Recruiters: The corporate hiring machine is evolving. Can you feel it?
As recently as a year ago, many top companies still selected candidates based on the most misleading of heuristics: The school they attended.
Harvard? Right this way! Community college? No thanks, we don’t take your kind around here.
This Pearson Hardman-style hiring strategy may have ‘worked’ in the past. Not any more, for two reasons: A) the talent isn’t out there, and B) everyday people expect a better standard of hiring fairness. They know that opportunity isn’t distributed equally, and that elite colleges are more a proxy for privilege than actual performance potential.
(Funny that it took a labor shortage to show companies that potential can come from anywhere. Psychologists and sociologists have known it, and have been saying it, for decades.)
Regardless, you’ve got LinkedIn CEO Ryan Roslansky telling Fortune that its company is favouring soft skills over college degrees, because such a practice creates a ‘much more efficient, equitable labor market, which then creates better opportunities for all’. He’s right about this approach. Even if you take away the benefits to diversity and inclusion, it makes sense purely mathematically: Now your hiring pool has increased from a few hundred thousand candidates to, at the very least, millions.
Soft skills are more useful, and fairer, than resumes
Resumes foster bias. Despite this fact, we insist on using them. Why? Because, until now, there hasn’t been a compelling reason not to. You could screen, interview, hire, and get warm bodies in seats with relative ease. Business could go on. Consequently, bias became a can that could be continually kicked down the road. Not anymore, for reasons discussed above.
The same is true for hire quality. Google ‘how to measure quality of hire’ and you’ll get a million different answers. Some advocate the for speed- or time-to-productivity approach; others say it’s about measuring ‘culture fit’. One or both of those might be true, but that’s beside the point: hire quality is nebulous not by its nature, but because the inputs (i.e. resumes) are messing with the outcomes.
This is why measuring soft skills is so important. Here’s an example:
We know that conscientiousness (that is, the propensity for someone work diligently and systematically on tasks) is a good predictor of on-the-job success. We also know that structured interviews are the best explainer (at 26%) of employee performance (versus previous job experience, which explains just 3%).
We might construct a valid candidate interviewing and vetting process based on these two facts alone. Fundamentally, we know that if A) we look for conscientiousness, and B) we do it in a structured, fair, repeatable way, we’ll get good candidates. Hire quality will take care of itself. Good inputs, good outcomes. Voila.
(It’s not quite that simple, but you get the point: There are reliable, proven ways to ensure validity, and the two examples cited above are very real and useable.)
Instead we rely on unstructured interviews, unruly hiring managers, and resumes – none of which can determine how hard-working a candidate is. Bad inputs that create bad outcomes. Consequently, we regularly examine hire quality and wonder why we struggle to measure it, or worse, connect it to the wider financial outcomes of our business.
So how do you measure soft skills?
Let’s keep this as simple as possible.
- Use structured interviews. Aside from the long-standing research above, an Aptitude Research report from this year found that structured interviews are 52% more likely to result in good hire quality.
- Ask questions based on known personality traits and behaviors, because these reliably point to high job performance. You want to know things like conscientiousness, empathy, emotionality, and so on.
Our free job interview rubric contains more than 20 questions designed by our psychologists to help you uncover hire quality. Get it here, use it, and let us know how you found it.
Your companion in accurate measurement of soft skills
There are millions of ways to assess for soft skills in interviews, just as there are millions of ways to calculate quality of hire. You may get some success by going it alone, but humans are, historically speaking, terrible at accurately assessing personality traits (and therefore, hire quality).
Our Ai Smart Interviewer does this very thing. Using deep, always-evolving personality science, our platform interviews and assesses candidates for desirable soft skills and behaviors, and even matches the resultant talent profiles to your company values.
Of course, the benefit is that hire quality is achieved and proven for you – you don’t have to worry about biased interviewers, bad questions, enforcing consistent processes, and the other headaches of recruitment. With that time back, you can focus on your people.
Or, think about it this way: LinkedIn is getting really smart with its hiring. Other companies like Apple, Delta, and IBM are too. Will you be left behind?
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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.