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The overlooked benefits of algorithms in the workplace

The overlooked benefits of algorithms in the workplace

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They describe the potential of using candidate screening technology in the form of an online game, such as Wasabi Waiter by a company called Knack, in which one person is a waiter at a busy sushi restaurant. How can this be effective in evaluating job applicants?

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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. They don’t just want what we call exploitation algorithms that look at who has a history of becoming successful employees, like someone who graduated from an Ivy League college and was a sports team captain.

There is a lot of talk about the black box problem that it is difficult to understand what the algorithm is actually doing. But also, based on my experience as an expert in employment discrimination litigation and as a hiring researcher, it is very difficult to penetrate the black box of our human mind and understand what happened. With digital processes, we actually have that paper trail and can verify that a game or some type of automated emotional screening outperforms the previous type of screening in creating a more diverse pool of people.

My personal experience of applying for jobs that require aptitude tests and personality screening is that I find them opaque and frustrating. Talking to someone face to face can give you a little sense of how you are feeling. When the whole process is automated, you don’t even really know what you’re being tested for.

Many people feel that. But I’m going to be a little different here. It’s not just about how people experience the interview, but what we know about how good people are at making judgments during an interview.

There’s a body of research showing that interviews are a poor predictor of job performance and that interviewers consistently overestimate what they can actually get out of an interview. There’s even research that shows prejudice creeping in in a matter of seconds. If we’re serious about expanding the pool of people eligible for a job, the sheer number of applicants will be overwhelming, at least for one human in the early stages.

Many of these workplace prejudices are well documented. We’ve 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 stagnant the gender pay gap has been even though we have equal pay legislation on the books. With the huge datasets that are now available, I think we can do better. Textio’s software helps companies write job ads that are more comprehensive and result in a more diverse pool of applicants. Syndio can detect wage inequalities between different sections of the workforce in large workplaces, which can be harder to detect.

It’s kind of intuitive: if we use software to browse many different payment methods and many different job ads, we can pierce through the veil of formal job descriptions in a large workforce and see what’s happening in terms of gender and race. We used to have this notion of one shot auditing – once a year – but here you can do continuous auditing over several months, or if there’s a sudden increase in pay gaps caused by things like bonuses.

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