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Deterring age discrimination. Count those mature hires ‘in’!

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.

And perhaps the greatest challenge to that need is age discrimination in hiring.

Ageism – a hiring challenge for our age

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.

And when they say ‘older’ they’re referring to candidates aged over the age of 50.

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.

Learned versus lived

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?

What is age discrimination in hiring?

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:

  • who gets shortlisted for interviews
  • who gets selected for a role
  • what benefits, terms and conditions are offered with that role
  • who is offered training opportunities 
  • who is considered and chosen for promotion, transfer, retrenchment or dismissal.

There are four main types of age discrimination

1. Direct discrimination in hiring

Direct discrimination is when someone is treated differently or less favourably than another person in the same situation because of their age.

For example:

  • Someone reviewing CVs refuses to even consider any candidate over 45 years of age.
  • A hirer believes older workers are slower and resistant to or incapable of adapting to new technologies.
  • Someone is marked for redundancy because they are the oldest – or youngest – employee.
  • An employer decides an employee is too old to undertake skills training while other, younger employees complete the training.

2. Indirect discrimination in hiring

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:

  • An employer has a policy that only people with postgraduate qualifications can be promoted. This could be seen to disadvantage young people who simply haven’t had the time to achieve post-grad qualifications. Or an older worker who didn’t go to university because ‘in those days’  it wasn’t commonplace to do so. 
  • A company requires all employees to meet a physical fitness test, even though that fitness standard is not relevant to the job. While the test might be easy for young people, it could be seen to disadvantage older employees.
  • An employer assumes that older people won’t fit in with the team due to their age

3. Harassment

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:

  • An older employee having difficulty learning a new online time management system becomes the subject of ongoing ridicule in staff meetings. This could be held up as age discrimination.
  • An older worker is nicknamed Granny Joan.

4. Victimisation

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.

What the law says about age discrimination

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.

Age discrimination in the workplace can be damaging and costly on so many levels. Here’s what employers need to know and do

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:

  • Know the law and stick to it – Just as there are laws that cover discrimination around sex, race or disability, the Age Discrimination Act (the ADA) says that an employer must take ‘all reasonable steps’ to prevent discrimination from happening at work or in connection with a person’s employment. This is called ‘vicarious liability’. 
  • Develop an anti-age discrimination policy – While any organisation’s employment policy will be shaped by the relevant employment and discrimination laws, it’s essential that the ‘laws of the land’ are enshrined in your own policies and practices. Written policies make it clear for all stakeholders that discrimination and harassment– age-based or otherwise – will not be tolerated in your workplace. These policies should be made familiar to all employees, contractors, recruiters and partners. They may also be part of your employer brand and be explicitly stated in your recruitment advertising.
  • Cultivate diversity – The benefits of diversity in the workplace are well recognised in contemporary business. Having a workforce comprised of employees of different gender, cultural and ethnic backgrounds, experience and education have been shown to positively impact a wide range of business metrics from productivity to sales, innovation to employee satisfaction and tenure. Often overlooked in the assessment of diversity is the value that having employees of every age bring to the organisation.
  • Challenge and change attitudes – Like all forms of discrimination, ageism is often driven by inaccurate stereotyping, misperceptions, myths and unconscious bias. A number of studies have shown that developing intergenerational teams explodes preconceptions and the beliefs around ageing or the abilities of the young. The more younger and older people work together the more their perceptions of each other are moderated and negative attitudes are softened.

Making recruitment practices and process fair for all

Perhaps the most important place to tackle age discrimination head-on is where it potentially begins and ends – in the recruitment process.

Remove age discrimination from candidate screening 

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.

Sapia offers blind screening at its best

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.

Here’s an example of how Ai is a fairer judge, regardless of age

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.

