Rarely is hiring somebody a single decision, but one made from a number of smaller decisions along a journey to a final one. As recruitment has become more sophisticated as an industry, so has our understanding of what can be flawed about the decisions humans make including the bias and subjectivity we bring when screening and interviewing candidates. These are essentially human traits that even the most well-intentioned of us cannot escape.
This does not mean we have to eliminate humans from hiring decisions to make it fairer – that would be problematic too – but rather that we have to use technology at strategic moments in hiring to improve our decision making. Our tendency to be biased is often related to the pressure we are under to make faster decisions. Again, this is human. When looking at thousands of CVs for example, our brains create shortcuts for us to process information that, quite frankly, we are unable to absorb. So we start scanning things based on our own biases in an unconscious way picking out schools that appeal to us, experiences that sound similar, names that feel familiar and people who ‘seem’ like others that we know.
Predictive tools that parse and score CVs, and help hiring managers assess potential candidates are unfortunately not helpful here, because they too, learn from us to favour certain characteristics that we do from CV data. Ultimately using CV data replicates institutional and historical biases, amplifying disadvantages lurking in data points like what university was attended, what gender someone is, how old they are or even what recreational clubs they belong to. A well publicised example of this was when Amazon tried to build a recruiting engine based on observing patterns in resumes submitted to the company over a 10-year period. Most of them were men, a reflection of male dominance across the tech industry. The result: the input data informed the machine learning that it didn’t like women.
The better approach is to use objective data and bias mitigating technology at the right moments in a recruiting process. It’s a way of letting the algorithms do the hard work of delving into the details that humans miss when making decisions under time pressure using biased mental shortcuts. This way we can build better accuracy than if humans alone were making decisions on their own, particularly in the early decision making or top of the funnel recruiting, with much higher efficiency given the speed of algorithms. We still need to test constantly for bias in these hiring algorithms, but by utilising them at the right moment we can help hiring managers make better – more human – decisions.
“When making decisions, think of options as if they were candidates. Break them up into dimensions and evaluate each dimension separately. Then – Delay forming an intuition too quickly. Instead, focus on the separate points, and when you have the full profile, then you can develop an intuition.”
Daniel Kahneman
Psychologist & Nobel Laureate[1]
How do we help humans make better hiring decisions at Sapia?
- We use objective data
The ability to assess someone’s suitability to do a job is not made using CV data, but rather from information we gather from answering five open-ended questions via a text chat that is ‘blind’ i.e. no identifying information is given to the hiring manager. In this model everyone gets an interview. Using advanced Natural Language Processing (NLP), we can determine a lot about someone from analysing their text answers. While a standard Myers-Briggs assessment identifies 16 personality types, based on essentially answering repeated questions, this new way of looking at language can account for 400+ personality types and counting. There is no way a human brain could distinguish these differences in people. This means we can truly identify job fit for all the candidates we screen – without bias – based on what hiring managers have identified as the skills deemed necessary in their ideal candidates. These skills and abilities cannot be uncovered in any other way.
See our product in action here.
- We constantly test for bias
Being aware that bias can exist in any data is not enough, you need to constantly test your algorithms for any emerging patterns that mimic human bias. Using a number of tests we are continually looking at our results to make sure that we are not amplifying bias in any way. Our results have shown that it is possible to mitigate bias using algorithms for better hiring outcomes. A recent piece of research looking at the hiring of Aboriginal and Torres Strait Islander peoples, the Indigenous peoples of Australia showed that we can elevate marginalised groups. Other research we have done has also proved we create a fair outcome for people who have English as a Second Language
See our approach to Ai here
- We help you calibrate team hiring decisions
Ultimately, final hiring decisions do fall back on humans, but this is also where technology can also be used to guide and calibrate scoring that hiring managers make when interviewing candidates. Decisions backed by data minimises the risk of bias, making hiring conversations more robust, and less subjective. Using standardised scoring that is live, the impression a candidate makes on a hiring manager is ranked against other assessors, as the interview is being conducted. It’s not about replacing human decision makers, but elevating their ability to make smarter, more transparent decisions, we cannot make without the help of technology.
