Companies like Google, Uber, or Facebook aren’t built to fix society. That includes cities.
The smart-city concept was born of the last recession, when the IT behemoths that dominated generations past, like IBM and Cisco, rushed into budget-crunched city halls, software in hand, pitching harried administrators ways to run electricity, water, and transportation systems faster, cheaper, and with data-driven “insights.” Now, thanks to the ubiquity of mobile devices, many of the smart-city solutions proffered on the expo circuit are more familiar to regular consumers than you might realize.
Look no further than Uber and Lyft, which have promised to un-clog congested roads and bring down carbon emissions through shared, on-demand rides. In reality, they appear to have grown demand for vehicle travel, and as such, a wealth of new research—a wave of it this year—implicates them in the thickening traffic and rising emissions that cities are lately experiencing. Ride-hailing vehicles aren’t the primary source of those problems; private passenger vehicles are. But Uber and Lyft’s model is built on their artificially low prices, subsidized by gobs of venture capital. So far, despite all those shared rides on Uber Pool and Lyft Line and many pilot “partnerships” with public transit agencies, that model has proven incompatible with the cleaner, faster transportation networks everybody wants. (By the way, there is an incredible transportation technology that would probably get us closer. It has four wheels, three letters, and it rhymes with fuss.)
To take another transportation example, look at self-driving cars, which come with twin promises from the auto and tech industries to save lives by eliminating imperfect and inattentive human drivers. Sorry to hammer on Uber, but this year, one of their self-driving Volvos struck and killed a pedestrian on a brightly lit road in Tempe, Arizona. In addition to relying on problematic software, Uber had ditched safer testing practices for its robotic cars by having their human backup drivers work solo. In essence, that made their workers all the more likely to fall victim to the same fatal distractions that cause regular crashes every year .... That episode also raised the potential for bias in the algorithms operating the vehicle. If the car couldn’t see a woman and her bicycle on a well-lit, wide roadway, what was it supposed to see?