Gendered Ageism Is Everywhere Online. Now It's in Your Hiring Pipeline

Solène Delecourt (0:00)

Hi, Sameer.

Sameer Srivastava (0:01)

Hey, Jenny.

Jenny Chatman (0:02)

So to start off today's episode, I want to ask you a question about perceptions of online content. It's so incredibly pervasive in our lives these days.

So have you ever seen something online, maybe a restaurant review or a vacation rental, and then gotten to the place, and the reality is completely different than the online image?

Sameer Srivastava (0:24)

Vacation rental comes to mind. So last summer, my family was traveling to Iceland, and we were getting an Airbnb and we found one that looked great in the photos. Everything looked exactly as we wanted until we actually showed up at the place and realized that there were no window treatments on the many skylights in the property. And of course, this is the land of the midnight sun. So we got exactly no sleep that night that we stayed in that place.

Jenny Chatman (0:51)

Oh, that sounds rough.

Sameer Srivastava (0:53)

It was.

Jenny Chatman (0:54)

Well, today we're going to explore how the images we see online can influence how people behave at work. Only this time the stakes are much higher than having a bad night's sleep or a crummy vacation rental.

We're going to be talking about distortions around gender and age and the impact these distortions have on organizations.

Sameer Srivastava (1:17)

I'm Sameer Srivastava, a professor at UC Berkeley's Haas School of Business and co-founder of the Berkeley Center for Workplace Culture and Innovation.

Jenny Chatman (1:26)

And I'm Jenny Chatman, a professor and dean at the Haas School. And this is The Culture Kit.

Sameer Srivastava (1:33)

Today we're joined by Solène Delecourt, Assistant Professor of Entrepreneurship and Innovation here at UC Berkeley's Haas School of Business.

Solène has recently published a paper in Nature, of all places, on the distortion of gender and age in online media and large language models. Her earlier research focuses on what drives variation and profits across firms and how we can reduce inequality in business performance among entrepreneurs in different market settings. This more recent research identified many areas where reliance on AI models or simply even our online searching might affect our expectations about how someone should look, or perhaps even influence how we think about pursuing a new role if everyone appears different from us in age or gender, say a doctor or an astronaut. We'll be talking about how this can affect organizations and erode some of the progress we've made towards gender equality and how leaders can actively counter it. Solène, welcome to The Culture Kit.

Solène Delecourt (2:34)

Thank you so much. I'm so excited to be here today.

Sameer Srivastava (2:37)

Solène, I know gender inequality is a theme that runs through a lot of your research, but this particular study starts with a fundamental question, which is, are stereotypes, particularly those we find online, accurate reflections of reality or are they distortions in some way?

And you chose age and gender as the place to dig into that question. Why was that the right lens in your mind, and what made it possible to actually study this question rigorously?

Solène Delecourt (3:05)

This is a very timeless question, whether stereotypes are accurate or if they're distorting reality, and what's very cool about age in particular is that it has an objective reality, it has an objective truth to it. If you know someone's birth date, you can measure their age, and there is no subjectivity about that, it's sort of very agreed upon. So the feature of being able to look at age in particular is that we can know if the representation is accurate or if it is distorted. And in that particular paper that was published in Nature that you were talking about, we look at how men and women are represented online in terms of age, and we find that women are over-represented as younger compared to men. And that is the case for the average woman online, but that's not an accurate representation of reality at all. That is a distortion because we know that the average woman in the real world is in fact older than the average man.

Women have a higher life expectancy than men, and so we know for a fact that the average woman in the real world is older.

But online, if you look at text or images or videos, you would think that the average woman is younger than the average man. If you were just looking at the online content and trying to predict reality, so that is a very cool feature of the data that allows us to look at this distortion.

Sameer Srivastava (4:48)

To get at representation, you also looked across industries and years using census data. Can you say a little bit more about that?