See how we can help humans interview. - Continuous learning via feedbackHuman decision making is unscalable. The more people you add to scale decisions, the more inconsistencies and biases you will be adding to the process. Moreover, humans are limited in their capacity to learn from objective feedback data such as which profiles of people work well in a given environment. This is where data-driven approaches like machine learning are far superior. Machine learning models are able to learn continuously from large amounts of feedback data, which candidate profiles are more likely to succeed than others. This ability to retain knowledge and then be able to explain how it arrives at a decision helps organisations to truly learn from their bad hires and keep nudging the hiring outcomes towards growth. Working together, recruiters and hiring managers can benefit from the learnings of AI in challenging their views and making the right hiring decisions.
- An interaction that is familiarText chat is how we truly communicate asynchronously, i.e. on your own time – we all do it everyday with our friends and family. It needs no acting; It is blind to how you look and sound. We all know how to chat. Candidates feel comfortable using chat, as they are in a familiar setting, unlike playing a neuroscience game, a one-way video recording or a psychometric test etc which are unfamiliar or artificial experiences. Many don’t enjoy them as they are made to behave in ways they usually don’t. This high engagement, which we capture via post interview feedback, is also a driving factor in capturing authentic data as candidate’s reflect and express in their own way.
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We cover this and so much more in our report: Hiring for Equality.
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[1] https://podcastnotes.org/2019/10/18/daniel-kahneman-decision-making/
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Blog
Reinventing the Competency Framework: A Data-Driven Approach for the AI Era
We can’t hide from reality anymore. Talent needs are shifting overnight, and AI is redefining what it means to work. Traditional talent frameworks are no longer fit for purpose. At Sapia.ai, we believe the future of talent strategy lies in a smarter, fairer, and more adaptive way of defining what great looks like.
Our AI hiring platform is built on the largest proprietary dataset of interview answers globally – we’re a data company at heart, and we’ve seen the power of data-driven people methodology in transforming how organisations hire and retain good talent.
So, when it came to building a new Competency Framework that could be leveraged globally for hiring for any role at any scale, of course, we used a ground-up, data-led methodology that bridges the gap between organisational psychology and AI.
Why Rethink Competency Frameworks?
Conventional frameworks are typically crafted through expert interviews and focus groups. While valuable, they tend to be subjective, static, and too slow to keep pace with evolving job demands. As roles become more fluid and technology augments or replaces task-based skills, organisations need a new way to understand the human capabilities that genuinely matter for performance.
We wanted to identify enduring, job-agnostic competencies that reflect what drives success in a modern workplace – capabilities like adaptability, resilience, learning agility, and customer orientation.
(Why competencies and not just skills? Read why here.)
Our Approach: Where AI Meets I/O Psychology
Sapia.ai’s methodology is rooted in the science of human behaviour but powered by cutting-edge AI. We asked two core questions:
- Can we make competency discovery agile, scalable, and evidence-based?
- Can we use AI to automate the process without losing the rigour of traditional psychology?
The answer to both: yes.
We began with a rich dataset of over 37,000 job descriptions across industries and role types. Using large language models (LLMs) and advanced NLP techniques, we extracted over 200,000 behavioural descriptors. These were distilled down through a four-step process:
- Behavioural Descriptor Extraction
- Clustering and Labeling
- Cluster Analysis by I/O Psychologists
- Thematic Categorisation and Definition of Competencies
This resulted in a refined list of 25 human-centric competencies, each with clear behavioural indicators and practical relevance across a wide range of roles.
Built to Scale. Built to Adapt.
Our framework is intelligent, but importantly, it’s adaptive. Organisations can apply this methodology to their own job descriptions to discover custom competencies. This bottom-up, role-data-led approach ensures alignment to real work, not just theoretical models.
And because the framework integrates directly with our AI-powered hiring tools, you get a connected system that brings your talent strategy to life.
Our framework comes to life in the following tools:
- Job Analyser – Starting with a job description, it creates a unique competency profile for each role to build tailored structured interviews in seconds.
- Structured Chat-based Interviews that assess candidates’ responses according to the competency profile for consistent candidate assessment.
- Talent Insights Reports from every interview with deep reasoning and explainability for fair and objective hiring decisions.
- Phai Career Coach for internal mobility and employee growth that considers their competency strengths and career aspirations.
