The term “woke” might be modern, but woke movements have been going on throughout history. And while an “awokening” is meant to further equality among systemically marginalized groups, they often can exacerbate existing social inequalities.

Musa al-Gharbi is a sociology and assistant professor of communication and journalism at Stony Brook University. His book, We Have Never Been Woke: The Cultural Contradictions of a New Elite, examines how professionals in the so-called symbolic capitalism space like media, nonprofits, and education have gained elite status through woke culture, and in turn, benefit from some of the inequalities they are morally aligned against.

Musa and Greg discuss the origins of woke movements throughout history including what factors in society can lead to “awokenings,” how symbolic capitalists have become the new elite, the role of cultural capital in today’s world, and why the elimination of DEI programs and pushback against woke culture can sometimes accelerate a new “awokening.”

*unSILOed Podcast is produced by University FM.*

Can we be committed to seeking social justice and elite status at the same time?

12:52: It’s our desire to be an elite that often ends up winning out and kind of transforming how we pursue these social justice goals, so that we mostly try to pursue them in ways that don't cost anything for us, risk anything for us, require us to change anything about our lifestyles and our aspirations, and the aspirations of our children, and all of that stuff. And so that mostly pushes us into pursuing these social justice goals in largely symbolic ways, on the one hand. And on the other hand, it often leads us to expropriate blame to other people, who often benefit far less from the system than we do, and exert a lot less influence over institutions and so on than we do.

Has diversity become a status symbol instead of a value?

46:01: Diversity is great as long as its fellow affluent, highly educated people. But God forbid, if they want to build affordable housing in your neighborhood, that's a hard no.

On competition over status

18:41: One of the things that's interesting about competitions over status and cultural capital and things like this is that status—one—it’s actually more of a zero-sum competition.

So, for wealth, it's possible for everyone in a society to have a decent amount of wealth or a high amount of wealth. But for status, that's not the case. A situation where everyone had a high amount of status—the same status—would be a situation where nobody had any status. Status is more zero-sum. You actually can't give more attention, more time, more deference, and whatever to one person without actually taking some from someone else, because our attention is finite, et cetera, et cetera. And so status is actually more of a zero-sum competition.

Recommended Resources:

• Pierre Bourdieu
• Why Has Critique Run out of Steam? From Matters of Fact to Matters of Concern by Bruno Latour
• Andrew Abbott
• Social Gospel movement..
• Secular Surge: A New Fault Line in American Politics

Guest Profile:

• Faculty Profile at Stony Brook University
• Professional Website
• Professional Profile on LinkedIn

His Work:

• We Have Never Been Woke: The Cultural Contradictions of a New Elite

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.*

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.

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

Are the algorithms that exist in our daily lives getting so smart that they know us better than our parents or our spouses? How do we balance the convenience and efficiency of this technology with privacy and consumer protections?

Sandra Matz is a professor at Columbia Business School and the director of the Center for Advanced Technology and Human Performance. Her book, Mindmasters: The Data-Driven Science of Predicting and Changing Human Behavior examines the link between algorithms and psychology.

Sandra and Greg chat about the bright and dark sides of psychological targeting, its applications in marketing, politics, and mental health, as well as the ethical considerations and future implications of using algorithms for personalized interactions.

*unSILOed Podcast is produced by University FM.*

Even the smartest algorithms slip up

24:01: When we talk about these algorithms, and I'm guilty of that myself, it always seems like, well, yeah, if they can predict your personality with high accuracy, that makes sense, but it still makes mistakes, right? So, accuracy is always captured at the average level. So, on average, we kind of get it right most of the time. But that still means that, at the individual level, we make a lot of mistakes. And those mistakes can be costly for the individual, right? 'Cause now you are seeing stuff that is completely irrelevant. Also costly for companies, 'cause now you are optimizing for something that's not actually true. So, I think if you can really think about application—I think the more you can turn this into a two-way street and conversation, the same way that this works in an offline world, right? If you kind of suddenly start talking to me about topics that I care nothing about, you're going to get that feedback, 'cause either I'm not going to see you again, or I just tell you we just talk about something else. And companies oftentimes don't get that because they don't allow users to interact with some of the predictions that they make. And I think it's a mistake, not just from an ethical point of view, but even from a kind of service, convenience, product point of view.

Are algorithms making us boring?

11:09: There's something nice about having these algorithms understand what we want, but I also do think that there's the risk of us just becoming really boring.

The trouble with signing away our data

49:29: The way that we typically sign away data is, we consent, but not because we understand it. And I think some of it is just that technology moves so fast that just keeping up with technology is almost impossible. So I think about this 24/7, and I have a hard time, and you also have to have this understanding of — not just in the here and now — like, a fully rational person would say, "Here's all the benefits, and here's all the downsides." And now I kind of make this rational decision that kind of maximizes utility. But we don't understand the downsides.

Recommended Resources:

• Big Five personality traits
• Cambridge Analytica

Guest Profile:

• Faculty Profile at Columbia Business School
• Professional Website
• Professional Profile on LinkedIn
• Center for Advanced Technology and Human Performance

Her Work:

• Mindmasters: The Data-Driven Science of Predicting and Changing Human Behavior