27 September 2023

Data-driven solutions to workplace diversity

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Dan Schawbel* says the development of technology can help organisations in their quest for genuinely diverse, equitable and inclusive workplaces.

The significance of diversity, equity and inclusion in the workplace is well-established.

Researchers first uncovered the connection between a diverse workforce and efficient performance more than two decades ago.

So, why haven’t we seen major improvements in this area?

As organisations seek to diversify their workforces, it’s also critical to ensure that the best talent available is being engaged throughout the process.

There has been progress made in the last two decades in terms of education, training, and awareness programs, but despite that, contingent worker data tends to be scarce.

In fact, one of the biggest obstacles to meaningful improvements in workforce diversity is a lack of such visibility.

If you can’t accurately measure the demographic make-up of your own workforce and your talent sources, you’ll never see measurable improvements.

However, innovative technology has evolved, and businesses are just now beginning to understand how this can move us closer to our diversity goals.

Technologies like AI algorithms and natural language processing (NLP) can help employers boost their diversity numbers.

In broad terms, AI and NLP can remove human bias from the hiring equation, as long as you have a clear view of where those biases exist in the process.

For example, you have to know which words in a job description might create new biases and/or reinforce existing ones.

Once you discover these potential liabilities, you must align these considerations with your algorithms to ensure you’re accurately removing them from the equation.

It’s critical to have technology in place that’s capable of recognising and remediating any language that runs the risk of perpetuating age discrimination or cultural biases.

Software company, CEIPAL has technology that is capable of measuring the aggregate, demographic breakdown of any workforce or talent pool.

This can be done without self-reporting and without reporting on the demographic background of any single individual.

Applied to any database of names and locations, this technology enables you to see the demographic breakdown of the database as a whole.

The algorithms ensure this can be done quickly and with a high degree of certainty, all while respecting the individual privacy concerns of employees and candidates.

That means you can see exactly where the diversity gaps exist within any organisation or talent pool.

You can also add layers upon layers to solve many of the most daunting diversity challenges.

For example, you might find out that your workforce is made of 40 per cent women and conclude that your organisation is doing well.

However, the technology enables you to dig deeper and ask more probing questions, such as what percentage of those women are in leadership roles compared to the percentage of men in similar roles.

Then you might dive even deeper and ask what those women in leadership roles are being paid compared to men in similar positions.

This is just one example of how AI can be leveraged to evaluate an organisation’s diversity progress without the need to ask intrusive questions.

When it comes to AI-powered diversity, equity and inclusion technology, we’re just scratching the surface in terms of its capabilities.

Regardless of the application, algorithms are only as good as the data they process, so we can anticipate that as the data improves, so too will our analysis.

Over the coming years, AI will process the data more quickly and more accurately, giving greater visibility and insights into workforces and talent pools in a shorter amount of time.


*Dan Schawbel is a bestselling author and Managing Partner of Workplace Intelligence, a research and advisory firm helping HR adapt to trends, drive performance and prepare for the future.

This article is part of his Workplace Intelligence Weekly series.

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