To find out how to interpret bias in recruitment, we also have a great eBook on inclusive hiring.
Once upon a time we were all happily employed and worked in our jobs until we reached the age of 65. Then we retired with a gold watch and lived happily ever after.
While that’s not quite the way it really happened, the reality is aging workers are faced with a very different story today. While the ability to ‘retire’ seems to move further out of reach, many people are faced with the challenges of needing to work longer.
A 2020 report conducted by LinkedIn found that nearly half of the baby boomers engaged in their survey believed that their age was the main reason their job applications had been rejected by an employer.
Earlier, a 2015 survey by the Australian Human Rights Commission found that 27 per cent of older people had recently experienced or witnessed age discrimination in the workplace, most often during the hiring process.
When you think that many of those will need to work for a further 20 years, their classification as older workers seems discriminatory in itself.
While ‘ageism’ tends to be more of a problem for older workers – shouldn’t we be calling them more experienced workers? Age discrimination can also affect younger workers. Employers might discriminate against younger job seekers, for example, because they believe they won’t be committed to the role or will move on to another job quickly.
Over the past 20-25 years, the number of post-graduates achieving master’s degrees has almost doubled.
But does a potentially over-qualified ‘green’ hire necessarily trump the experience that an older employee has gained through the university of life and years working in a role?
What ‘qualifications’ have they earned and learned that formal education could never provide?
A textbook definition of age discrimination from the website of Shine Lawyers is “where a person is treated less favourably than another person of a different age in circumstances that are the same or not materially different. The person may be treated differently due to their actual age, or due to a characteristic that pertains or is imputed to pertain to persons of that age. Further, age discrimination can occur when an employer places conditions, requirements or practices that are not reasonable and have the effect of disadvantaging a person or persons of a certain age.”
While in Australia employment laws are in place to protect employees from all forms of discrimination at all stages of employment – from recruitment through to redundancy or retirement – age discrimination can creep in at any time. It can happen when decisions are being made about:
Direct discrimination is when someone is treated differently or less favourably than another person in the same situation because of their age.
For example:
Indirect discrimination can be less obvious than direct discrimination. It describes the situation where an organisation has a particular policy, job requirements or way of working that would appear to apply to everyone but which puts a person or group of people at a disadvantage because of their age.
For example:
This is when discrimination crosses a line to become dangerous – for those being discriminated against, of course, but also for the employer that risks potential criminal charges and reputational damage. Harassment happens when employers, managers or colleagues make people feel humiliated, offended or degraded.
For example:
A step up from harassment, victimisation is when individuals are treated poorly because they have made a formal complaint about age discrimination and the way they have been harassed, overlooked for promotion or otherwise discriminated against. Colleagues or co-workers who have also supported someone in their discrimination complaint may also be victimised by their managers or employers.
In a range of global jurisdictions including the US, the EU, UK and in nations across Asia-Pacific such as New Zealand and Australia, discrimination laws are designed to protect all people from age discrimination in many areas of life – getting an education, accessing services, renting a property, accessing and using public facilities… and protecting people from discrimination at work.
The laws cover all sorts of employers and employees across private sector and government, charities and associations and all part-time, full time or casual workers and contractors.
Taking positive steps to address age discrimination can help organisations attract, motivate and retain good staff while building your reputation and brand as an equal opportunity employer.
Starting with legal obligations, there are a few key areas that employers and recruiters should address to minimise age discrimination:
Perhaps the most important place to tackle age discrimination head-on is where it potentially begins and ends – in the recruitment process.
The ultimate goal in overcoming discrimination in the workplace is to build a culture that thrives on diversity and a team that values the benefits diversity brings.
Sapia helps organisations start where they intend to finish by removing the potential for a wide range of biases – including age discrimination – from top-of-funnel interview screening.
Our Artificial Intelligence enabled chat interview platform offers blind screening at its best. It solves bias by screening and evaluating candidates with a simple open, transparent interview via an automated text conversation. Candidates know text and trust text and questions can be tailored to suit the requirements of the role and the organisation’s brand values.
People are more than their CV and their age. Candidates tell us they appreciate the opportunity to tell their story in their own words, in their own time. In fact, Sapia only conversational interview platform with 99% candidate satisfaction feedback.
Unlike other pre-employment assessments, Sapia has no video hookups, visual content or voice data. No CVs and no data extracted from social channels. All of which can be triggers for discrimination and bias – unconscious or otherwise.
Sapia’s solution is designed to provide every candidate with a great experience that respects and recognises them as the individual they are. It won’t know (or care) whether a candidate is 18 or 58, male or female, tall or short, Asian or Caucasian. What it will know is whether a person is a right fit for your organisation.
This case study graph demonstrates the effectiveness of Sapia’s platform in removing age bias from the candidate shortlisting process. While Sapia specifically excludes age data from the screening process, the data listed here was extracted from the client’s ATS after the hiring process was complete to check for any bias. This data comes from HIRED people, hence the high YES rate.
