“Many people have been denied housing, bank loans, jobs, promotions and more because of their race, but they are rarely told that’s the reason,” said Facebook
’s global director for diversity Maxine Williams, in an article for the Harvard Business Review.
Research shows that individuals who view themselves as objective are often the ones who apply the most unconscious bias, she says.
Williams herself has experienced discrimination because of her race, and says people who are underrepresented in the workplace actually yearn for two things:
- To hear they are not crazy to suspect, at times, that there is a connection between negative treatment and bias; and
- To be offered institutional support.
Research on how pervasive bias remains, but how can workplaces provide institutional support in the quest for genuine diversity? Certainly, if one wants to fire and manage fairly, gut-based decisions are not enough.
“Statistics don’t capture what it feels like to be the only black team member,” she says.
According to Williams, organizations are turning to people analytics: This replaces gut decisions with data-driven, evidence-based practices. “Statistically significant findings have led to some big changes in organizations,” she said.
But some who try analytics find they have little to work with. They “often complain that the relevant data sets don’t include enough people to produce reliable insights – the sample size, n, is too small.”
Data volume alone, however, would not give leaders the insight they need to increase diversity in their organizations. Instead, they must also take a closer look at the individuals from underrepresented groups who work for them—those who barely register on the analytics radar, Williams suggested.
And “to supplement a small n, they can venture out and look at the larger context in which they operate.”
Acknowledging these gaps, Williams makes the following recommendation:
- Look beyond the limited hard data
The phrase “statistically significant” does not have to be present in diversity/ behavioral reports to make managers understand the impact of bias. Hard research, she says, can be combined with what we hear and see on the ground.
In their reports, analysts should also provide confidence intervals to tell managers how much managers can trust the data is the ns are too small to prove statistical significance.
- Be more consistent and comprehensive in qualitative analysis
Companies should to create and process more objective performance evaluations, given the internalized biases of both employees and managers, and to determine how those biases affect ratings.
So what can companies do? They can begin with educating all employees on the real-life impact of bias and negative stereotypes, and form cross-functional teams to help reap the benefits of cognitive diversity.
“Working together stretches everyone, challenging team members’ own assumptions and biases,” Williams says.
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