hire?
- If asked, what would your colleagues in your current or previous role say about your work-related exchanges with them?
- Tell me about any of your previous personal or career achievements that you feel relate to this role
- We strive to be as diverse and inclusive as possible. Tell me about the places where you’ve most enjoyed working and the communication styles of people you’ve worked best with. Please expand on your reasons
- Can you recall a time you’ve needed to make a business decision with incomplete information? How did you move forward?
Of course, you’ll need to frame and adjust those questions to match the role and your company.
How AI for HR helps interviews – and interviewers – get closer to perfection
AI equips recruiters with impartial insights that resumes, questionnaires and even personality profiles can’t provide. Well-constructed, supervised algorithms overlook all the biases that every human has. And that can only be a good thing.
Statistically robust AI uses an algorithm, derived from business performance and behavioural science, to shortlist candidates. It can predict which ones will do well, fit well and stay. We can trust it to know what makes a successful employee, for our particular organisation and this specific role. It can tell us to invest effort with the applicants on that shortlist. However unlikely they seem at first glance.
So we can use all of our knowledge and skills to understand a candidate’s suitability and look beyond things that might have previously led us to a rejection.
AI is the recruiter’s friend, not a competitor. It can stop us wasting time chasing candidates who we think will make great hires but instead fail to live up to the expectation. And it can direct us to the hidden gems we might have otherwise overlooked.
Technology like AI for HR is only a threat if you ignore it.
Don’t be that company that still swears by dated processes because that’s the way it’s always been done. The opportunity here is putting technology to work, helping your organisation evolve for the better. The longer the delay, the harder it will be. So don’t be left at the back playing catch-up.
There are very few businesses these days that communicate by fax machines – and that’s for a reason. In a few years, you’ll look back and wonder “Why didn’t we all embrace Artificial Intelligence sooner?”
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Sapia.ai Wrapped 2024
It’s been a year of Big Moves at Sapia.ai. From welcoming groundbreaking brands to achieving incredible milestones in our product innovation and scale, we’re pushing the boundaries of what’s possible in hiring.
And we’re just getting started 🚀
Take a look at the highlights of 2024
All-in-one hiring platform
This year, with the addition of Live Interview, we’re proud to say our platform now covers screening, assessing and scheduling.
It’s an all-in-one volume hiring platform that enables our customers to deliver a world-leading experience from application through to offer.
Supercharging hiring efficiency
Every 15 seconds, a candidate is interviewed with Sapia.ai.
This year, we’ve saved hiring managers and recruiters hours of precious time that can now be used for higher-value tasks.
Giving candidates the best experience
Our platform allows candidates to be their best selves, so our customers can find the people that truly belong with them. They’re proud to use a technology that’s changing hiring, for good.
Leading the way in AI for hiring
We’ve continued to push the boundaries in leveraging ethical AI for hiring, with new products on the way for Coaching, Internal Mobility & Interview Builders.
Blog
Situational Judgement Tests vs. AI Chat Interviews: A Modern Perspective on Candidate Assessment
Choosing the right tool for assessing candidates can be challenging. For years, situational judgement tests (SJTs) have been a common choice for evaluating behaviour and decision-making skills. However, they come with limitations that can make the hiring process less effective and less inclusive.
AI-enabled chat-based interviews, such as Sapia.ai, provide organisations with a modern alternative. They focus on understanding candidates as individuals and creating a hiring experience that is both fair and insightful while enabling efficient screening and selection.
This shift raises important questions: Are SJTs still a tool that should be considered for volume hiring? And what do AI assessments offer in comparison?
1. The Static Nature of SJTs
Traditional SJTs use predefined multiple-choice questions to assess behavioural tendencies and situational knowledge. While useful for screening, these static frameworks lack the flexibility to adapt based on real-world performance data or evolving role requirements.
Once created, SJTs don’t adapt to new data or evolving organisational needs. They rely on fixed scenarios and responses that may not fully reflect the dynamic realities of modern workplaces, and as a result, their relevance may diminish over time.
AI-enabled chat interviews, on the other hand, are inherently adaptive. Using machine learning, these tools can continuously refine their models based on feedback from real-world outcomes such as hiring or turnover data. This ability to evolve ensures the assessments align with organisations’ needs.
2. Richer Data Through Open-Ended Responses
One of the main critiques of SJTs is their reliance on multiple-choice responses. While structured and straightforward, these options may not capture the full scope of a candidate’s thinking, communication skills, or problem-solving ability. The approach is often limiting, reducing complex human behaviour to a few predefined choices.
AI-enabled chat interviews work more holistically and dynamically. These tools provide a more complete picture of a person by allowing candidates to answer questions in their own words. Natural language processing (NLP) analyses their responses, offering insights into personality traits, communication skills, and behavioural tendencies. This open-ended format lets candidates express themselves authentically, giving employers a deeper understanding of their potential.
3. The Candidate Experience: Stressful or Supportive?
SJTs often include time constraints and rigid formats, which can create pressure for candidates. This is especially true when candidates feel forced to choose options that don’t fully reflect how they would actually behave. The process can feel impersonal, even transactional.
In contrast, chat-based interviews are designed to be conversational and low-pressure for candidates. By removing time limits and adopting a familiar chat interface, these tools help candidates feel more at ease. They also frequently include personalised feedback, turning the assessment into a valuable experience for the candidate, not just the employer.
