Faking or cheating by candidates in the selection process is not a new phenomenon. Depending on the selection method, candidates may fake responses to improve their chances of progressing to the next stage. For instance, some candidates incorporate keywords into their resume to manipulate resume parsers, provide socially desirable responses in a personality test, and utilize impression management in face-to-face interviews. One study found that 70% of a surveyed sample of 1914 employees admitted to lying in their resume, with 76% on cover letters and 80% on interviews.
The rise of generative AI, particularly Large Language Models (LLM) like ChatGPT, has introduced a whole new way for candidates to cheat, posing a serious threat to the fairness and validity of online assessments. Candidates can now generate a whole new resume or use these tools to cheat on online assessments. Especially in asynchronous online chat interviews, the use of AI-generated content (AGC) can seriously impact both the effectiveness and the fairness of the outcomes.
Sapia.ai has been innovating in the space of text chat based structured interviews since 2019. Our flagship AI screening product, Chat Interview™, is used by leading brands to increase the efficiency, fairness, diversity, and candidate experience at the top of the funnel filtering in high-volume recruitment. Candidates’ responses are already scanned for plagiarized content and flagged for the attention of recruiters or hiring managers, letting a human take action. Our work has also shown that only an average of 3% of candidates plagiarize across different role families, showing that the majority of the candidates want to stay authentic.
To combat the latest cheating challenge, researchers at Sapia.ai have developed a novel way to distinguish between interview responses generated by AI, such as ChatGPT, and genuine human-written responses. They leveraged Sapia.ai’s vast proprietary dataset of over 12 million human-written interview answers in training this detector (over 1.0 billion words). The detector capitalizes on a subtle distinction to identify responses that may have been generated by AI rather than the candidate. By observing the probability discrepancy between AI-generated and human-written text after random perturbations (a form of randomly changing words), the detector can identify, with high accuracy, whether a response is AI-generated or not. The AGC detection model proved highly effective, achieving a very high accuracy (ROC-AUC of 94.49%).
Based on the AGC detector outcomes, Sapia.ai researchers analyzed responses from over half a million candidates who participated in online chat-based structured interviews between January and September 2023 to understand the scope of the problem. January 2023 was chosen as the start of the period as ChatPT had already started getting popular by that time. During this period, 1.16% of candidates were flagged for using AGC in all five questions asked in the Chat Interview™, while 20.20% had at least one response likely generated by AI.
Now, detection is only one aspect of the solution. Deterring candidates from using AGC, on the other hand, is the better way to address this problem. To deter candidates from resorting to AI-generated content, we implemented several methods: instructions were added to encourage candidates to write their own answers, and warnings about tracking plagiarism attempts were included. Additionally, the ability to paste responses was disabled, and pop-up warnings were introduced when answers were flagged as using AGC.
With the implemented deterrence methods, we observed a drastic decline in AGC rates, showing that these measures have a tangible impact on curbing cheating. The weekly average of the candidates flagged for all five questions fell as low as 0.20% from a high of 2.4% (a tenfold reduction) prior to implementing the deterrence mechanisms. Further, we observed a clear alignment between the timing of the implementation of deterrents and the reduction in AGC rates, emphasizing the effectiveness of these measures in encouraging candidates to be themselves when completing their Chat Interviewf.
As technology continues to evolve, so do the challenges in maintaining the integrity of job interviews and assessments. The addition of AI-generated content poses a serious threat, but innovative solutions, such as the AGC detector and deterrence methods, show how Sapia.ai effectively de-risks cheating via cutting-edge innovation, in order to maintain the fairness, validity, and reliability of the hiring process.
Related
Blog
Joe & the Juice Partners with Sapia.ai, Scaling an Exceptional Candidate Experience and Cutting Time to Hire
Read the full press release about the partnership here.
Joe & the Juice, the trailblazing global juice bar and coffee concept, is renowned for its vibrant culture and commitment to cultivating talent. With humble roots from one store in Copenhagen, now with a presence in 17 markets, Joe & The Juice has built a culture that fosters growth and celebrates individuality.
But, as their footprint expands, so does the challenge of finding and hiring the right talent to embody their unique culture. With over 300,000 applications annually, the traditional hiring process using CVs was falling short – leaving candidates waiting and creating inefficiencies for the recruitment team. To address this, Joe & The Juice turned to Sapia.ai, a pioneer in ethical AI hiring solutions.
A Fresh Approach to Hiring
Through this partnership, Joe & The Juice has transformed its hiring process into an inclusive, efficient, and brand-aligned experience. Instead of faceless CVs, candidates now engage in an innovative chat-based interview that reflects the brand’s energy and ethos. Available in multiple languages, the AI-driven interview screens for alignment with the “Juicer DNA” and the brand’s core values, ensuring that every candidate feels seen and valued.
Candidates receive an engaging and fair interview experience as well as personality insights and coaching tips as part of their journey. In fact, 93% of candidates have found these insights useful, helping to deliver a world-class experience to candidates who are also potential guests of the brand.
“Every candidate interaction reflects our brand,” Sebastian Jeppesen, Global Head of Recruitment, shared. “Sapia.ai makes our recruitment process fair, enriching, and culture-driven.”
Results That Matter
For Joe & The Juice, the collaboration has yielded impressive results:
-
33% Reduction in Screening Time: Pre-vetted shortlists from Sapia.ai’s platform ensure that recruiters can focus on top candidates, getting them behind the bar faster.
-
Improved Candidate Satisfaction: With a 9/10 satisfaction score from over 55,000 interviews, candidates appreciate the fairness and transparency of the process.
-
Bias-Free Hiring: By eliminating CVs and integrating blind AI that prioritizes fairness, Joe & The Juice ensures their hiring reflects the diverse communities they serve.
Frederik Rosenstand, Group Director of People & Development at Joe & The Juice, highlighted the transformative impact: “Our juicers are our future leaders, so using ethical AI to find the people who belong at Joe is critical to our long-term success. And now we do that with a fair, unbiased experience that aligns directly with our brand.”
Trailblazing for the hospitality industry
In an industry so wholly centred on people, Joe & the Juice is paving the way for similar brands to adopt technology that enables inclusive, human-first experiences that can reflect a brand’s core values.
If you’re curious about how Sapia.ai can transform your hiring process, check out our full case study on Joe & The Juice here.
Blog
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.