Artificial intelligence and robotics are among the leading technologies shaping deep changes in cities around the world. Ride hailing companies like Uber and Lyft are perfect examples of the way these technologies have already transformed the nature of mobility and automobile ownership. And these are but the tip of the iceberg in the way AI is transforming the city into a platform for new technology and innovation.
But AI itself comes in several dimensions: robotic devices, collaborative systems like crowd sourcing, the Internet of “things”, and learning algorithms, are each playing a major role in reshaping our view of the urban future. Let’s have a quick look at just a couple of these new “city views” to get a glimpse of the overall picture.
- Transportation—autonomous vehicles are the plat du jour of the urban environment at the moment. The idea of transport without traffic congestion and relief from human foibles in driving behavior generates a dream world of personal transportation. But there is a downside, too.
Driverless vehicles opens the possibility of unanticipated pedestrian deaths from machines run amok to job losses from automation. There is also the very open question of what happens to public transportation when/if driverless cars takes over urban transport? The opportunity is certainly there for real-time information and machine learning to turn public transport into a dream that eliminates many frustrations and frictions that public transport creates at present. But . . .
- Public Safety—Many types of AI techniques have been deployed already to combat things like credit-card fraud and other security issues like record keeping and CCTV videos that gather data for addressing various types of crimes.
But use of AI technology for surveillance and prediction of crime leads to a big question that cities must address: trust and elimination of discriminatory targeting. It can be argued that AI prediction tools could help remove human bias. But the other side of the coin is that the tools can also replicate the biases of the humans who actually create the tools. So the whole technology is a two-edged sword that must be used carefully to avoid creating the very problems it is supposed to solve.
There are many other issues such as health care an education where AI tools will lead to major changes. The key caveat is to make sure these tools guard against discriminatory behavior of the sort that tacitly assumes certain racial indicators or similar factors into the machine learning of these systems.