Women at work


This Women’s History Month, I’ve learned a lot about the role of women in the workforce. Women were “welcomed” into the workforce during WWII as the male labor supply steeply declined and the war effort expanded and continued. Despite this emergence of a female labor class – who took on some of the jobs previously and exclusively held by men –  women were still marginalized.

Fast forward to today. There has been outpouring of confessional tales of workplace harassment and discrimination as a result of the #MeToo and #TimesUp movements have shined a light on injustice that surely no woman has failed to experience.

So, while we celebrate women’s history in the workplace, recent events have shown that the workplace is still fundamentally male and based in a dominance culture rife with self-serving biases.

How do we fix it? Here’s my take:

Recognizing bias is only half, maybe one third of the battle. In our research developing the Riff Platform, we have studied gender and cultural bias in a variety of settings. What we understand is that awareness is very important for changing people’s behavior, but knowing what action to take is how people actually change. For example, if you drive through a residential area and you know that the speed limit is 30MPH, if you’re inclined to speed (which is likely self-serving in some way), then you will speed, even with that awareness. However, if a sensor showing your speed is placed next to the same sign, you will slow down. Awareness combined with a nudge is much more effective.

In the case of workplace biases, (for example: collaborative exchanges between coworkers) people may be dimly aware that they interrupt, tend to ignore, or endorse certain people. When they are shown actual data that reinforces what they may or may not know, and suggestions toward better behaviors, their biases are gradually eroded and new, healthier workplace cultures arise.

Bias is drastically reduced in highly functional teams. In Charles Duhigg’s 2016 New York Times Magazine piece, “What Google Learned From Its Quest to Build the Perfect Team,” we come to understand two key factors that indicate for successful teams. One is that people in these teams tend to have equally distributed conversational turn-taking, (an active measurement with real-time feedback in the Riff Platform). The second is that they have higher-than-average social sensitivity, which means that they are deeply aware of the feelings of their teammates. In other words, respect and empathy between members characterized good teams.

As Women’s History Month draws to a close, we cannot forget the lessons we’ve learned about the role of women in the workforce. Since WWII, the boundary of the margin may have changed, but the fact of it did not. Glass ceilings still exist, a gender wage gap is a reality, and bias against women (particularly in leadership roles) is a sad truth.

The Riff platform can help change the story. I contend that you can literally build good teams through computational social science techniques. When you systematically and consistently measure and give feedback about these characteristics to teams, real change can occur.