You describe the potential of using candidate screening technology that takes the form of an online game, such as Wasabi Waiter from a company called Thing, where a person is a waitress in a busy sushi restaurant. How can this be effective in assessing job candidates?
Courtesy of Hachette
It’s about thinking more creatively about what we’re looking for, using insights from psychology and other research into what makes a good team player. You don’t just want what we call mining algorithms, looking at who’s become successful employees in the past, like someone who’s graduated from Ivy League college and been captain of a team athletic.
We talk a lot about the black box problem, about how difficult it is to understand what the algorithm is actually doing. But in my experience as an expert witness in employment discrimination litigation and hiring research, it’s also very difficult to break into the black box of our human minds and trace what’s going on. ‘happened. With digital processes, we actually have that paper trail and can test whether a game or some kind of automated emotional screening will outperform the previous screening method by creating a more diverse group of people.
My personal experience of applying for jobs that require aptitude tests and personality tests is that I find them opaque and frustrating. When you talk to someone face to face, you can get an idea of how you are doing. When the whole process is automated, you don’t even really know what you’re being tested on.
That’s what a lot of people feel. But this is where I get a little more annoying. It’s not just about how people experience the interview, but what we know about people’s ability to make assessments during an interview.
There is quite a bit of research showing that interviews are a bad predictor for job performance, and that interviewers systematically overestimate what they can actually get out of an interview. There is even to research it shows how within seconds prejudice creeps in. If we really want to expand the pool of people eligible for employment, the number of applicants will be too large for a human, at least initially. steps.
Many of these workplace biases are well documented. We have known about the gender pay gap for a long time, but closing it has been very difficult. Can automation help there?
It has been frustrating to see how the gender pay gap has stagnated, even though we have equal pay laws on the books. With the vast datasets now available, I believe we can do better. Textio’s Software helps companies write more inclusive job postings and create a more diverse candidate pool. Syndio can detect wage inequalities between different parts of the workforce in large workplaces, which can be harder to see.
It’s pretty intuitive: if we use software to look at lots of different compensation schemes and lots of different job postings, we can peek through the veil of formal job descriptions in a large workforce and see what happens in terms of gender and race. We used to have this idea of a one-time audit – once a year – but here you can have a continuous audit over several months, or when there’s suddenly an increase in pay gaps introduced by things like bonuses.
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