Written by Nathan Hewitt
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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The new kind of battle in the war for talent
Before COVID, the conversations I was having with HR executives were about how Sapia might help them with the volume of candidates they were receiving for job openings. For every job posted there were often over a thousand candidates, and it doesn’t take much of a stretch of the imagination to understand how overwhelmed many big organisations were. Our Ai was seen as the solution to automate dealing with candidate volume in a way that found the best people, but also touched base with everyone who applied as part of their brand building. In a nutshell, before the pandemic, efficiency was the key driver in looking for automated hiring solutions like ours.
Now that we’re emerging from the disruption of COVID, no one is talking to me about needing help with the volume of candidates they receive. In fact, they are asking how we might help them get any candidates in the first place! All around the globe, and across multiple industries, there is a need for candidates. It’s certainly been an abrupt change that has left many scratching their heads, but there is almost no time to wrap your head around it if you want to stay in the game. This is a new war for talent unlike any we’ve seen before, and candidates have the upper hand. It’s created a need for a solution to solve two things: firstly, to identify skills in candidates that traditional ways of hiring failed to identify (I call this cohort “undiscovered talent”) and a strong candidate experience (you are the one being interviewed from the moment they hit “apply”).
I thought it was worth looking at how the “war of talent” has evolved since it was first coined by Steven Hankin at McKinsey & Company in 1997. At that time there was a shift in the way that companies valued their talent, and it became seen as important to attract the best in order to have a successful organisation. It’s hard to think about this now, but at that time the whole idea of cultivating company cultures that aimed to elevate and value employees was new. At this stage though the “war” was largely for executive talent with recruiters focusing on building their brand by poaching star C-Suite talent off competitors, wooing them with big sign-up bonuses and lavish overtures like unexpected gifts and trips.
As tech companies started to become the big players in the market, the focus turned from business acumen to the need for the best digital and technical talent. Recruiting became less about material perks (though many engineers still commanded high salaries) but also about giving talent things they wanted besides just money. Flexibility, free lunches, unlimited holidays and creating cultures that were about “working hard and having fun” were how the war for technical talent was won. This was really a time of culture wars between companies, but also meant that many companies hired only for culture-fit. This resulted in fairly homogenous teams that were largely white male techbros, and eventually many large tech companies were called out on it. Beyond tech, corporates were also waking up to the fact that they had some serious diversity issues that needed to be addressed. This led to a new war. The war for diverse talent.
Pre-COVID, hiring more diversely was a strong focus for companies to find the best talent. We all know that diverse teams result in better business outcomes and anyone who had a “pale, male and stale” executive team was seen as minted in the past. Coupled with Black Lives Matter, which became a global movement to address racial inequality from the C-suite down, finding more diverse talent through reducing bias in hiring, was where the war was being fought. This is not a won battle by the way, and remains a large focus for many companies that we work with and help. Importantly, finding diverse talent is still a key part of this new and emerging next phase of the “war on talent” … the one where workers have the upper hand. The one where candidates are in short supply, and people want jobs that suit them just as much as whether they are seen as just suited to the job.
Recruiters have been forced to look at people differently – and this is not a bad thing. Factors like age, ethnicity, education, gender and even past experience that obscured our understanding of someone’s ability to do a job have all been cancelled as qualifying factors. Soft skills, or human skills, have become the focus on what we need to understand in order to assess someone’s suitability to do a job. Are they a team player? Do they like to problem solve? How aligned are they to our company values? Are they self-aware and in touch with their emotions? Can they put stress aside to achieve outcomes?
“What we recruit for” has significantly shifted for many already, but there is still some catching up to do on the “how we recruit”. To be blunt, CV’s and cover letters begging recruiters to “pick me!” serve no purpose in this new battle. They ask too much of candidates from the outset, serve no valuable purpose in the information they provide, confirm our biases and just create work on the HR manager’s side.
We need to walk in a candidate’s shoes and make sure that our recruiting process puts them first, treats them fairly and without bias, meets them where they are at, and is both friendly and informative. And, HR teams need to do this all while working efficiently and fast. Speed is crucial when talent is in short supply.
Impossible? No, not at all. Recruiters need to understand that Ai platforms like ours exist to solve all these problems. We’re not a “technical” solution, but a human one, in that we can accurately identify soft skills immediately and engage with candidates in a one-on-one way, at scale.
You cannot win this war on talent without chat-driven Ai technology. Technology like ours is the only way you can quickly understand the real human skills that every candidate brings to the table, without dismissing anyone upfront.
I can’t help but think that these issues we’re facing as recruiters and HR managers right now, where workers have the upper hand, while unchartered territory, will only serve our industry for the better. It’s a chance to give everyone a fair go, truly understand them, treat them with the dignity they deserve … and still hire better teams.
Maybe it’s not a battle after all. Maybe it’s a win-win.
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For more on how to improve candidate experience using recruitment automation, we have a great eBook on candidate experience.
Blog
The Machine picked the wrong guy
AI used to inform decisions about people
I work with a team building a product-driven by AI which is used to inform decisions about people. This means I am often approached on social media or in-person by people who have a point of view about that, often with fear or frustration about being picked (or rejected) by a machine.
This week I received an email from a commerce/law graduate who had recently applied for a role at one of the big ‘accounting’ professional services firms. This student, let’s call him Dan, had to complete an online game in order to qualify for the next step which was a video interview.
