Faking is a common issue with traditional self-report assessments in personnel selection (Levashina et al., 2014).
The major concern with faking is that it may affect construct and criterion-related validity (Tett & Simonet, 2021). Concerningly, some research reports the prevalence of self-report faking to be as high as 30-50% depending on the assumed faking severity (Griffith et al., 2007).
In this paper, we examine a parallel adversarial input type in modern text/chat-based interviews: plagiarism. Plagiarism poses a threat similar to faking in self-reports impacting construct and criterion-related validity. Furthermore, both plagiarism and faking impact fairness. The rank order of applicants may be altered by both practices, thereby changing the hiring decisions (Levashina et al., 2014).
While not studied exclusively in the selection space, plagiarism has been a major concern for the education sector and extensively studied in the literature (Park, 2003). One aspect that has received considerable attention is gender differences in plagiarism. Results remain inconclusive, with some evidence that men are more likely to plagiarize than women (Jereb, et al, 2018; Negre et al., 2015).
We also explore differences in plagiarism rates across different job families and device types (i.e., mobile vs. desktop).
Data from over 200,000 candidates (56% female) who applied to various organizations across the world. Candidates participated in an online chat-based structured interview, answering 5-7 open-ended questions on the Sapia Chat Interview™ platform. Over 1 million individual textual answers were checked against answers from past candidates (over 6.4 million answers) for plagiarism. Plagiarism detection calculates the Jaccard similarity coefficient between the new submission and all existing answers, and answers resulting in a Jaccard coefficient (Wang et al., 2013) over 0.75 were marked as plagiarized and flagged for hiring manager review.
Results show that 3.28% of candidates plagiarized at least one answer, which is significantly lower than the up-to 30-50% of candidates estimated to be faking self-report measures (Griffith et al., 2007).
Consistent with previous findings on self-report faking, males plagiarized significantly more than females. Plagiarism rates also differed significantly across role families, with the highest level of plagiarism observed among candidates who applied to ‘Call center sales’ roles and the lowest plagiarism rates observed for ‘Graduate’ roles. Additionally, we found candidates answering on a mobile phone plagiarized significantly higher than those using a desktop computer.
This work represents an important first step in investigating plagiarism detection in online, open-text chat interviews. While the prevalence is much lower than faking in self-reports, there are still fairness implications, especially given men are more likely to plagiarism than women. This is why it is so important to flag candidates who plagiarize so the hiring manager is made aware and can manually review their responses.
References:
Griffith, R. L., Chmielowski, T., & Yoshita, Y. (2007). Do applicants fake? An examination of the frequency of applicant faking behavior. Personnel Review, 36(3), 341–355.
Jereb, E., Urh, M., Jerebic, J., & Šprajc, P. (2018). Gender differences and the awareness of plagiarism in higher education. Social Psychology of Education : An International Journal, 21(2), 409–426.
Levashina, J., Weekley, J. A., Roulin, N., & Hauck, E. (2014). Using Blatant Extreme Responding for Detecting Faking in High-stakes Selection: Construct validity, relationship with general mental ability, and subgroup differences. International Journal of Selection and Assessment, 22(4), 371–383.
Negre, J. S., Forgas, R. C., & Trobat, M. F. O. (2015). Academic Plagiarism among Secondary and High School Students: Differences in Gender and Procrastination. Comunicar. Media Education Research Journal, 23(1).
Park, C. (2003). In Other (People’s) Words: Plagiarism by university students–literature and lessons. Assessment & Evaluation in Higher Education, 28(5), 471–488.
Tett, R., & Simonet, D. (2021). Applicant Faking on Personality Tests: Good or Bad and Why Should We Care? Personnel Assessment and Decisions, 7(1).
Wang, S., Qi, H., Kong, L., & Nu, C. (2013). Combination of VSM and Jaccard coefficient for external plagiarism detection. 2013 International Conference on Machine Learning and Cybernetics, 04, 1880–1885.
Related
Blog
The AGC Debate: Are AI-Written Interview Answers a Red Flag or Smart Strategy?
