Andy Stern spent his career leading the Service Employees International Union, but he left in 2010, convinced that the nature of work was changing faster than unions, or anyone else, could handle. Now Stern is one of the most prominent advocates of a universal basic income — a kind of Social Security payment for everyone that could “ease the transition and provide a floor for people” whose jobs are automated out of existence (Vox).
With changes in transportation alone likely to eliminate millions of driving jobs in the near future, we’re going to need to do something. Stern emphasizes that the basic-income concept is not a pipe dream: “All the resources and assets are available to make it real. It’s just that we have a huge distribution problem.”
What are the obstacles? Getting a divided Congress to support anything this big will be tough, though the basic income finds supporters among conservative thinkers as well as progressives. Even tougher: overcoming our Protestant work-ethic resistance to giving people “something for nothing.”
Stern suggests we begin by admitting just how big this change is. Then we can start thinking about “work” much more broadly, to include all the important stuff we already do — like taking care of parents or kids, creating art, learning skills, or solving problems — but that doesn’t earn us a paycheck.
The Tech Roots of Trumpist Alienation
Trumpism has multiple causes, but one, surely, lies with the disruptive changes that new technologies are imposing on our economy. Those changes are happening first in urban centers, but resistance to them is also growing in rural communities that are Trump’s stronghold. While Silicon Valley insiders argue about billionaire Peter Thiel’s support for Trump, the larger story, writes Khan Shoieb in Backchannel, is that Trump’s voters think the American system is rigged against them, and a lot of them are going to be blaming tech for their woes.
Companies promoting new technologies and platforms will need to try to win back the trust of these distrustful fellow citizens, Shoieb writes: “A class of voters that is convinced that life will be worse for the next generation in America is not one that is inclined to accept with alacrity a tale about technology’s promise.” Two centuries ago, the original Luddites threw crowbars into the power looms that ate their jobs; in one unhappy scenario, Trump’s disaffected followers will update that model for our time.
Time to Put Email Out of Its Misery?
From the controversy over Hillary Clinton’s personal email server to the torrent of Democratic Party email messages stolen by Russian hackers and spilling out of Wikileaks, email has been at the center of many of the 2016 election’s longest-running dramas. Hoary old email is “as tempting as it is inescapable,” writes Farhad Manjoo in The New York Times. It is also insecure and inefficient — “simply not up to the rigors of modern political and business life,” Manjoo says. He argues that we should either fall back on phone calls and in-person meetings or move forward to Slack, Signal (messaging with encryption), and other modern collaboration tools.
OK, sure: For use within organizations, email is outdated. But it remains highly useful as a method for crossing organizational boundaries and reaching out to strangers. And every proposed replacement has its downsides. Maybe it’s time to “dance on email’s grave,” as Manjoo proposes. Or maybe we should realize that — to paraphrase Churchill’s famous quip about democracy — email is the worst form of communication, except for all the others that have been tried.
(If you are still using email to run your organization — and let’s face it, many still are — here’s some good advice from Fast Company on modeling better email behavior for your co-workers.)
How Ratings Smuggle Discrimination Into the Platform Economy
Customer ratings provide a critical feedback loop for platform owners: They are how Uber knows whether its drivers are performing up to snuff. Are they also a back-door for discrimination?
That’s the conclusion of a new paper from Data & Society, “Discriminating Tastes: Customer Ratings as Vehicles for Bias.” It’s illegal for companies to make employment decisions based on race or ethnicity. But what if the company essentially hands that employment decision over to a ratings system — and those ratings are provided by people who are biased, consciously or unconsciously?
That is happening right now with Uber, these scholars argue. The ratings system becomes an end-run around anti-discrimination protections. The paper’s authors propose some possible “interventions” to solve the problem they’ve identified: Collecting and publicizing baseline employment data in the gig economy. Validating customer-rating data against actual behavioral data (i.e., see whether complaints about a speeding driver are borne out by the vehicle’s instruments). Weighting ratings to account for the likelihood of bias. Demanding more detail from customers who provide negative ratings.
One thing is clear: It’s not just about Uber. Asking these questions now is a way of helping us create fairer, better businesses and platforms everywhere.
Never Mind the Millennials — Here’s the Perennials
The game of dividing humanity into generational tranches with catchy names is hardly new. Remember Hemingway’s “Lost Generation”? But marketers have tried to make it more and more of a science, dividing us into Boomers and GenXers and Millennials and pretending that the decade in which we were born predicts much of our behavior (and product preference).
Now, though, “the days of targeting media and products at people based on their age is over,” writes Gina Pell (NewCo Shift). Pell celebrates what she calls “Perennials: ever-blooming, relevant people of all ages who live in the present time, know what’s happening in the world, stay current with technology, and have friends of all ages.” Generational labels are handy for advertisers who want to reach specific populations, but for the rest of us, they’re more divisive than useful. Accumulating and analyzing lots of data doesn’t require us to see one another in the aggregate; we can also use that information to treat others as individuals rather than labels.
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