The left-hand column shows the number of applicants sorted by age groupings. In this sample, there are ±500 people over 50 – a group that often reports age discrimination.
The middle column shows the percentage of people in each group who were allocated a green for go ‘yes’ recommendation for the role, an amber ‘maybe’ or a red ‘no’.
The predictive model (and corresponding Sapia scores) reveals no age bias in the process – with an equal percentage of candidates receiving a ‘yes’ recommendation in the over 60s as the under 20s. Without blind screening, and without the removal of age bias, the value and brilliance of the older candidates might otherwise have been easy to overlook or, at worse, wilfully disregarded or ignored.
While Sapia offers one of the easiest ways to provide a level playing field for all candidates, it’s one part of your overall process that should be reviewed to check for built-in age discrimination and other biases as well. Some other important considerations:
It offers a pathway to fairer hiring in 2021 so that you can get diversity and inclusion right while hiring on time and on budget.
In this Inclusivity e-Book, you’ll learn:
Download Inclusivity Hiring e-Book Here >
Find out how Sapia can help take age discrimination and other biases out of the equation in screening interviews.
Why neuroinclusion can’t be a retrofit and how Sapia.ai is building a better experience for every candidate.
In the past, if you were neurodivergent and applying for a job, you were often asked to disclose your diagnosis to get a basic accommodation – extra time on a test, maybe the option to skip a task. That disclosure often came with risk: of judgment, of stigma, or just being seen as different.
This wasn’t inclusion. It was bureaucracy. And it made neurodiverse candidates carry the burden of fitting in.
We’ve come a long way, but we’re not there yet.
Over the last two decades, hiring practices have slowly moved away from reactive accommodations toward proactive, human-centric design. Leading employers began experimenting with:
But even these advances have often been limited in scope, applied to special hiring programs or specific roles. Neurodiverse talent still encounters systems built for neurotypical profiles, with limited flexibility and a heavy dose of social performance pressure.
Hiring needs to look different.
Truly inclusive hiring doesn’t rely on diagnosis or disclosure. It doesn’t just give a select few special treatment. It’s about removing friction for everyone, especially those who’ve historically been excluded.
That’s why Sapia.ai was built with universal design principles from day one.
Here’s what that looks like in practice:
It’s not a workaround. It’s a rework.
We tend to assume that social or “casual” interview formats make people comfortable. But for many neurodiverse individuals, icebreakers, group exercises, and informal chats are the problem, not the solution.
When we asked 6,000 neurodiverse candidates about their experience using Sapia.ai’s chat-based interview, they told us:
“It felt very 1:1 and trustworthy… I had time to fully think about my answers.”
“It was less anxiety-inducing than video interviews.”
“I like that all applicants get initial interviews which ensures an unbiased and fair way to weigh-up candidates.”
Some AI systems claim to infer skills or fit from resumes or behavioural data. But if the training data is biased or the experience itself is exclusionary, you’re just replicating the same inequity with more speed and scale.
Inclusion means seeing people for who they are, not who they resemble in your data set.
At Sapia.ai, every interaction is transparent, explainable, and scientifically validated. We use structured, fair assessments that work for all brains, not just neurotypical ones.
Neurodiversity is rising in both awareness and representation. However, inclusion won’t scale unless the systems behind hiring change as well.
That’s why we built a platform that:
Sapia.ai is already powering inclusive, structured, and scalable hiring for global employers like BT Group, Costa Coffee and Concentrix. Want to see how your hiring process can be more inclusive for neurodivergent individuals? Let’s chat.
There’s growing interest in AI-driven tools that infer skills from CVs, LinkedIn profiles, and other passive data sources. These systems claim to map someone’s capability based on the words they use, the jobs they’ve held, and patterns derived from millions of similar profiles. In theory, it’s efficient. But when inference becomes the primary basis for hiring or promotion, we need to scrutinise what’s actually being measured and what’s not.
Let’s be clear: the technology isn’t the problem. Modern inference engines use advanced natural language processing, embeddings, and knowledge graphs. The science behind them is genuinely impressive. And when they’re used alongside richer sources of data, such as internal project contributions, validated assessments, or behavioural evidence, they can offer valuable insight for workforce planning and development.
But we need to separate the two ideas:
The risk lies in conflating the two.
CVs and LinkedIn profiles are riddled with bias, inconsistency, and omission. They’re self-authored, unverified, and often written strategically – for example, to enhance certain experiences or downplay others in response to a job ad.
And different groups represent themselves in different ways. Ahuja (2024) showed, for example, that male MBA graduates in India tend to self-promote more than their female peers. Something as simple as a longer LinkedIn ‘About’ section becomes a proxy for perceived competence.
