There has been some negative media attention lately surrounding the use of Artificial Intelligence (AI) in the recruitment space with warnings ranging from the fact that AI produces a shallow candidate pool to more serious things like the amplification of bias.
There are many instances of AI being used in a way that has harmful outcomes, but it is important to clarify that this is about how AI is being implemented and not an issue with the use of AI itself.
When AI is used appropriately, responsibly, and following regulatory guidelines it is an incredibly powerful tool that can create fair outcomes for candidates who are selected without bias – in a way that no other tool at our disposal can.
This is why we think it’s worthwhile that more people better understand AI and some of the differences in the way it is used and implemented.
Most media articles refer to AI as if it represents a singular master algorithm and fail to identify how varied its implementations are. Almost all AI we have today falls into the category of “narrow AI”, in other words algorithms, mostly machine learning, built to solve a specific problem. E.g. classify sentiment, detect spam, label images, parse resumes. These purpose-built AI are highly dependent on the nature of the underlying training data and the expertise of the developers in making the right assumptions and tests of validity of their models. When built in the right way and used responsibly, AI has the ability to empower humans. This is why at Sapia.ai we have made various conscious design choices and adhered to a framework called FAIR™ that tests for bias, validity, explainability, and inclusivity of our AI-based tools.
The biggest cause for alarm is when AI is applied to analyzing video, which can lead to irrelevant inputs like clothing, background, and lighting being used as predictors of personality and job fit. Video and speech patterns also make it nearly impossible to remove demographic information like race and gender as inputs.
Additionally, analyzing facial expressions is problematic, especially when evaluating certain candidates like those with Autism Spectrum Disorder or other forms of neurodiversity.
This is why Sapia.ai does not, and will not, use AI scoring for video interviews or even voice transcriptions from videos or audio given the word error rate introduced in transcribing speech. Instead, we opt for text – which we implement in a friendly no pressure environment that feels like you are texting a friend.
It’s worth noting that no data other than the answers given by the candidate are used in the ‘fit score’ calculation – that is, we never use demographic data, social media, CV or resume data (which also contain demographic signals, even when de-identified), or behavioral metrics such as time to complete.
Even a candidate’s raw text itself contains gender and ethnicity signals that can introduce bias, if not mitigated. This is why we only use feature scores (e.g., personality, behavioral competencies, and communication skills) derived according to a clearly defined rubric in our scoring algorithms, which our extensive research shows contain significantly less gender and ethnicity information than raw text.
Another common concern is that AI will result in more uniformity rather than diversity in the workforce as algorithms narrow the pool in order to search out an employer’s ideal candidate. There are several things worth noting here.
First, identifying what the ideal candidate is – that is, what knowledge, skills, abilities, and other characteristics are important for success in the role – is what a job analysis is for and should, legally, be what your selection tool is designed to measure.
This is also not specific to AI, as all selection systems are designed to identify which candidates have a profile of traits and characteristics that indicate they will likely be successful in the role. This doesn’t automatically mean that every hire is going to be exactly the same, though. When you focus on the traits and characteristics that will set someone up to be successful, considering potential more than background or pedigree, you’re more likely to uncover hidden talent and hire more successful people from a broader, more diverse range.
Relying solely on past data to build your model also runs the risk of introducing historical data biases. This is actually why it is so important to consider the ideal candidate profile and use that to inform your scoring model. We strongly believe in keeping the human in the loop, which is why our scoring models are centred around the human-determined (via job analysis) ideal candidate profile and then optimized to ensure all bias constraints (e.g., 4/5ths rule and effect sizes) are met.
Using this approach, Sapia has helped clients achieve their DEI goals and increase their diversity hires, including impressive statistics like hiring 3x more ethnic minorities, 1.5x more women, and 2x more LGBTQ+ candidates in just 3 months.
Lastly, it’s worth acknowledging that there is often a “black box” mystery of how AI recruitment tools work. People don’t trust what they don’t understand. While we don’t expect everyone to be an expert in AI or Natural Language Processing, we do strongly believe in building trust through transparency and work hard to make sure that our models are easily understood and open to scrutiny. From third-party audits to detailed model cards to in-depth dashboarding and reporting, we aim to maximize transparency, explainability, and fairness.
We believe a fairer future can only be achieved when AI is used responsibly. AI is not the enemy, rather it’s the experience and motivation behind those promoting it that can make the difference between what is good AI and what is harmful AI.
Competition for candidates today is fierce. COVID, border closures, BREXIT, the last two years have created a global candidate shortage that’s hitting large organisations hard.
That’s why in today’s market, candidate experience is king. The consistent theme in all of my conversations with CHROs globally is how to improve candidate experience and get an edge on the competition.
However, the bottom line is ever present, especially in industries that are now in recovery mode. How can recruitment teams and hiring managers be expected to deliver a world-class, personalised and interactive candidate experience when they’re already stretched too thin?