The Future of Talent Acquisition & Development is Competency-First
Skills alone cannot predict success. Competencies do. As AI continues transforming how we work, Sapia.ai’s Competency Framework offers a scalable, scientific, and fair foundation for hiring and developing the talent of tomorrow.
Want to see how it works? Download the full framework.
Blog
It’s Time to Stop Hiring for Skills, and Start Hiring for Competencies
If you’re a CHRO or Head of Recruitment at an enterprise today, chances are you’ve been inundated with messages about the importance of “skills-based hiring.” LinkedIn’s recent Work Change Report (2025) is full of compelling data: a 140% increase in the rate at which professionals are adding new skills to their profiles since 2022, and a projection that by 2030, 70% of the skills used in most jobs today will have changed.
This is essential reading. But there’s a missed opportunity: the singular focus on “skills” fails to acknowledge the real metric that talent leaders need to be using to future-proof their workforce — competencies.
Skills vs Competencies: The Crucial Distinction
- Skills are task-specific capabilities. Think Python programming, Excel, or even negotiation.
- Soft skills refer to interpersonal or behavioural qualities like adaptability, communication, and resilience.
But skills on their own — even soft ones — are generic, disjointed, and often disconnected from real-world performance. In contrast:
- Competencies are clusters of skills, knowledge, behaviours and abilities that are observable, measurable, and context-specific.
Put simply, competencies answer the all-important question: Can this person apply the right skills, in the right way, at the right time, to deliver results in our environment?
Why Competencies Matter More Than Ever
The Work Change Report outlines a future where job titles are fluid, roles evolve quickly, and AI is a constant disruptor. This creates three massive challenges for hiring at scale:
- Roles are changing faster than static skill frameworks can keep up
- Job candidates may have non-linear, cross-functional backgrounds
- The shelf-life of technical skills is shrinking rapidly
Skills alone don’t tell us whether someone can succeed in a role that will look different 12 months from now. But competencies can. Because they measure not just what a person knows, but how they apply it.
Adaptive Talent: The New Competitive Advantage
The LinkedIn report highlights a critical insight: organisations now prioritise agility in entry-level hiring. And there’s a good reason for that. With professionals expected to hold twice as many jobs over their careers compared to 15 years ago, adaptability is not just a nice-to-have. It’s core to success.
But you can’t measure agility with a keyword on a CV. You measure it by looking at competencies like:
- Learning agility
- Change resilience
- Cross-functional collaboration
- Problem-solving in ambiguous contexts
When you shift the focus away from skills to behavioural competencies that can be defined, observed, and assessed in structured ways, you open yourself up to a much more dynamic and more useful way of managing talent.
Building a Competency-Based Talent Framework
To hire effectively at scale, particularly in a technology-driven world of work, talent leaders must shift their lens:
- Define Role-Specific Competencies: Move beyond job descriptions based on qualifications or vague skill sets. Break roles down into measurable competencies that reflect current and emerging performance expectations. This step is crucial for organisations to be able to accurately assess role-fit in the next stages. Sapia.ai does this automatically, taking job descriptions and building role-specific competency models in seconds.
- Assess Competencies Fairly and Objectively: Use structured behavioural interviews, ideally at scale. These provide a much more accurate picture of a candidate’s readiness than self-reported skills or credentials. Sapia.ai’s AI powered interviews enable competency assessment, at scale.
- Build Pathways for Development and Internal Mobility: A competency framework makes it easier to identify transferable strengths, development gaps, and future-fit potential. It gives employees clarity on how to grow within the business. Using an AI-powered coach can help ensure that talent is being continuously developed against the organisation’s competency framework.
The Future of Work Requires Depth, Not Just Breadth
LinkedIn’s data shows that people are learning more skills more quickly than ever. But the real question for talent leaders like you is: Are those skills being applied in ways that drive value? Are we hiring for task proficiency or performance?
The truth is that the organisations that will thrive in an AI-driven, skills-fluid economy aren’t the ones chasing the next hot skill. They’re the ones designing systems to identify, develop and scale competence.
Keen to Shift to Competencies, but Lacking a Framework?
Sapia.ai has developed a comprehensive Competency Framework using a data-driven approach. Download the full paper here.
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 👇