529. Fixing Systems, Not People: What Works With Equality feat. Iris Bohnet

What does a workplace look like where everyone can thrive and flourish? Once we know the makeup of that space, how can companies work to achieve it? When is it smart to rely on numbers and when will strict adherence to data lead you astray in the quest for equality?

Iris Bohnet is a professor at the Kennedy School at Harvard and the author of the books Make Work Fair: Data-Driven Design for Real Results and What Works: Gender Equality by Design.

Greg and Iris discuss the concepts of workplace fairness, representation, and the indicators of a fair work environment. They delve into implicit and explicit biases, systematic interventions like structured hiring and promotions, and the effectiveness of diversity training. Iris emphasizes the importance of focusing on systemic changes rather than trying to 'fix' individuals. They also touch upon the necessity of role models, the impact of organizational culture, and the balance between fairness and business objectives. 

*unSILOed Podcast is produced by University FM.*

Episode Quotes:

We should stop trying to fix people and fix our systems

09:17: We should stop trying to fix people and fix our systems. And this goes way beyond bias in terms of gender, race, or anything other in terms of demographic characteristics or social identities, but just general in behavioral science. We have by now identified more than 200 different types of biases. It's incredibly hard to unlearn them, and so that's why many behavioral scientists, again, beyond the question of fairness, now focus on changing the environment. So basically making it easier for all of us to get things right.

Meritocracy and the need for fairness

15:01: There is no meritocracy. Without fairness, we have to have that equal playing field to allow the best people to end at the top. And so, I think meritocracy is a valuable goal to have. I don't think we have ever lived in a meritocratic world.

Representation as an indicator of fairness

02:14:  Representation is not a dependent variable per se, independent of anything else. But, as you said, it is a bit of an indicator of whether what we're doing truly creates a level playing field where everyone can thrive.

On the value of larger diverse talent pool

16:07:  We now benefit from a larger talent pool. And that's the argument behind it—the larger talent pool has two implications. One is we literally have a larger talent pool, so we can draw from more people, and it goes back to the quote that you offered earlier: we're more likely to find the right person for the right job at the right time. And secondly, and that often is overlooked, we can also allocate that work better, that, in fact, Sandra Day O'Connor finds exactly the job for which she excels. And that fraction of GDP protector growth is about 14%. So I think that's the macro business case that I always have to remember—that, in fact, more talent is just good. And giving the talent the chance that they deserve and that our organizations deserve is both the right thing and the smart thing to do.

Show Links:

Recommended Resources:

• Intersectionality
• Claudia Goldin
• Proportional Representation
• Harvard Kennedy School

Guest Profile:

• Faculty Profile at the Harvard Kennedy School
• Profile on Wikipedia
• Profile on LinkedIn

Her Work:

• Personal Webpage
• Amazon Author Page
• Make Work Fair: Data-Driven Design for Real Results
• What Works: Gender Equality by Design