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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. 


Blog

New Research Proves the Value of AI Hiring

A new study has just confirmed what many in HR have long suspected: traditional psychometric tests are no longer the gold standard for hiring.

Published in Frontiers in Psychology, the research compared AI-powered, chat-based interviews to traditional assessments, finding that structured, conversational AI interviews significantly reduce social desirability bias, deliver a better candidate experience, and offer a fairer path to talent discovery.

We’ve always believed hiring should be about understanding people and their potential, rather than reducing them to static scores. This latest research validates that approach, signalling to employers what modern, fair and inclusive hiring should look like.

The problem with traditional psychometric tests

While used for many decades in the absence of a more candidate-first approach, psychometric testing has some fatal flaws.

For starters, these tests rely heavily on self-reporting. Candidates are expected to assess their own traits. Could you truly and honestly rate how conscientious you are, how well you manage stress, or how likely you are to follow rules? Human beings are nuanced, and in high-stakes situations like job applications, most people are answering to impress, which can lead to less-than-honest self-evaluations.

This is known as social desirability bias: a tendency to respond in ways that are perceived as more favourable or acceptable, even if they don’t reflect reality. In other words, traditional assessments often capture a version of the candidate that’s curated for the test, not the person who will show up to work.

Worse still, these assessments can feel cold, transactional, even intimidating. They do little to surface communication skills, adaptability, or real-world problem solving, the things that make someone great at a job. And for many candidates, especially those from underrepresented backgrounds, the format itself can feel exclusionary.

The Rise of Chat-Based Interviews

Enter conversational AI.

Organisations have been using chat-based interviews to assess talent since before 2018, and they offer a distinctly different approach. 

Rather than asking candidates to rate themselves on abstract traits, they invite them into a structured, open-ended conversation. This creates space for candidates to share stories, explain their thinking, and demonstrate how they communicate and solve problems.

The format reduces stress and pressure because it feels more like messaging than testing. Candidates can be more authentic, and their responses have been proven to reveal personality traits, values, and competencies in a context that mirrors honest workplace communication.

Importantly, every candidate receives the same questions, evaluated against the same objective, explainable frameworkThese interviews are structured by design, evaluated by AI models like Sapia.ai’s InterviewBERT, and built on deep language analysis. That means better data, richer insights, and a process that works at scale without compromising fairness.

Key Findings from the Latest Research

The new study, published in Frontiers in Psychology, put AI-powered, chat-based interviews head-to-head with traditional psychometric assessments, and the results were striking.

One of the most significant takeaways was that candidates are less likely to “fake good” in chat interviews. The study found that AI-led conversations reduce social desirability bias, giving a more honest, unfiltered view of how people think and express themselves. That’s because, unlike multiple-choice questionnaires, chat-based assessments don’t offer obvious “right” answers – it’s on the candidate to express themselves authentically and not guess teh answer they think they would be rewarded for.

The research also confirmed what our candidate feedback has shown for years: people actually enjoy this kind of assessment. Participants rated the chat interviews as more engaging, less stressful, and more respectful of their individuality. In a hiring landscape where candidate experience is make-or-break, this matters.

And while traditional psychometric tests still show higher predictive validity in isolated lab conditions, the researchers were clear: real-world hiring decisions can’t be reduced to prediction alone. Fairness, transparency, and experience matter just as much, often more, when building trust and attracting top talent.

Sapia.ai was spotlighted in the study as a leader in this space, with our InterviewBERT model recognised for its ability to interpret candidate responses in a way that’s explainable, responsible, and grounded in science.

Why Trust and Candidate Agency Win

Today, hiring has to be about earning trust and empowering candidates to show up as their full selves, and having a voice in the process.

Traditional assessments often strip candidates of agency. They’re asked to conform, perform, and second-guess what the “right” answer might be. Chat-based interviews flip that dynamic. By inviting candidates into an open conversation, they offer something rare in hiring: autonomy. Candidates can tell their story, explain their thinking, and share how they approach real-world challenges, all in their own words.

This signals respect from the employer. It says: We trust you to show us who you are.

Hiring should be a two-way street – a long-held belief we’ve had, now backed by peer-reviewed science. The new research confirms that AI-led interviews can reduce bias, enhance fairness, and give candidates control over how they’re seen and evaluated.

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AI Maturity in the Enterprise

Barb Hyman, CEO & Founder, Sapia.ai

 

It’s time for a new way to map progress in AI adoption, and pilots are not it. 

Over the past year, I’ve been lucky enough to see inside dozens of enterprise AI programs. As a CEO, founder, and recently, judge in the inaugural Australian Financial Review AI Awards.

And here’s what struck me:

Despite the hype, we still don’t have a shared language for AI maturity in business.

Some companies are racing ahead. Others are still building slide decks. But the real issue is that even the orgs that are “doing AI” often don’t know what good looks like.

You don’t need more pilots. You need a maturity model.

The most successful AI adoption strategy does not have you buying the hottest Gen AI tool or spinning up a chatbot to solve one use case. What it should do is build organisational capability in AI ethics, AI governance, data, design, and most of all, leadership.

It’s time we introduced a real AI Maturity Model. Not a checklist. A considered progression model. Something that recognises where your organisation is today and what needs to evolve next, safely, responsibly, and strategically.

