This class of people includes journalists, yes, but also people who work in the tech industry, academics, nonprofit leaders, influencers, and those who work in politics. From now on, I’ll refer to this group broadly as “the messenger class.”
The messenger class plays a fundamental role in any democracy. Democratic self-governance requires not just fair procedures for making decisions but an accurate and shared picture of social reality to reason about. That picture is revealed through the communicated experiences of citizens, filtered through the messenger class, which decides which experiences are urgent and require intervention.
But if our mediating institutions are all staffed by people drawn from the same narrow demographic band, then the picture they produce will be skewed in ways nobody intends and few notice. This isn’t about whether the messenger class is full of bad people — it’s largely not — it’s about whether it’s even possible to know when you’re acting as a mirror to society, or a spotlight on what you personally happen to care about. [...]
[ed. Examples: Gentrification; Opioid Epidemic; AI Job Displacement; Rising Unionization:]
The psychology of projection
There is a name for what’s happening here. Psychologists call it the false consensus effect — the tendency to overestimate how much others share your beliefs, behaviors, and experiences.
First established by Ross, Greene, and House in 1977, it has been confirmed in a meta-analysis of 115 hypothesis tests and found to be a robust, moderate-sized effect. Later research shows that it persists even when people are warned about it.
Neuroimaging research has shown that projecting your views onto others activates the brain’s reward centers; it feels good to believe everyone is like you. And a 2021 study found that social media use amplifies the effect: The more time you spend in an environment where your views are echoed back to you, the more convinced you become that those views are universal.
The false consensus effect is usually studied at the individual level. But what I’m describing is a class-wide and industry-wide version.
It’s not just that any one journalist overestimates how representative her experience is; it’s that an entire class of professionals shares a similar set of experiences, confirms those experiences with each other on the same platforms, and then produces a body of public knowledge that reflects those experiences as though they were the norm.
And even when people from nontraditional backgrounds join the fray, they are incentivized to conform through social media, company cohesion, editorial norms, and the normal human urge to get along with your peers and be taken seriously by the people you respect.
So many problems, so little time
Agenda-setting is zero-sum.
There’s only so much time elected officials, charities, nonprofits, or businesses have to respond to the public’s needs. So if something is getting more coverage than may be warranted, that means other things are getting less. And that means fewer solutions are being explored.
Remember that gentrification report? It found that 15% of urban neighborhoods showed signs of gentrification over 50 years, while 26% experienced substantial population decline.
The far more common trajectory for a poor urban neighborhood is not invasion by white yuppies — it’s continued segregation, disinvestment, and deteriorating housing stock. But that story doesn’t get told with anywhere near the same intensity.
The same asymmetry shows up in the AI conversation. The workers most likely to struggle if displaced by AI are not the ones getting the most ink.
A Brookings analysis found that roughly 6.1 million workers face both high AI exposure and low adaptive capacity — limited savings, advanced age, narrow skill sets, scarce local opportunities. Eighty-six percent of these workers are women, and they’re concentrated in clerical and administrative roles in smaller metro areas.
I’m not arguing that journalists are dishonest, that scholars are corrupt, or that the messenger class is engaged in some conspiracy to distort public reality. The people I’m describing are, by and large, doing their best to tell the truth about the world.
The problem is that they’re drawing on their own experiences, their own social networks, and their own platform ecosystems as raw material — and those inputs are unrepresentative in ways they have no easy mechanism for detecting.
Part of this could be resolved with an increased fluency with quantitative data. But that’s not actually enough. Many stories — like the opioid epidemic — are ones that require journalists to respond to anecdotes before the quantitative data has been assembled, analyzed, and produced by the academy.
by Jerusalem Demsas, The Argument | Read more:
Image: Klaus Vedfelt via Getty Images
