Education

Want the perfect job in a fair and diverse workplace? There's an app for that

Fair game ... neuroscience has inspired a new recruitment tool Image: REUTERS/Andrew Kelly

Barbara Marder
Senior Partner and the Global Innovation Leader, Mercer
Frida Polli
Founder and CEO, Pymetrics
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It’s true: women have what it takes to be successful traders, entrepreneurs and software engineers. And now, using advances in gamification, neuroscience and data science, there is a way to prove it. Emerging new career assessment and hiring platforms clearly show that women are equally qualified for traditionally male-dominated professions, even though the current workplace doesn’t reflect this yet.

Once thought of as purely entertainment, games are now being used as accurate behavioural assessment tools.

In a game, rather than asking someone what they would do (and receiving a flattering self-portrait in reply), you can directly and objectively measure a person's behaviour. Using games, you can remove the self-report bias that is present in even the best-designed personality or career questionnaire.

In fact, the National Science Foundation, US Army Research Institute and the US intelligence community have all supported projects that test human behaviour using video game platforms. These platforms are of interest to other demographics too: immersive and engaging, they can grab the attention of graduates and jobseekers. They are especially successful with millennials, the generation that now represents the largest share of the American workforce, as well as Generation Z, the group now entering the workforce.

The neuroscience community has developed behavioural games that collect quantitative and unbiased data on cognitive and emotional aptitudes, which can then be mapped to career success. These data points allow people to form a more objective and accurate idea of the kind of career that would suit them than if they followed the traditional approach, which calculates according to their interests, motivations or academic history and can be muddled by subjective self-reporting and gender stereotyping. Neuroscience can recommend careers in an objective and bias-free way.

In addition, researchers have long lamented the limitations of traditional (linear) modelling to uncover the complex relationship between traits and career success. But advances in machine-learning algorithms, data science and artificial intelligence are radically improving this predictive power. Sophisticated analysis techniques allow us to capture non-linear relationships, and are therefore better at modelling real-life problems.

However, nothing is infallible, not even an algorithm. It’s important that the formulas used for career assessment and hiring are tested and confirmed to be free of bias. If they are, they essentially become “blind auditions” for career assessment and hiring. Blind auditions evaluate people on their raw potential, free of gender-bias. In orchestras, they have increased female participation from 5% to 35%. Unbiased career assessment and hiring could also see more diverse candidates participating in traditionally white, male professions.

It's already happening, according to the early data. These platforms are allowing companies to achieve gender parity in their application flow and reach a larger selection of people from non-traditional backgrounds.

Inspired by this new approach to career advice and job matching, the global consulting company Mercer is partnering with a tech start-up called Pymetrics to demonstrate the potential of women, veterans and other underrepresented groups when it comes to traditionally homogeneous, male-dominated STEM professions, such as engineering and finance.

It’s time to harness the power of new technologies to bring career assessment and hiring into the 21st century. This will benefit not only women and minorities, but also the global economy. Ultimately, improving diversity in the workforce is not just about equality; it is about economic health.

You can find more blogs in the Skills for Your Future series here

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