It is widely thought that Thomas Edison invented the concept of the job interview back in the early 1900s. To screen candidates, he would ask them to join him at a restaurant and eat a bowl of soup while he watched. He could pick out the losing candidates by their tendency to season their soup before eating it. According to Edison, premature salt-and-peppering speaks to a person’s over-reliance on assumptions. If you’re a true visionary, he posited, you leap into your soup face-first.
The soup test is definitely out there. And, given what we now know about psychology and candidate experience, it is not, strictly speaking, scientifically valid. But this exercise was first tested more than 100 years ago, so maybe we can forgive Edison for filling the holes in his data with social experiments.
Funnily enough, though, things haven’t changed much since Edison souped up his hiring game. Initial face-to-face job interviews remain the predominant tool of hiring managers. There are benefits to in-person interviews, but the deficits certainly outweigh the benefits. Simply put, the practice is infused with all manner of biases, unfairnesses, inefficiencies, and oddities. In the early 1900s, we had soup – now we have inscrutable corporate-isms, and bizarre group tasks with arbitrary scoring criteria.
Let’s say you’re looking to fill a position where quick thinking and adaptability are the two most important skills. You want your candidates to think fast, and think smart, especially when faced with sudden adversity. How do you find these people?
There is no perfect answer. People are people, after all. But there are far better ways to find out than dropping a pen in the middle of an interview to see whether or not a candidate picks it up for you. The ‘pen-drop’ test assumes that the quickest candidates are the most adaptable, and are the highest in empathy. But we have more reliable predictors for these, predictors subject to far fewer variables. The quickest pen picker-upper on a given day may not be the best lateral thinker, or the most open – they may have merely been the shortest candidate, or the most flexible candidate, or the candidate closest to the pen. Because you don’t have a control, or any way to account for variables such as these, can you really trust the findings?
Yes, the pen-drop test is an extreme example of a screening exercise that is only tenuously related to its desired outcome. But we have all, at some point in our working lives, participated in strange tasks and odd jobs during interviews. The greater point is this: Even the best-planned exercises are not a viable substitute for sound scientific measurement.
The HEXACO personality inventory has at least three major dimensions relating to the test of a quick-thinking, empathic person: Extraversion, agreeableness, and conscientiousness. If you can assess a candidate using the HEXACO inventory, you might learn that the candidate is:
And that’s only the start of what you might learn. By using an Ai-based recruitment or hiring tool, with a HEXACO personality modelling function, you have a simple, trustworthy, accurate, and fair way to sort your quick thinkers from your leaders, your leaders from your long-term planners, and so on.
That’s the essence of what a smart interviewer can do, and why we developed the world’s first smart interviewer. You no longer need to think up some strange post-interview exercise where you pull unsuspecting candidates into an impromptu indoor hockey game. You can simply:
(We’re not the fun police, of course. If your approach to offering first-rate candidate experience involves a blind-folded three-legged race, count us in. Just make sure you have a smart interview waiting at the finish line. Fun, then statistical validity. Best of both worlds.)
We all want a world filled with better, fairer, simpler interviews. How will you go about it? Data, or gut-feel? Soup, or science?
Do you know how much employee turnover costs your organisation? And how much you could save by improving your retention?
Only 28% of organisations can answer ‘Yes’ to these two questions!
For everyone else, it’s a daunting dilemma that’s often swept under the carpet. But that’s obviously not going to solve the problem.
If you really want to do something about your turnover issue, the first step is to fully understand it.
So, we have highlighted the 11 essential things you need to know about employee turnover in this handy infographic!
All the facts in the infographic are hand-picked from our white paper ‘Employee Turnover: The hidden cost crippling business’.
The paper explores virtually every aspect of turnover, including the best metrics to monitor, costs to include when you calculate your turnover costs, and how to go about combating the issue before it gets out of hand.
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/)
To create an organisation that hires and promotes everyone equitably, and has a diverse representation of people, you must confront uncomfortable truths – and have the confidence to employ compassionate solutions. The path to meaningful change cannot be done without data. Data allow us to clearly diagnose issues in a company, and also, just as importantly, to measure what is working. Without data-backed accountability, it’s unlikely that much will change.
The good news is that, these days, most organisations believe that diverse teams are beneficial to business outcomes, innovation, customer loyalty and employee trust. The better your team represents its customers, the fewer blind spots you’ll have when it comes to meeting customer needs.
The biggest challenge is not often not around intention, but rather diagnosing what is happening inside an organisation. The reasons contributing to bias are often numerous and complex, like company history, systemic racism and sexism, leaving decisions to ‘gut feelings’, and well-intentioned directives to hire based on “culture fit” that only result in more homogenous teams.
This is where data are so powerful.
Data help us look at the facts objectively, and while we might “feel” we hire fairly, it is impossible for a human to hire without bias. Data allow organisations to have an honest look at where they are falling short, assess how specific groups are not being treated equally, and address these issues before churn becomes an issue.
Here are five things you need to be doing to help data drive better Diversity, Equity and Inclusion (DEI) in your organisation.
In order to measure and track your progress on DEI you need to look at what current data you have and identify any data gaps. This is more than just identifying the demographics of who you have historically hired. For example, of the women you hired recently, do you know what percentage were represented in applying for a job versus landing a job? If 70% of people applying for the job are women, and 30% are only getting jobs, you need to identify where in the funnel there is a drop-off.
Note: There are sensitivities and legalities to collecting data around demographics and there are differing laws within countries about how you can do this.
Sapia’s reporting dashboard DiscoverInsights (Di) takes that worry away and gives you all the real-time metrics needed, and can instantly fill any data gaps you have. Candidates are never asked a personal or intrusive question and this data is not used in vetting candidates (keeping it blind and equatable.)
When hiring managers complain that they only had men applying for a role, or that there wasn’t any representation of Indigenous peoples, or that no one under 40 applied, they are talking about lagging indicators on inclusion. These point to issues in a hiring process that is not inclusive. Leading indicators might be a real-time analysis of the demographics of applicants so that hiring managers can change their approach quickly.
DiscoverInsights (Di) also reduces the the risk of lag indicators on DEI, by giving you real-time lead indicators so you can instantly assess the inclusiveness of your approach to hiring.
The data and platform you are using to track metrics and assess your progress needs to be agreed on from the outset, and should become your single source of truth. This is an important part of keeping everyone accountable (improving DEI is the responsibility of everyone in a company.) This should be a platform that cannot be used to present a desired outcome, but rather it should aim to be a robust fact-driven dataset that shines a light on issues. Identifying problems is the only way an organisation can address them.
Building trust among your employees on issues around DEI is foundational to the success of your initiatives. Be transparent about your findings, even if they feel uncomfortable. Part of what makes successful DEI measures is the leadership shown by the C-suite in acknowledging faults, identifying how they will be addressed, and making themselves accountable to employees on delivering these changes.
This is being accountable. Measure where you are at on DEI, learn from it, and set on improving on where you are. Then do it again. This is where the power of data really lies: By trying initiatives and testing what is working, and then measuring the outcomes, you can iterate quickly when no headway is being made. This takes all the guesswork out of whether there is improvement or not.
We have helped scores of the world’s biggest and best companies implement, track, and achieve their DEI goals. To find out more, check out our guide on data, equity and inclusion.