4. Addressing Bias and Fairness
Traditional SJTs are prone to transparency issues, as candidates can often identify and select the “best practice” answers without revealing their true tendencies. Additionally, static test designs can unintentionally embed bias; due to the nature of the timed test, SJTs have been found to disadvantage some groups.
AI chat interviews, when developed ethically within a framework like Sapia.ai’s FAIR Hiring Framework, eliminate explicit bias by relying solely on the content of a candidate’s responses. Their machine learning models are continuously validated for fairness, ensuring that hiring decisions are free from subjective judgments or irrelevant demographic factors.
5. An Assessment That Improves Over Time
Workplaces are constantly changing, and hiring tools need to keep up. SJTs’ fixed nature can make them less effective as roles evolve or organizational priorities shift. They provide a snapshot but not a dynamic view of what’s needed.
AI-enabled chat interviews are built to adapt. With feedback loops and continuous learning, they incorporate real-world hiring outcomes—like retention and performance data—into their models. This ensures that assessments stay relevant and effective over time.
Rethinking Candidate Assessment
As hiring demands grow more complex, so does the need for tools that can capture the whole person, not just their response to hypothetical scenarios. While SJTs have played an important role in hiring practices, they are increasingly being replaced by tools like AI-enabled chat interviews.
These modern approaches provide richer data, adapt to changing needs, and create a richer and more engaging experience for candidates. Perhaps most importantly, they emphasise fairness and inclusivity, aligning with the growing demand for unbiased hiring practices.
For organisations evaluating their assessment tools, the question isn’t just which method is “better.” Understanding the specific needs of your roles, teams, and candidates will help you choose tools that help you make decisions that are both informed and equitable.
Blog
Keeping Interviews Real with Next-Gen AI Detection
It’s our firm belief that AI should empower, not overshadow, human potential. While AI tools like ChatGPT are brilliant at assisting us with day-to-day tasks and improving our work efficiency, employers are increasingly concerned that they’re holding candidates back from revealing their true, authentic selves in online interviews.
As an assessment technology provider, we are responsible for ensuring the authenticity and integrity of our platform. That’s why we’re thrilled to unveil the latest upgrade to our flagship Chat Interview: the AI-Generated Content Detector 2.0. With groundbreaking accuracy and a candidate-friendly design, this innovation reinforces our mission to build ethical AI for hiring that people love.
Artificially Generated Content (AGC) is content created by an AI tool, such as ChatGPT, Claude, or Pi. We initially rolled out the first version of our AGC detector last year and have continued to improve it as our data set has grown and these AI tools have evolved.
What’s New?
Our updated AGC Detector 2.0 achieves an impressive 98% detection rate for AI-assisted responses, with a false positive rate of just 1%. This gives organisations peace of mind that they’re getting the most authentic assessment of every candidate.
This cutting-edge system builds on Sapia.ai’s proprietary dataset of over 2 billion words, derived from more than 20 million interview question-answer pairs spanning diverse roles, industries, and regions. It’s trained on real-world data collected before and after the release of tools like ChatGPT, ensuring it remains robust and reliable even as AI tools evolve.
The Challenge of AI in Chat-based Interviews
Our data shows that around 8% of candidates use tools like GPT-4 to generate responses for three or more interview questions. While these tools may offer a quick way for candidates to complete their interview, they can inadvertently hide a person’s true personality and potential – qualities our customers are most interested in understanding through our platform. In fact, research from Sapia Labs shows that these tools have their own personality traits, which may be quite different from the candidate applying for the role.
For Candidates: Enabling Authenticity
When a response is flagged as potentially AI-generated, the system doesn’t disqualify candidates. Instead, a real-time warning pops up, allowing them to revise their answers or submit them as-is. This ensures that candidates are encouraged to present themselves authentically, reflecting their unique communication styles and sharing their genuine experiences.
For Hiring Teams: Actionable Insights
Responses flagged as AI-generated are highlighted in the candidate’s Talent Insights profile, accessible via Sapia.ai’s Talent Hub or ATS integrations. These insights give hiring teams the transparency to make informed decisions, fostering trust while accelerating hiring timelines.
Built on Unmatched AI Interview Expertise
“Our detection model’s strength lies in its foundation of real-world interview data collected from diverse roles and regions,” says Dr Buddhi Jayatilleke, Sapia.ai’s Chief Data Scientist. This depth of understanding enables the AGC Detector to maintain its industry-leading accuracy – even when candidates subtly modify AI-generated answers to appear more human.
Why This Matters
The AGC Detector 2.0 embodies Sapia.ai’s commitment to ethical AI that amplifies human potential. As our CEO Barb Hyman explains:
“The hiring landscape has fundamentally changed since ChatGPT, but our commitment remains clear: AI should amplify human potential, not penalise it. This breakthrough fosters authentic hiring conversations. Our real-time warning system helps candidates make better choices and gives enterprises confidence in their selection decisions.”
Testing and Validation of the AGC Detector 2.0
The new detector has been rigorously tested on over 25,000 interview responses generated by humans and leading AI models like GPT-4, Claude-3.5, and Llama-3. The results speak for themselves, reinforcing the reliability and fairness of this game-changing technology.
Fairness & Transparency in AI-Enabled Hiring
By detecting AI-generated content while allowing candidates to correct their responses, our AGC Detector 2.0 ensures every applicant has the chance to put their best, most authentic foot forward when applying for a role powered by Sapia.ai. For enterprises, it provides confidence in the integrity of their hiring decisions and ensures they’re connecting with real candidates at scale.