Frontier technology that puts people at the heart of their recruitment solution is rewarded for its ground-breaking approach that also solves for bias and reduces recruitment costs.
Melbourne, Australia, September 21 – Sapia, a text-based AI recruitment solution has been recognised globally for its commitment to creating a hiring experience that is empowering and motivating to the individual and which enhances your company’s brand.
The TIARA Talent Tech Star, awarded to Sapia, honours the most exemplary businesses globally in the talent acquisition industry.
Selected from a group of international finalists as a bold and innovative startup, Sapia was deemed top of the class. All for having demonstrated the value and impact of their solution at a time when agency and in-house recruiters are embracing technology and new ways of working.
The Candidate Experience Solution of the Year Award recognised Sapia as
“a matching solution that could fundamentally change the way
candidates experience recruitment, delivering valuable insight to both the
employer and the candidate whether they are recruited or not.”
The judges included the Head of Search & Staffing UK&I and EMEA at LinkedIn, Sales and Marketing Director of ManpowerGroup UK, and Head of Innovation and Transformation at PwC.
Sapia awarded for solving a core pain point
Sapia CEO Barb Hyman said the team was honoured to receive industry recognition for their candidate experience solution which was a core pain point that the company solved along with bias and recruitment costs.
“For far too long as an industry HR has failed so many jobseekers by not giving everyone an equal chance to prove themselves and then ghosting those who do make it through to interviews,” Hyman said.
“With Sapia, everyone gets a chat interview and is treated equally by replying to text-based questions on their phone, without any demographic data being used to make hiring decisions.”
She said the company’s goal around candidate experience is to be recognised as the most inclusive recruitment solution at scale. The team has done extensive testing on how different groups respond to chat-based interviews. This includes a range of candidates from a multitude of gender, race and language backgrounds. For these groups, the experience has been transformative. For candidates who might otherwise feel intimidated by a video format feel safe and comfortable interviewing by chat. The demographics collected on this front are only used in reporting for HR leaders against DEI targets, and not in any hiring decisions.
“We hold ourselves to incredibly high standards when it comes to creating an inclusive product, and ultimately it’s placing people at the core of what we do, that sets us apart from others, and makes our solution so successful for our customers.”
The Qantas Group, the Iceland Group, Telefonica, Bunnings and other trusted consumer brands have seen dramatic improvements from applying Sapia to their hiring/ promotion decisions.
About Sapia
Sapia (Formerly PredictiveHire) is a frontier technology solution which solves for three pain points in recruiting: bias; candidate experience, and efficiency. With only five free-text behavioural questions taking around 20 mins, and using over 80 features extracted from the candidate responses, our predictive models assign a “suitability” score to each candidate. To date, 400,000 candidates looking for roles in retail, healthcare, customer service, hospitality, contact centres and graduate roles across 34 countries have experienced Sapia with positive feedback averaging 99%.
Media Contact | Barb Hyman, CEO barb@sapia.ai
Have you seen the 2020 Candidate Experience Playbook?
Finally, if there was ever a time for our profession to show humanity for the thousands that are looking for work, that time is now.
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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.
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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.
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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.
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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.
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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.
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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.