The answer lies in human-centred technology with an experience that makes candidates feel valued and heard, while automating the components of the process that suck time out of your team’s day and extend the time to offer, losing candidates in the process.
Why Australia’s largest private employer turned to automation
With close to 1 million candidates annually, and a video interview experience that was sub-par for candidates and frustrating for hiring managers, Woolworths needed to drastically re-imagine their recruitment experience, making it more efficient and engaging.
How Sapia re-invigorated and streamlined Woolworth’s recruitment process
With a completely automated interview process, every retail candidate is interviewed by Smart Interviewer with Sapia’s Chat Interview chat. The automatically shortlisted candidates progress directly to VideoInterview – a chat based video interview that is reviewed by hiring managers who can then move straight to offer. It’s a seamless process that’s designed to be fair and human-centred.
The results are simply fantastic
Candidate satisfaction has blown the team away – 9.2/10 for FirstInterview and an unprecedented 9.0/10 for VideoInterview. Yes, you read that right – 9/10 for a video interview, from almost 9,000 candidates.
Completion rates for the video interview are above 75%, showing that candidates are happy to engage with a video interview that’s mobile-friendly, interactive and frankly, just works. Almost 50% of candidates complete both interviews on their mobile, making it easy for candidates to interview literally anytime, anywhere.
Here’s what Woolworths candidates had to say about their VideoInterview experience:
“The chat makes you feel like you’re in a safe space – it gives everyone an equal opportunity instead of in person interview as people can get extremely nervous”
“I found the process to be reflective and I liked how they wanted to know about me”
“everything was amazing! by far the best interview system i’ve encountered! it allowed me be comfortable and be myself, it really allowed me to take my time with my responses rather than stutter over my words”
“It was great. I like the potential to retake videos and how quick you’ve responded. ”
“I felt really calm during this interview. Which I definitely would not be in physical interviews. I was able to really sort out my thoughts and express myself to the fullest. I really love this format of interviewing !”
Automating the end to end experience has given time back to extremely time-poor hiring managers, who no longer need to manage shortlisting or scheduling and can simply review the video responses of the top candidates as they come in. Smart Interviewer has video interviewed almost 9,000 candidates,
In some cases, candidates have moved from ad to offer in 24 hours – giving Woolworths an edge as they can move quickly to capture candidates who otherwise might have accepted offers elsewhere.
If you’d like to have a candidate experience as good as Woolworths, get in touch here for a product demo.
The first is that you cannot just think about bringing AI as a tool. It’s not that simple, even though ChatGPT makes it seem so. You can’t just go out there and buy something with AI because the board is pressuring you to start doing something with AI.You actually have to really understand the business that you’re supporting and what their biggest problem or their three biggest problems are.
Most likely, the solution that you bring to bear is going to have an AI component because AI is such a powerful accelerant to deliver productivity to recruiters, your HR people, and to the business. But you’ve got to figure out what that business problem is first.
Do you have an issue with churn? Values? Hire quality? General speed to hire? Candidate experience? The first step is setting out exactly what you need from an AI system, and going to market with that objective in mind.
Take candidate experience: According to this 2022 report by Aptitude Research, 63% of companies aren’t planning on doing anything about candidate experience in 2023.
That’s a great place to start.
The second thing is that you’ve got to be really honest and hold the mirror up to you, your team, and the organization on whether or not you’re ready to embrace technology that has AI in it.
There is a lot of investigation and due diligence required for any company bringing in AI. And there are elements around privacy, as well as US regulations that you need to comply with.
You need to have a level of maturity and sophistication and awareness about the effort that you’re going to have to put the organization through to really embrace and adopt it.
That very much starts with your team and your people: are they ready to embrace the opportunity of AI?
Right now, I find that many companies and recruiters are very fearful of it. They’re worried about what it will do to their jobs.
They don’t think of it as a co-pilot; they think of it as a replacement.
So, I would encourage you to start having conversations with your team as early as possible about the opportunity that AI could bring before you ram an AI solution down their throat, which is likely to face some resistance.
It’s no wonder recruiters and organizations approach AI with fear. The media has been quick to paint it as a Doomsday creator, destined to put 300 million people out of work.
Just as computers did not make millions of people redundant, nor will smart AI systems. They’re force multipliers. The stats prove this.
According to this article, recruiters have embraced partial AI innovation with great results. 94% said it has improved their hiring processes. 44% said it helps recruiters save time.
42% think that it will help recruiters be more strategic. This stat is particularly interesting to us, because we care first and foremost about giving recruiters time back to focus on deep people strategy.
There’s nothing to fear if you can see that your chosen AI solution is improving hiring outcomes, while also giving you time back to focus on strategy.
Smart Chat Interviewing technology uses structured interviews to deliver fair and comparable insights on all candidates: Soft skills, hard skills, and cognitive ability. According to time-honored research by Frank Schmidt and John Hunter, structured interviews are the best explainer of performance on the job (26%).
Structured interviews are hard for humans to do – especially across decentralized networks – but the complete objectivity of the right AI makes it perfect for this job.
The proof? Sapia.ai has helped its customers achieve a 25% decrease in employee turnover through better interviewing. It does this because it can accurately match people to the roles for which they’re best suited.
Take Woolworths Supermarkets, Australia’s largest private employer. Using Sapia.ai’s Smart Chat Interviewer, Woolworths interviews 1 million people per year and hires 50,000 people per year.
Hiring managers are happy, and the in-house Talent Acquisition team has more purpose than ever before. No one has lost their job due to AI. Candidates are happier, staff are happier.
A recent CNN story quoted only 12% of companies used AI last year to deliver not just a faster status quo, but a complete reinvention of the way they work. The automated learning that comes from AI solutions grounded in machine learning also delivers exponential returns to those who start early.
That same news story quantified those benefits as a 20% increase in cash flows over 10 years and the inverse is true as well – a 20% decline in cash flows for those that wait. These kinds of stats should trigger ‘FOMO’ for any enterprise business.
‘BC’ (before Covid-19), the motivation ‘to AI in HR’ might have been the automation of manual expensive HR processes, like recruitment, in a world of declining HR budgets and growing concerns about the bias we humans bring to those processes.
‘To AI’ your HR processes can also go beyond your bottom line. It’s a way to humanise your candidate experience. A way to reduce the asymmetry of recruitment, to empower both sides to make the right decisions. It gives you this kind of candidate feedback from a solution that looks like this.
Right now, curiosity about AI is being replaced by a burning platform for change. For those wearing the exhaustion of surge recruitment using old traditional processes (not to mention the increased chances of bias as a result), the case for change is obvious. For everyone else who does any volume of recruitment, 4 factors will accelerate the move to AI solutions.
1. The need for humanity in your people processes especially recruitment.
Even though tragically it will soon be an employers market as unemployment rises, any organisations, including government, that can make that experience better for job seekers is onto a winner. Nothing sucks more than having to line up at Centrelink, or fill out endless tedious application forms, and then hear nothing.
We ‘live’ on our smartphones, we expect convenience and immediate results, we want to be able to navigate a wide range of opportunities fast and make decisions fast. This applies to services we consume regularly (think Uber Eats, Afterpay, even banking services such as our next home loan). That immediacy and convenience is now the new norm for consumers, and candidates as a consumer of their next job are looking for the same experience.
Imagine if your applicants only needed to answer 5 engaging questions over a text conversation. Every applicant also receives their own personalised feedback which helps them prepare for future interviews!
Compare recruitment to applying for a bank loan where AI has been in use for a decade or more. That’s now a reality with AI in recruitment.
Use Sapia’s FirstInterview to see how easy it is for you to give every job seeker a fast, simple and empowering experience.
And read what job seekers think about it here.
We specialise in volume recruitment for those roles where it is even more critical to hire the right people now. Frontline roles like your customer service teams, carers and health care workers, sales consultants, and blue-collar workers. Our ready-made predictive models are instantly deployable enabling you to go live in under an hour. When using our AI saves you at least $20 on every applicant, (i.e. if you receive 1000 applications, that is a saving of $20,000), and deployment is as easy a sending a link to your applicants, AI offers value to any sized organisation.
3. The right AI tool can remove bias from your recruitment and deliver a more diverse workforce
The ability to measure bias is one reason to use AI-based screening tools over traditional processes. The growing awareness that AI can be fairer for people prompted the California State Assembly to pass a resolution to use unbiased technology to promote diversity in hiring.
Avoiding bias is why we use text data to assess applicants. With 25 million words to draw upon in our data bank, across 10 critical volume hiring roles, our approach is both bias-free in its design and its execution. Our technology is built on the advances in ML and NLP that allow computers to gain valuable insights from large volumes of textual data. Our AI is entirely ignorant of race, age, gender or any of those irrelevant markets of job fit.
Marketing guru Seth Godin wrote a blog a few years ago on the ‘real skills’ that matter in hiring.
Whilst we all know what matters for our roles, our teams, our culture- real skills like resilience, curiosity, humility, drive and so on, these attributes are invisible in a CV and very hard to assess fairly and scientifically in a phone call or f2f interview.
Using text data, we can not only uncover standard personality traits such as extraversion, openness, humility but also real skills that matter such a drive, critical thinking, team player and accountability. Our data science team has recently uncovered that the language one uses in answering standard interview questions show a correlation to how likely they are to hop jobs. New hires that leave early cost significant time and money for organisations. Identifying such candidates early on can help companies make better hiring decisions.