Here’s an early sketch based on what I’ve seen:

The 5 Stages of AI Maturity (for real enterprises)
  1. Curious
    • Awareness is growing across leadership
    • Experimentation led by innovation teams
    • Risk is unclear, appetite is cautious
    • AI is seen as “tech”
  2. Reactive
    • Gen AI introduced via vendors or tools (e.g., copilots, agents)
    • Some pilots show promise, but with limited scale or guardrails
    • Data privacy and sovereignty questions begin to surface
    • Risk is siloed in legal/IT
  3. Capable
    • Clear policies on privacy, bias, and governance
    • Dedicated AI leads or councils exist
    • Internal use cases scale (e.g., summarisation, scoring, chat)
    • LLMs integrated with guardrails, safety reviewed
  4. Strategic
    • AI embedded in workflows, not layered on
    • LLM/data infrastructure is regionally compliant
    • AI outcomes measured (accuracy, equity, productivity)
    • Teams restructured around AI capability — not just tech enablement
  5. AI-Native
    • AI informs and transforms core decisions (hiring, pricing, customer service)
    • Enterprise builds proprietary intelligence
    • FAIR™/RAI principles deeply operationalised
    • Talent, systems, and leadership are aligned around an intelligent operating model
Why this matters for enterprise leaders

AI is a capability.And like any capability, it needs time, structure, investment, and a map.

If you’re an HR leader, CIO, or enterprise buyer, and you’re trying to separate the real from the theatre, maturity thinking is your edge.

Let’s stop asking, “Who’s using AI?”
And start asking: “How mature is our AI practice and what’s the next step?”

I’m working on a more complete model now, based on what I’ve seen in Australia, the UK, and across our customer base. If you’re thinking about this too, I’d love to hear from you.

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Beyond the Black Box: Why Transparency in AI Hiring Matters More Than Ever

For too long, AI in hiring has been a black box. It promises speed, fairness, and efficiency, but rarely shows its work.

That era is ending.

“AI hiring should never feel like a mystery. Transparency builds trust, and trust drives adoption.”

At Sapia.ai, we’ve always worked to provide transparency to our customers. Whether with explainable scores, understandable AI models, or by sharing ROI data regularly, it’s a founding principle on which we build all of our products.

Now, with Discover Insights, transparency is embedded into our user experience. And it’s giving TA leaders the clarity to lead with confidence.

Transparency Is the New Talent Advantage

Candidates expect fairness. Executives demand ROI. Boards want compliance. Transparency delivers all three.

Even visionary Talent Leaders can find it difficult to move beyond managing processes to driving strategy without the right data. Discover Insights changes that.

“When talent leaders can see what’s working (and why) they can stop defending their strategy and start owning it.”

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Metrics That Make Transparency Real (and Actionable)

 

🕒 Time to Hire

 

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What it is: The median time between application and hire.

Why it matters: This is your speedometer. A sharp view of how long hiring takes and how that varies by cohort, role, or team helps you identify delays and prove efficiency gains to leadership.

Faster time to hire = faster access to revenue-driving talent.

 

💬 Candidate Sentiment, Advocacy & Verbatim Feedback

 

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What it is: Satisfaction scores, brand advocacy measures, and unfiltered candidate comments.

Why it matters: Many platforms track satisfaction. Sapia.ai’s Discover Insights takes it further, measuring whether that satisfaction translates into employer and consumer brand advocacy.

And with verbatim feedback collected at scale, talent leaders don’t have to guess how candidates feel. They can read it, learn from it, and take action.

You don’t just measure experience. You understand it in the candidates’ own words.

 

🔍 Drop-Off Rates, Funnel Visibility & Automation That Works

 

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What it is: The percentage of candidates who exit the hiring process at different stages, and how to spot why.

Why it matters: Understanding drop-off points lets teams fix friction quickly. Embedding automation early in the funnel reduces recruiter workload and elevates top candidates, getting them talking to your hiring teams faster.

Assessment completion benchmarks in volume hiring range between 60–80%, but with a mobile-first, chat-based format like Sapia.ai’s, clients often exceed that.

Optimising your funnel isn’t about doing more. It’s about doing smarter, with less effort and better outcomes.

 

📈 Hiring Yield (Hired / Applied)

 

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What it is: The percentage of completed applications that result in a hire.

Why it matters: This is your funnel efficiency score. A high yield means your sourcing, screening, and selection are aligned. A low one? There’s leakage, misfit, or missed opportunity.

Hiring yield signals funnel health, recruiter performance, and candidate-process fit.

 

🧠 AI Effectiveness: Score Distribution & Answer Originality

 

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What it is: Insights into how candidate scores are distributed, and whether responses appear copied or AI-generated.

Why it matters: In high-volume hiring, a normal distribution of scores suggests your assessment is calibrated fairly. If it’s skewed too far left or right, it could be too hard or too easy, and that affects trust.

Add in answer originality, and you can track engagement integrity, protecting both your process and your brand.

From Metrics to Momentum

To effectively lead, you need more than simply tracking; you need insights enabling action.

When you can see how AI impacts every part of your hiring, from recruiter productivity to candidate sentiment to untapped talent, you lead with insight, not assumption. And that’s how TA earns a seat at the strategy table.

Learn more about Discover Insights here

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