What if an All-Knowing Algorithm Ran Traffic and Transit? – Slate Magazine

A journalist who reports on cities and autonomous vehicles responds to Linda Nagata’s “Ride.”
I like to think of myself as deeply skeptical of the many internet algorithms telling me what I want and need. I turn off targeted advertising wherever I can. I use AdBlock to hide what’s left. Most of my YouTube recommendations are for concerts or sports highlights, but I know I’m just a few clicks away from a wild-eyed influencer telling me to gargle turpentine for a sore throat. Twitter trending topics? I regret clicking immediately.
But I make an exception for the sweet, all-knowing embrace of the Spotify algorithm, to whom I surrender my ears several times a day. This software doesn’t just know my taste in music better than my friends; it acts on it, with chains of songs that build off things that I know I like, or forgot I did.
Being steered by an algorithm is the subject of Linda Nagata’s “Ride.” In this case, I mean it literally: Nagata’s story is set in a neighborhood of Honolulu, Waikīkī, in a not-so-distant future where a self-driving transit system takes residents from place to place. Whom you ride with, how long you wait, and how you get where you’d like to go are all in the hands of an opaque computer program. So when Jasmine, a disenchanted hotel receptionist, gets a tip about an “Easter egg” in the transit app code, the possibility of switching things up makes her flutter with excitement.
Most of us probably experience this kind of algorithmic control only in fits and spurts, in the finely tuned interface of a dating app or a playlist, but Nagata’s characters live under the algorithm. It is the only game in town when they need to get from A to B, and as the guardian of the public right-of-way, the code’s power extends all the way to the type of people who get to meet one another. Riders are sorted by social rating. Because the cars are autonomous, there is no driver to appeal to when something goes wrong.
Nagata’s vision of fleet transportation is not so different from the way that Silicon Valley CEOs like Tesla’s Elon Musk or Lyft’s John Zimmer have imagined the urban future. In this scenario, electric, autonomous vehicles expand mostly under a shared model, so cheap and convenient to use that they eat into our purchases of private cars. All that ride sharing—as in, literally sharing cars with fellow travelers—theoretically produces more mobility without adding more traffic.
Historically, transit monopolies have been some of the most reviled actors in urban politics, from early streetcar franchises to taxi cartels. Cab drivers’ avoidance of Black customers and neighborhoods was one reason Uber was able to summon the political capital to disrupt the system that prevailed in big cities at the start of the 21st century. Today, big-city residents who don’t drive often have a choice between at least two venture-funded taxi services, in addition to a publicly run transit system.
I don’t mean to present the status quo as some kind of utopian panoply of movement options. Uber and Lyft, the country’s two largest taxi companies, are neither cheap for consumers, nor remunerative for drivers, nor profitable for shareholders. Public transit service remains woefully poor almost everywhere, an option of last resort. Leasing and driving one’s own car, while it’s clearly the most dependable option to get from A to B, imposes an inescapable financial penalty on the working poor. Also, it’s terrible for the environment.
From this angle, there is something awfully appealing about the seamless Transit AI system that ferries Jasmine from home to work and back again. Its cost is never mentioned; its efficiency is legendary (even if, under duress, the Transit AI’s emergency functions need a little work). Unthinking convenience, to the extent that Nagata’s protagonist wishes to mess it up and bring a little excitement into her routine.
Under the hood, though, Waikīkī’s ubiquitous utopian system replicates, under the guise of “social rating,” many of the socioeconomic inequities built into our own system. (Waikīkī also has “luxury services” and personal cars, by the way.) This is not such a far-fetched idea; service ratings do determine who gets access to ride-hail services. For workers, all-important ratings can be the difference between a plum job and no work at all.
But perhaps it’s best to see the social rating system in Waikīkī’s Transit AI as a metaphor for the kind of sorting that is so easily accomplished in the many algorithmically governed spaces that, increasingly, make up the setting of our lives. The internet is a place of destabilizing context collapse, where we can brush shoulders in the mentions with famous authors and trash-talking basketball players or vote on whether the world’s richest man should sell $20 billion in stock. It sometimes feels more like a subway car, in other words, than the stratified boarding zones of an international flight.
But online, behind that apparent free-for-all lies code that, as often as not, is trying to sort like with like, feed me news that suits my biases, and play me musicians whose songs I know, and sell me boots like the ones every other 31-year-old man wants to buy. “I’m ready for a change,” Jasmine says. The software has just the thing for her.
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