Every day, we read stories of increased fake or AI-assisted applications. Tools like LazyApply are just one of many flooding the market, driving up applicant volumes to never-before-seen levels.
As an overwhelmed hiring function, how do you find the needle in the haystack without using an army of recruiters to filter through the maze?
At Sapia.ai, we help global enterprises do just that. Many of the world’s most trusted brands, such as Qantas Group, have relied on our hiring platform as a co-pilot for better hiring since 2020.
Our Chat Interview has given millions of candidates a voice they wouldn’t have had – enabling them to share in their own words why they’re the best fit for the role. To find the people who belong with their brands, our customers must trust that their candidates represent themselves. Thus, they want to trust that our AI is analysing real human answers—not answers from a machine.
The Rise of GPT
When ChatGPT went viral in November 2022, we immediately adopted a defensive strategy. We had long been flagging plagiarised candidate responses, but then, we needed to act fast to flag responses using artificially generated content (‘AGC’).
Many companies were in the same position, but Sapia.ai was the only company with a large proprietary data set of interview answers that pre-dated GPT and similar tools: 2.5 billion words written by real humans.
That data enabled us to build a world-first:- an LLM-based AGC detector for text-based interviews, recently upgraded to v2.0 with 99% accuracy and a false positive rate of 1%. An NLP classification model built on Sapia.ai proprietary data that operates across all Sapia.ai chat interviews.
Full Transparency with Candidates
Because we value candidate trust as much as customer trust, we wanted to be transparent with candidates about our ability to detect artificially generated content (AGC). As an LLM, we could identify AGC in real time and warn candidates that we had detected it.
This has had a powerful impact on candidate behaviour. Since our AGC detector went live, we have seen that the real-time flagging acts as a real-time disincentive to use tools like ChatGPT to generate interview responses.
The detector generates a warning if 3 or more answers are flagged as having artificially generated content. The Sapia.ai Chat Interview uses 5 open-ended interview questions for volume hiring roles, such as retail, contact centre, and customer service, and 6 questions for professional roles, such as engineers, data scientists, graduates, etc.
Let’s Take a Closer Look at the Data…
We see that using our AGC detector LLM to communicate live with candidates in the interview flow when artificial content has been detected has a positive effect on deterring candidates from using AI tools to generate their answers.
The rate of AGC use declines from 1 question flagged to 5 questions – raising the flag on one question is generally enough to deter candidates from trying again.
The graph below shows the number of candidates, from a total of almost 2.7m, that used artificially generated content in their answers.
Differences in AGC Usage Rate by Groups
We see no meaningful differences in candidate behaviour based on the job they are applying for or based on geography.
However, we have found differences by gender and ethnicity – for example, men use artificially generated content more than women. The graph below shows the overall completion ratios by gender – for all interviews on the left and for interviews where the number of questions with AGC detected is 5 or more on the right.
Perception of Artificially Generated Content by Hirers.
We’re curious to understand how hirers perceive the use of these tools to assist candidates in a written interview. The creation of the detector was based on the majority of Sapia.ai customers wanting transparency & explainability around the use of these tools by candidates, often because they want to ensure that candidates are using their own words to complete their interviews and they want to avoid wasting time progressing candidates who are not as capable as their chat interview suggests.
However, some of our customers feel that it’s a positive reflection of the candidate, showing that they are using the tools available to them to put their best foot forward.
It’s a mix of perspectives.
Our detector labels it as the use of artificially generated content. It’s up to our customers how they use that information in their decision-making processes.
This concept of having a human in the loop is one of the key dimensions of ethical AI, and we ensure that it is used in every AI-related hiring product we build.
Interested in the science behind it all? Download our published research on developing the AGC detector 👇
Research Paper Download: AI Generated Content in Online Text-based Structured Interviews
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:
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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.
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Improved Candidate Satisfaction: With a 9/10 satisfaction score from over 55,000 interviews, candidates appreciate the fairness and transparency of the process.
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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.