 

Check your bias, Check your process

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:

  • review selection criteria – ensure your documented criteria for a role are consistent with the ‘essentials’ of the role, the qualifications and skills actually required, not based on stereotypes or arbitrary traits. Check you’re not making assumptions that it’s a young person’s role.
  • review job listings –  at a minimum, you need to be sure that job descriptions are compliant with employment and discrimination law. Advertising for a “25-30-year-old woman”, for instance, is discriminatory. Twice.
  • add diversity to your candidate sourcing – make a virtue of your inclusive and diverse hiring policies by explicitly mentioning them in your job ads. Consider where your recruitment ads are being seen. There may be better places to connect with candidates that will help support your organisation’s diversity goals.
  • check your hiring processes – review application forms, screening factors,  ATS filters, onboarding and workplace culture, to see that age discrimination (amongst others) isn’t unintentionally embedded in your processes and your collective workplace thinking.

Have you seen the Inclusive e-Book?

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: 

  • How to design an inclusive recruitment path. From discovery to offer and validation of the process.
  • The hidden inclusion challenges that are holding your organisation back.
  • How to tell if Ai technology is ethical.

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. 


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Neuroinclusion by design. Not by exception.

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.

Shifting from retrofits to inclusive-by-design

Over the last two decades, hiring practices have slowly moved away from reactive accommodations toward proactive, human-centric design. Leading employers began experimenting with:

  • Sharing interview questions in advance

  • Replacing group exercises with structured simulations

  • Offering a variety of assessment formats

  • Co-designing assessments with neurodiverse candidates

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.

Insight 1: The next frontier of hiring equity is universal design

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:

  • No time limits — Candidates answer at their own pace
  • No pressure to perform — It’s a conversation, not a spotlight
  • No video, no group tasks — Just structured, 1:1 chat-based interviews
  • Built-in coaching — Everyone gets personalised feedback

It’s not a workaround. It’s a rework.

Insight 2: Not all “friendly” methods are inclusive

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.”

Insight 3: Prediction ≠ Inclusion

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.

Where to from here?

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:

  • Doesn’t rely on disclosure

  • Removes ambiguity and pressure

  • Creates space for everyone to shine

  • Measures what matters, fairly

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. 

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Skills Measurement vs Skills Inference – What’s the Difference and Why Does It Matter?

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:

  • Skills Measurement: Directly observing or quantifying a skill based on evidence of actual performance. 
  • Skills Inference: Estimating the likelihood that someone has a skill, based on indirect signals or patterns in their data. 

The risk lies in conflating the two.

The Problem Isn’t Inference of Skills. It’s the Data Feeding It

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.

Sophisticated ≠ Objective

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.

Transparency (or Lack Thereof)

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.

Presence ≠ Proficiency

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.

A Smarter Way Forward: The Hybrid Model

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.

The Bottom Line

Inference can support and guide, but only measurement can prove. And when people’s futures are on the line, proof should always win.

References

Ahuja, A. (2024). LinkedIn profile analysis reveals gender-based differences in self-presentation among Indian MBA graduates. Journal of Business and Psychology.

 

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Making Healthcare Hiring Human with Ethical AI

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.

Hiring for the traits that matter

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.

Inclusion, built in

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:

  • 91.8% of carer candidates complete their interviews
  • Carer candidates report 9/10 Candidate Satisfaction with their interview experience 
  • 80% of candidates would recommend others to apply 
  • Every candidate receives personalised feedback, regardless of the outcome

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. 

Real outcomes in care hiring

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: 

Anglicare – a leading provider of aged care services
  • Time-to-offer dropped from 40+ days to just 14
  • Candidate pool grew by 30%
  • Turnover dropped by 63%
Abano Healthcare – Australasia’s largest dental support organisation
  • 1,213 recruiter hours saved  in the first month (67 hours per individual hiring team member) 
  • $25,000 saved in screening and interviewing time
Berry Street – a not for profit family & child services organisation
  • Time-to-hire down from 22 to 7 days
  • 95.4% of candidates completed their chat interviews

A smarter way to hire

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:

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