An AI hiring firm says it can predict job-hopping based on your interviews. The idea of “bias-free” hiring, already highly misleading, is being used by companies to shirk greater scrutiny for their tools’ labor issues beyond discrimination.
Since the onset of the pandemic, a growing number of companies have turned to AI to assist with their hiring.
The most common systems involve using face-scanning algorithms, games or other evaluations to help determine which candidates to interview.
Activists and scholars warn that these screening tools can perpetuate discrimination. However, the makers themselves argue that algorithmic hiring helps correct for human biases.
Algorithms can be tested and tweaked, whereas human biases are much harder to correct—or so the thinking goes.
In a December 2019 paper, researchers at Cornell reviewed the landscape of algorithmic screening companies to analyze their claims and practices. Of the 18 they identified with English-language websites, the majority marketed as a fairer alternative to human-based hiring. Thus suggesting that they were latching onto the heightened concern around these issues to tout their tools’ benefits and get more customers.
But discrimination isn’t the only concern with algorithmic hiring. Some scholars worry that marketing language that focuses on bias lets companies off the hook on other issues, such as workers’ rights. A new preprint from one of these firms serves as an important reminder. “We should not let the attention that people have begun to pay to bias/discrimination crowd other issues,” says Solon Barocas, an assistant professor at Cornell University and principal researcher at Microsoft Research, who studies algorithmic fairness and accountability.
The firm in question is Australia-based Sapia (Formerly PredictiveHire), founded in October 2013.
It offers a chatbot that asks candidates a series of open-ended questions. It then analyses their responses to assess job-related personality traits like “drive,” “initiative,” and “resilience.”
According to the firm’s CEO, Barbara Hyman, its clients are employers that must manage large numbers of applications, such as those in retail, sales, call centers, and health care.
As the Cornell study found, it also actively uses promises of fairer hiring in its marketing language. On its home page, it boldly advertises: “Meet Smart Interviewer – Your co-pilot in hiring. Making interviews super fast, inclusive and bias free.
As we’ve written before, the idea of “bias-free” algorithms is highly misleading. But Sapia’s latest research is troubling for a different reason. It is focused on building a new machine-learning model that seeks to predict a candidate’s likelihood of job-hopping. That is the practice of changing jobs more frequently than an employer desires. The work follows the company’s recent peer-reviewed research that looked at how open-ended interview questions correlate with personality.
The study used the free-text responses from 45,899 candidates who had used Sapia’s chatbot.
Applicants had originally been asked five to seven open-ended questions and self-rating questions about their past experience and situational judgment.
These included questions meant to tease out traits that studies have previously shown to correlate strongly with job-hopping tendencies, such as being more open to experience, less practical, and less down to earth. The company researchers claim the model was able to predict job hopping with statistical significance. Sapia’s website is already advertising this work as a “flight risk” assessment that is “coming soon.” Sapia’s new work is a prime example of what Nathan Newman argues is one of the biggest adverse impacts of big data on labor.
Machine learning for the win!
Machine-learning-based personality tests, for example, are increasingly being used in hiring to screen. This is to out potential employees who have a higher likelihood of agitating for increased wages or supporting unionisation. Employers are increasingly monitoring employees’ emails, chats, and data to assess which might leave and calculate the minimum pay increase to make them stay.
None of these examples should be surprising, Newman argued. They are simply a modern manifestation of what employers have historically done to suppress wages by targeting and breaking up union activities. The use of personality assessments in hiring, which dates back to the 1930s in the US, in fact began as a mechanism to weed out people most likely to become labor organizers. The tests became particularly popular in the 1960s and ’70s once organizational psychologists had refined them to assess workers for their union sympathies.
In this context, Sapia’s fight-risk assessment is just another example of this trend. “Job hopping, or the threat of job hopping,” points out Barocas, “is one of the main ways that workers are able to increase their income.” The company even built its assessment on personality screenings designed by organizational psychologists.
Barocas doesn’t necessarily advocate tossing out the tools altogether. He believes the goal of making hiring work better for everyone is a noble one and could be achieved if regulators mandate greater transparency.
By Karen Haoa, July 24, 2020, MIT Technology Review | https://www.technologyreview.com/
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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.
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All-in-one hiring platform
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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.