We know that the global pandemic has caused a disruption in global workforces. Much has already been said about the Great Resignation, and how it has morphed into the Great Reshuffle, a period in which many are looking to reinvent themselves in the light of new jobs and careers. No industries or role types have been spared, either, it seems – even recruiters are leaving positions in the tens of thousands.
With a reshuffle, however, comes uncertainty, doubt, and anxiety. The war on talent may have benefited some, but the path to career reinvention is by no means guaranteed. Consider the following factors, factors job-hunters must face every day:
It’s little wonder that some Great Reshufflers, especially emerging adults (ages 18-24), are experiencing anxiety about working in the post-COVID world. Instability is the only constant. Consider, too, that some people are better at dealing with uncertainty – or, in technical terms, they are higher-than-average in the HEXACO personality traits Flexibility (or Adaptability, as it’s sometimes known).
This hypothesis is supported by at least one study, published last year in the International Journal of Social Psychiatry. It suggested that, “…due to the outbreak of ‘Fear of COVID-19’, people are becoming depressed and anxious about their future career, which is creating a long-term negative effect on human psychology.”
The traditional face-to-face interview is typified by stilted small talk and a general air of nervousness. If a candidate is low in Extraversion, high in Agreeableness, or high in the Anxiety and Fearfulness scales of the Emotionality personality domain, their experience of walking into a blind interview is likely to be worsened by the additional stressors left by COVID-19.
Consider, as is likely to be the case, that the candidate might possess a combination of all three traits, in the proportions laid out above. These people, especially if they are young, may not even bother to apply for a job in today’s climate.
The ramifications of this are obvious: You risk, at best, filling your workforce with open, disagreeable, type-A employees. At worst, you risk baking unfairnesses or bias into your recruitment process, at the cost of good candidates who don’t shine in awkward face-to-face situations.
Take this small data visualisation from our TalentInsights dashboard as a key example. Please note here that the following results apply to the outcomes of the hiring process, and not Smart Interviewer’s recommendations.
It presents an assessment of candidate hiring outcomes according to key HEXACO personality traits. The red dots represent female candidates, the blue dots male. Immediately, we can see that when it comes to Conscientiousness – one of the best predictors of workplace success – females and males are more or less identical.
The main differences between the two genders occur, however, in the domains of Agreeableness and Emotionality. Combined, these two traits are good predictors of anxiety and/or aversion to fear. As you can see, females tend to be higher in Agreeableness and Emotionality than males.
Though the difference is not incredibly significant, it is still present – and it may require a slight change to the way you bring female candidates into your hiring process. The data proves, of course, that your best candidates are just as likely to be female as male – but your recruitment tactics may be producing outcomes that favour males.
We’ve said it before, and it’s the whole reason we exist: A blind, text-based Chat Interview with a clever, machine-learning Ai. Smart Interviewer is our smart interviewer, and it has now analysed more than 500 million candidate words to arrive at the kinds of data points you see above. It helps you combat bias at the top of your funnel, and gives you the Talent Analytics you need at the bottom.
And it works. Take it from the candidates high in Agreeableness:
“I have never had an interview like this online in my life… able to speak without fear or judgement. The feedback is also great to reflect on. I feel this is a great way to interview people as it helps an individual to be themselves and at the same time the responses back to me are written with a good sense of understanding and compassion also. I don’t know if it is a human or a robot answering me, but if it is a robot then technology is quite amazing.”
– Graduate Candidate A
“[It was] approachable, rather than daunting. I found the process to be comprehensive and easy to complete. I also enjoyed that the range of questions were different than those commonly asked. The visual aspects of the survey makes the task seem approachable rather than daunting and thus easier to complete.”
– Graduate Candidate B
The future of work is uncertain. But with a fair and unbiased assessment tool, you can prevent the best talent from being lost under the dust of the Great Reshuffle – and save a lot of time and money doing it.
COVID has taught us that on reflection the focus on individual action with a community benefit as a goal is really a focus that leads to the greater good. In our home state of Victoria, Australia now 7 straight days with ZERO new cases. It has been an effort founded on facts and science over misinformation. In Victoria, many sacrificed a lot for their well-being for ALL. If anything, there is now proof, thanks to Victorians, that when we see facts, listen to science and let data show you how to lead that change, you can make it happen.
AI, especially predictive machine learning models, are an outcome of a scientific process, it’s no different to any other scientific theory, where a hypothesis is being tested using data.
The beauty of the scientific method is that every scientific theory needs to be falsifiable, a condition first brought to light by the philosopher of science Karl Popper. In other words, a theory has to have the capacity to be contradicted with evidence.
There are three decisions that are made by a human in building that scientific experiment.
One can argue 2 and 3 are the same as if the methodology is not sound the data collection wouldn’t be either. That’s why there is so much challenge and curiosity as there should be about the data that goes into an algorithm.
Think of an analogy in a different field of science: the science of climate change.
A scientist comes up with a hypothesis that certain factors drive an increase in objective measures of climate warming, eg CO2 emissions, cars on the road, etc. That’s a hypothesis and then she tests it using statistical analysis to prove or disprove that her hypothesis holds beyond random chance.