To give himself the maximum chance of ‘doing well’ in the game, Dan created a dummy profile ‘Jason’ to see what the experience was like and get an inside read of the questions so that when he did it for real he would really nail it. This first time round he fudged the test as it was a trial run and he left most answers blank. When Dan went and did this for real, he was conscientious of course and wrote thoughtful answers and tried to pick the right behaviour in the balloon popping game!
Jason, who scored 44% received a video interview. Jason does not exist.
Dan, who scored 75% did not progress to the next round.
The machine picked the wrong guy
Let me be upfront and say that we too use machines to help identify the best-fit applicants for roles.
Every business like ours that works in this space recognises that this is new technology, and so still very much in the early stages of development. Like humans, machines will make mistakes. In our business, we call them false positives (people recommended who just aren’t right) or false negatives (people who are missed by the machine who could be right for the role).
Dan’s questions are legitimate…
- -how can Jason who scored lower get through the process?
- -how can Jason who fudged it and left most of the questions blank, get through?
- -how does the number of balloons you popped, or let pop, dictate whether you are going to be a good hire?
When you are rejected by humans, either you hear nothing or you may get an explanation like — ‘you aren’t a good culture fit’ when they reject you. Machines may give you a score.
For me what this reveals is that any business who uses AI and ML for candidate selection, it’s critical to have empathy for the person who is experiencing this, in this case, empathy for the candidate experience.
- What does it feel like to be judged based on how well you play an online game? Is that a human experience? How gameable is it? Is it fair to all, given that some people have grown up playing video games and others haven’t?
- How do you give feedback in a way that’s human and helpful and ideally developmental? The feedback given here was to say you didn’t make the % threshold.
An individual’s traits, strengths and weaknesses can be predicted.
Machines can make better selection decisions about people because they have access to a larger more comprehensive set of data, can process data faster, and if built with the right objective data, they can be far less bias than humans.
When used in recruitment, they need to work for both parties — the organisation and the candidate. Building trust in these technologies is critical in our space. It can’t all be about the organisation getting their efficiency gains.
This means :
- Making these experiences reflective of human experiences — it should feel like an interview not like a game.
- Giving the candidate something back, by way of qualitative feedback rather than making you feel like a number or a percentage on a distribution chart.
Recruitment wants to rise above being a process. So AI in recruitment should enable that if it’s to be trusted by candidates.
Suggested Reading:
https://sapia.ai/blog/culture-surveys-no-use/
Blog
How the language of texting can reveal how long we might stay in a role
Language has long been seen as a source of truth for personality – it defines who we are.
It should not be surprising then that language is also the basis of most traditional forms of personality testing.
This lexical hypothesis is a thesis, current primarily in early personality psychology. Subsequently subsumed by many later efforts in that subfield. Despite some variation in its definition and application, the hypothesis is generally defined by two postulates.
- The first states that those personality characteristics that are important to a group of people will eventually become a part of that group’s language.
- Second follows, stating that more important personality characteristics are more likely to be encoded into language as a single word.
Lexical hypothesis is a major foundation of the Big Five personality traits. The HEXACO model of personality structure and the 16PF Questionnaire and has been used to study the structure of personality traits in a number of cultural and linguistic settings.
Noam Chomsky summed up the power of language nicely:
“Language is a mirror of mind in a deep and significant sense. It is a product of human intelligence … By studying the properties of natural languages, their structure, organization, and use, we may hope to learn something about human nature; something significant …”
Language is the basis of conversational Ai and it’s very different (and way smarter) than a simple chatbot.
Where chatbots can be programmed to provide answers to basic questions real-time, so that your people don’t need to do that, these answers are canned answers to basic questions delivered through text. They lack the smarts to truly discover what your text responses say about you. The engagement between the chatbot and the individual is purely transactional.
Conversational AI is more about a relationship built through understanding, using natural language to make human-to-machine conversations more like human-to-human ones. It offers a more sophisticated and more personalized solution to engage candidates through multiple forms of communication. Ultimately, this kind of artificial intelligence gets smarter through use and connects people in a more meaningful way.
Put simple, Conversational Ai is intelligent and hyper-personalised Ai, and in the case of ‘Sapia labs’, its is underpinned by provable and explainable science. We have already published our peer-reviewed scientific research which underpins our personality science.
We published our second piece of research to explain how our Conversational Ai can predict someone’s likelihood to stay in a role.
The scientific paper may not make it to your reading table, although you can download it here (“Predicting job-hopping likelihood using answers to open-ended interview questions” ) but the business implications cannot be ignored.
According to one report, voluntary turnover is estimated to cost U.S. companies more than $600 billion a year. This is due to one in four employees projected to quit and to take a different job. If your turnover is even a few basis points above your industry average, then leveraging conversational Ai will save your business costs.
Our research used the free-text responses from 45,899 candidates who had used Sapia’s conversational Ai. Candidates had originally been asked five to seven open-ended questions on past experience and situations. They also responded to self-rating questions based on the job-hopping motive scale, a validated set of rating questions to measure one’s job-hopping motive. The self-rating questions were based on the job-hopping motive scale, a validated set of rating questions to measure one’s job-hopping motive.
We found a statistically significant positive correlation between text based answers and self-rated job-hopping motive scale measure. The language inferred job-hopping likelihood score had correlations with other attributes such as the personality trait “openness to experience”.
This is the power of true predictive Ai.
Ai, that is the bridge between HR and the business. It is this kind of quantifiable business ROI that distinguishes traditional testing with Ai models.
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