Job titles are vague. Skill descriptions vary. Proficiency is rarely signposted. Even where systems draw on internal performance data, the quality is often questionable. Ratings tend to cluster (remember the year everyone got a ‘3’ at your org?) and can often reflect manager bias or company culture more than actual output.
The most advanced skill inference platforms use layered data: open web sources like job ads and bios, public databases like O*NET and ESCO, internal frameworks, even anonymised behavioural signals from platform users. This breadth gives a more complete picture, and the models powering it are undeniably sophisticated.
But sophistication doesn’t equal accuracy.
These systems rely heavily on proxies and correlations, rather than observed behaviour. They estimate presence, not proficiency. And when used in high-stakes decisions, that distinction matters.
In many inference systems, it’s hard to trace where a skill came from. Was it picked up from a keyword? Assumed from a job title? Correlated with others in similar roles? The logic is rarely visible, and that’s a problem, especially when decisions based on these inferences affect access to jobs, development, or promotion.
Inferred skills suggest someone might have a capability. But hiring isn’t about possibility. It’s about evidence of capability. Saying you’ve led a team isn’t the same as doing it well. Collecting or observing actual examples of behaviour allows you to evaluate someone’s true competence at a claimed skill.
Some platforms try to infer proficiency, too, but this is still inference, not measurement. No matter how smart the model, it’s still drawing conclusions from indirect data.
By contrast, validated assessments like structured interviews, simulations, and psychometric tools are designed to measure. They observe behaviour against defined criteria, use consistent scoring frameworks (like Behaviourally Anchored Rating Scales, or BARS), and provide a transparent, defensible basis for decision-making. In doing this, the level or proficiency of a skill can be placed on a properly calibrated scale.
But here’s the thing: we don’t have to choose one over the other.
The real opportunity lies in combining the rigour of measurement with the scalability of inference.
Start with measurement
Define the skills that matter. Use structured tools to capture behavioural evidence. Set a clear standard for what good looks like. For example, define Behaviourally Anchored Rating Scales (BARS) when assessing interviews for skills. Using a framework like Sapia.ai’s Competency Framework is critical for defining what you want to measure.
Layer in inference
Apply AI to scale scoring, add contextual nuance, and detect deeper patterns that human assessors might miss, especially when reviewing large volumes of data.
Anchor the whole system in transparency and validation
Ensure people understand how inferences are made by providing clear explanations. Continuously test for fairness. Keep human oversight in the loop, especially where the stakes are high. More information on ensuring AI systems are transparent can be found in this paper.
This hybrid model respects the strengths and limits of both approaches. It recognises that AI can’t replace human judgement, but it can enhance it. That inference can extend reach, but only measurement can give you higher confidence in the results.
Inference can support and guide, but only measurement can prove. And when people’s futures are on the line, proof should always win.
Ahuja, A. (2024). LinkedIn profile analysis reveals gender-based differences in self-presentation among Indian MBA graduates. Journal of Business and Psychology.
Hiring for care is unlike any other sector. Recruiters are looking for people who can bring empathy, resilience, and energy to the most demanding human roles. Whether it’s dental care, mental health, or aged care, new hires are charged with looking after others when they’re most vulnerable. The stakes are high.
Hiring for care is exactly where leveraging ethical AI can make the biggest impact.
The best carers don’t always have the best CVs.
That’s why our chat-based AI interview doesn’t screen for qualifications. It screens for the the skills that matter when caring for others. The traits that define a brilliant care worker, things like:
Empathy, Self-awareness, Accountability, Teamwork, and Energy.
The best way to uncover these traits is through structured behavioural science, delivered through an experience that allows candidates to open up. Giving candidates space to give real-life, open-text answers. With no time pressure or video stress. Then, our AI picks up the signals that matter, free from any demographic data or bias-inducing signals.
Candidates’ answers to our structured interview questions aren’t simply ticking boxes. They’re a window into how someone shows up under pressure. And they’re helping leading care organisations hire people who belong in care and those who stay.
Inclusivity should be a core foundation of any talent assessment, and it’s a fundamental requirement for hirers in the care industry.
When healthcare hirers use chat-based AI interviews, designed to be inclusive for all groups, candidates complete their interviews when and where they choose, without the bias traps of face-to-face or phone screening. There are no accents to judge, no assumptions, just their words and their story.
And it works:
Drop-offs are reduced, and engagement & employer brand advocacy go up. Building a brand that candidates want to work for includes providing a hiring experience that candidates want to complete.
Our smart chat already works for some of the most respected names in healthcare and community services. Here’s a sample of the outcomes that are possible by leveraging ethical AI, a validated scientific assessment, wrapped in an experience that candidates love:
The case study tells the full story of how Sapia.ai helped Anglicare, Abano Healthcare, and Berry Street transform their hiring processes by scaling up, reducing burnout, and hiring with heart.
Download it here: