To find out how to improve candidate experience using Recruitment Automation, we also have a great eBook on candidate experience.
By Jennifer Hewett, Australian Financial Review, 31 January
The online questionnaire wants to know whether I respect and comply with authority. I get five options – strongly agree, agree, neutral, disagree or strongly disagree. I tick “neutral”. Well sort of, sometimes, I think to myself.
Same choice for whether I am good at finding fault with what’s around me at work. I tick “neutral” again, guiltily acknowledging it’s just possible my editor might have a different opinion about whether I am far too good at that particular skill.
The choice seems less ambiguous when I am asked whether I forget to put things back in their proper place. I hover over “strongly agree” or “agree” and tick the latter – perhaps a little optimistically.
And on it goes for 90 questions, with slight variations in the possible answers, as devised by an AI (artificial intelligence) algorithm. My responses to the bot will determine whether I get to the next stage of actually being interviewed for a job by a real person. AI approves who you should interview
I soon get an encouraging email from Michael Morris, chief executive of Employsure – a company which provides advice on workplace relations and health and safety issues to small businesses. If I ever give up journalism, Morris tells me, I can try for a new career at Employsure. AI has approved me. Despite my deep scepticism about the process, I can’t help but feel a little pleased by the bot’s assessment.
That is because my rather self-serving answers to random personality questions fit those of the best performers at Employsure. There’s no possibility of ageism or sexism or any other latest “ism” influencing that. No old schoolmates or university or sporting framework, no biases about looks or clothes or mannerisms or personal history.
Instead, I participated in what is a variation on a personality test – based on the algorithmic analysis provided by another company, Sapia, operating in Europe and Australia and with 20 clients.
Morris says Employsure tested the performance of employees selected by Sapia’s algorithm against the choices of Employsure’s own human recruitment team for much of last year.
The fast-growing company hired around 450 people in 2018 with a workforce now totalling more than 800. Morris wanted good people and those more likely to stay.
The experience convinced him that rather than using more traditional CVs to screen applicants, it was worth paying Sapia for its AI technology as Employsure continues to expand its numbers this year. Employsure now only interviews the 10-15 per cent of those who are graded “yes” or “maybe” by the bot.
“The overlay of AI made a significant difference in overall performance, productivity and tenure,” Morris says. “And it means the recruitment team can have a head start on engaging in better conversations with those who have interviews.” This is still a distinct minority view among Australian businesses which have been generally reluctant to embrace the promise of AI when it comes to hiring.
Read: The Ultimate Guide to Interview Automation
The trend to make greater use of AI in business generally is inevitable and accelerating. Just consider all those online “conversations” we now have about customer service and products as the ever-patient bot nudges us this way and that.
Just as inevitably, it is leading to community concerns about whether AI will be used to replace too many people’s jobs. According to a study by the McKinsey Global Institute, intelligent agents and robots could eliminate as much as 30 per cent of human labour by 2030. The scale would dwarf the move away from agricultural labour during the 1900s in the United States and Europe.
Of course, the record of technology shifts over centuries always ending up creating many more other types of jobs does not completely soothe fears that this time it’s different. Even if such alarm is overstated, dramatic changes in technology can certainly prove socially and economically disruptive for long periods. AI can also be scary.
But this version of AI is more about filling new jobs more efficiently. Many large global companies already use it to filter job applicants, especially those coming in at lower levels. Its advocates argue it efficiently eliminates bias or the tendency for people to hire in their own image.
Not that this always goes smoothly – even for the most digitally sophisticated businesses. Amazon abandoned its own AI hiring tool last October when management realised it had only introduced more bias into the process. Its AI system was based on modelling the CVs of those already at the company – who tended to be male. Naturally, that made prospective hires more likely to be male too. So much for gender-diversity targets.
Sapia’s chief executive is Barb Hyman, formerly a human resources executive for the online real estate advertising company REA Group. She says the system doesn’t work for those companies that don’t measure the performance of their existing employees but the data becomes more and more accurate as more information is added.
By matching responses of applicants against only those employees who are already doing well, it can be extremely efficient with immediate payback – especially for larger companies. The data can also be used to change the culture in an organisation by screening the types of personalities who are hired.
Not surprisingly, Hyman says the data demonstrates how different personalities are better fitted to different sorts of roles. So those who do well in caring jobs tend to be reliable and demonstrate traits of modesty and humility. Good salespeople are focused, somewhat self-absorbed, disorganised and transactional. Those who are involved in building long-term business relationships need to be more adaptable, resilient and open.
Sounds more like common sense than AI. But there’s less and less of that around anywhere. AI beckons instead.
At Sapia we are attuned to research and stories around bias – for most of us, it’s the reason we work here.
Our team has observed the speed with which the blame for Coronavirus has targeted an entire ethnicity.
In this case, I’ve heard some say, “it’s not racism, people are genuinely scared of the spread of the virus. It’s a deadly virus. As it originated in China people naturally worry about anyone from China”.
Unfortunately, this is the very definition of bias.
A flawed logic that seems sensible on the surface, nothing but pure stereotyping underneath. Simply, everyone who looks Chinese are not recent travels from China.
Australia is home to 1.2mil Chinese origin Australians according to the 2016 Census. Should we worry about all of them? Bias has no place in fighting any problem, even when it is a deadly virus. It only creates stress and disharmony.
At the beginning of this week, one of our team who had come down with a cold shared he would work from home, to keep the team safe from his contagion.
We laughed at the time about him being a carrier of Coronavirus. By the end of the week, members of our team with holidays booked to visit family and travel in China during the Easter break had cancelled their trip.
They did this before Qantas stopped their direct flights and before the Australian government announced that Chinese people won’t be allowed back into Australia.
The team member who had a cold this week is Sri Lankan by birth. I guess that means we would have all been safe if he turned up to work as he is the ‘right’ ethnicity.
As a white immigrant myself, I don’t experience those prejudices. I have had career and life opportunities beyond my dreams, unfettered by racial bias.
Building a technology that gives equivalence to such career opportunities is why we work for our company. Some of our team have been screened out of job openings. Maybe they had the wrong name, went to the wrong school or just didn’t look like a cultural fit?
Not all AI is equal. HireVue, an AI-driven recruitment company, has recently been taken to the US Federal Trade Commission with a prominent rights group claiming unfair and deceptive trade practices in HireVue’s use of face-scanning technology to assess job candidates’ “employability.”
Using video is an obvious problem as a data source for reasons around race and gender and their associated biases, but you might be surprised to know that CV’s can be just as flawed and are in much broader use as a first parse for algorithms.
At Sapia, we rely on a simple open, transparent interview via a text conversation to evaluate someone for a role. No visuals, no CV data. No voice data as that too carries the risk of bias. Neither do we take data from Facebook. Using nothing that the candidate does not know about.
Bottom line, testing for bias and removing it from algorithms is possible. Whereas for humans, it’s not.
No amount of bias training will make you less biased. Maybe that’s one reason why using machines to augment and challenge decisions is fast becoming mainstream.
It certainly helps to reduce the impact of unconscious bias in hiring decisions.
PredictiveHire and Iceland Foods are finalists in the 2021 Recruiter Awards for the category In-House Innovation in Recruitment.
Established in 2002, the Recruiter Awards are the UK’s most prestigious honours in recruitment.
The awards recognise best practice and celebrate achievement by agencies and in-house recruiters during the prior 12 months, also throwing a spotlight on marketing and technology. The In-House Innovation in Recruitment category recognises outstanding innovation by an in-house recruitment team that has led to the achievement of strategic business goals.
The success story PredictiveHire has been nominated for starts with 2020 creating a crisis for Iceland, as it did for many. Increased trade and COVID-19 absences meant store leaders were massively drained of time, yet there was a surge in both the need to hire and the number of applicants. As it stood, the recruitment process was 100% manual, and all done at store level by store managers.
Iceland had to innovate. Hiring needed to be centralised and automated.
Iceland developed a set of ‘non-negotiable’ criteria for an automated platform and began looking. They ultimately chose PredictiveHire as their interview automation partner – they loved the notion of ‘hiring with heart’.