The best way to make sure you are following a sound scientific approach is to share your findings with the broader scientific community. In other words, publish in peer-reviewed mediums such as journals or conferences so that you are open to scrutiny and arguments against your findings.
Or to put it another way, be open for your hypothesis to be falsified.
In AI especially, it is also important to keep testing whether your hypothesis holds over time as new data may show patterns that lead to disproving your initial hypothesis. This can be due to limitations in your initial dataset or assumptions made that are no longer valid. For example, assuming the only information in a resume related to gender are name and explicit mention of gender or a certain predictive pattern such as detecting facial expressions are consistent across race or gender groups. Both of these have been proven wrong*.
The only way to improve our ability to predict, be it climate change or employee performance, is to start applying the scientific method and be open to adjusting your models to better explain new evidence.
Therefore the idea that a human can encode their own biases in the AI — well it’s just not true if the right science is followed.
* Amazon scraps secret AI recruiting tool that showed bias against women (https://www.reuters.com/article/us-amazon-com-jobs-automation-insight-idUSKCN1MK08G)
* Researchers find evidence of bias in facial expression data sets (https://venturebeat.com/2020/07/24/researchers-find-evidence-of-bias-in-facial-expression-data-sets/)
On 26th August, our CEO Barb Hyman facilitated a webinar on “Hiring with Heart” in collaboration with The Recruitment Events Network.
To our surprise, Jeff Uden who is the Head of Talent and L&D for Iceland Foods also joined the webinar.
During the session, Jeff offered some wonderful comments. We took a transcript of Jeff’s input and have jotted it here. It offers insights on dealing with enormous volumes of candidates, offering positive candidate experience and communicating culture from a candidate’s first experience with a brand.
Thanks for your insights, Jeff. Incredibly valuable.
At Iceland Foods, we have started working with Sapia. That was as a result of a couple of things. One was the element of the mass recruitment that we were doing. Just to put it in perspective, in the first four months of this year, we received over five hundred thousand applications.
We wanted to find a way that delivered a level of fairness, a level of consistency around how we sift those applications that then enabled store managers to reduce that amount of time that they are spending on doing the recruitment.
The other thing that we wanted to do was significantly enhance our candidate experience. One of the challenges that I had around the experiences that we had within the business is that it felt like it was really standard. It felt like it was cold; it felt like it came from a computer. We wanted to change how we did that and more importantly give something back to the candidates.
Often nowadays people apply for jobs, and there’s the standard ‘bulk’ response that says if you haven’t heard anything from us in two weeks take it that you haven’t been successful.
As big companies or companies of any size we have a duty to help those individuals to understand why they haven’t been successful and to help them to be successful in the next role for which they apply.
The fact that they won’t be hired into your business is probably the right decision because they wouldn’t have been the right fit given the testing that they have gone through. However, that doesn’t mean they are a bad individual. What we need to do is to help them to understand where their strengths are and where their development needs are, and certainly, that was a massive appeal of working with Sapia.
Going through and reading some of the feedback that we’ve had from the candidates, it’s having a huge effect on the candidate experience.
We had a swift implementation planned. But probably one of the lengthiest parts of it was about actually getting the questions right and getting the language right. We really did spend a decent period doing that.
I just had a quick look at one of the pieces of feedback here, and this is completely unedited:
That’s what’s coming over from the way in which we put the language across within the questions.
We are genuinely really chuffed about how they are engaging far more with us as a brand and how they are feeling like they are getting something back. They genuinely don’t feel like this is a computer process in any way whatsoever; they genuinely feel like they are talking to people.
To keep up to date on all things “Hiring with Ai” subscribe to our blog!
If there was ever a time for our profession to show humanity for the thousands that are looking for work, that time is now.
I am not a CFO but surely every CFO out there is encouraging, if not mandating, that their leaders look for investments that keep delivering business value (over those that are a sunk cost, or a one-time use).
I am a CEO though, and I like to ask the same question around meaningful data that keeps delivering value to a business. Because I am a CEO of a HR Tech company solving for human recruitment at scale, I also ask this question about meaningful recruitment data.
Sure HR departments are drowning in data, but it’s often not the right data.
Meaningful recruitment data isn’t:
Meaningful recruitment data:
Think about a candidate who completes a typical assessment and then gets hired. Usually, that’s the end of the data story. Josh Bersin reckons about $2bn a year are spent on these ‘disposable’ assessments. Each time one of these assessments is used it is a sunk cost. The data goes into the system and stagnates there, never to be used again.
Wouldn’t you love to know what your newest hire is capable of, beyond the job they’ve been hired for? What other roles they could fill as business needs change? Or say you need 1,000 contract tracers fast? Or your business plan calls for 200 agile coaches or 50 product managers immediately?
If you don’t have easily accessible data on your employees’ aptitudes, their strengths and underutilized skills, then every time you are forced to restructure you do it inefficiently–at huge cost to both your people and your bottom line.
HR teams need to be thinking about how we use data about company employees to continually improve recruitment and retention. In much the same way that marketing and advertising uses data to learn about what people want, and recommend things based on that.
Imagine the world of possibility if recruitment data was used this way. Imagine if we built an Amazon recommendation system for people’s skill sets that looked at their ability to perform in any role?
What are you waiting for? Let us show you what we can do for you.