Once PredictiveHire was selected, we had integrated with their API (Kallidus) and the applications started rolling in within four weeks. Iceland estimated in the first 4 months they saw 5x payback, 8,000 hours freed up for their time-poor store managers across the organisation, and over 50,000 applications were processed every month. They also found that 97% of candidates who followed the link to apply completed the application process and 99% of candidates reported a positive experience.
You can read the full Iceland Foods + Kallidus + PredictiveHire story here.
The award will be presented at the annual Gala in London on September 23, 2021.
The rigorous judging process is done by a judging panel of 33 credible and experienced industry professionals including Rob McCargow, Director of Artificial Intelligence at PwC UK, James Fieldhouse, M&A Managing Director at BDO, and Karolina Minczuk, Relationship Director at Natwest Bank.
Other nominees are BDO in partnership with Amberjack, BUPA, GQR, and Virgin Media in partnership with Amberjack.
PredictiveHire is a frontier interview automation solution that solves three pain points in recruiting – bias, candidate experience, and efficiency. Customers are typically those that receive an enormous number of applications and are dissatisfied with how much collective time is spent hiring. Unlike other forms of assessments which can feel confrontational, PredictiveHire’s FirstInterview™ is built on a text-based conversation – totally familiar because text is central to our everyday lives
Every candidate gets a chance at an interview by answering five relatable questions. Every candidate also receives personalised feedback (99% CSAT). Ai then reads candidates’ answers for best-fit, translating assessments into personality readings, work-based traits and communication skills. Candidates are scored and ranked in real-time, making screening 90% faster. PredictiveHire fits seamlessly into your HR tech-stack and with it you will get off the Richter efficiency, reduce bias and humanise the application process. We call it ‘hiring with heart.’
If you’d like to stay up to date with PredictiveHire, you can subscribe to our newsletter here.
Over a six-month period, Sapia gave personalised, same-day feedback to 250,000 candidates after each completed a text-based interview using its AI platform, says CEO Barbara Hyman.
The candidates ranged in age from 16 to 80 and included people from non-English speaking and Indigenous backgrounds. The feedback highlighted their strengths, as well as tips on areas for development.
The outcome, Hyman tells Shortlist, is that 99% of candidate reported satisfaction with their interview experience; 70% said they were more likely to recommend the company as an employer of choice; and 95% reported they loved receiving their feedback and “found it empowering, constructive and ‘scarily accurate'”.
Recruiters using Sapia gain insights into each candidate’s personality and the quality of their response to behavioural interview questions.
Sapia realised candidates would benefit from receiving some form of feedback and insight into their traits as well, and so it began rolling this out 15 months ago. The feature has since won a UK-based candidate experience award.
The feedback specifically does not include information about whether the candidate is a good fit for the role, “because that’s not our job – that’s the client’s job”, Hyman says.
For AI to be trusted, she says, the candidate needs to trust it, and so the candidate needs to get something out of it – including “the ability to understand themselves”.
Candidate experience isn’t simply an automatic email that says, “thanks, we’ve had lots of applicants, but we may not get back to you”, Hyman says.
Rather, a good candidate experience is “when everybody gets something out of it”.
“There really isn’t any excuse now for ghosting. And the feedback that companies give when they do it through humans is not that constructive. Getting a phone call saying you’re not a great culture fit – what’s that telling you? That’s a big cop-out.”
When Sapia first deployed the candidate feedback feature, its clients were initially too scared to use it, says Hyman.
“They thought that if you give candidates feedback, you’ll risk a whole lot of candidates calling up and asking, ‘why didn’t I get the job?’ or candidates would disagree with it and it would undermine their trust in the process. This might diminish their employer brand,” she explains.
But these fears proved unfounded when recruiters started reading the responses candidates were invited to give about their feedback, which included whether they agreed with the feedback and whether they would recommend the organisation as an employer or retailer (most of Sapia’s clients are consumer brands).
“The fact we were able to show to clients what candidates thought about it really disrupted that fear and killed the notion feedback is a ‘risk’.
“In fact, what candidates feel is feedback is a gift, and that gift is really playing out in terms of employer brand,” says Hyman.
Reference: Shortlist 2020 | https://www.shortlist.net.au
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