The Promise and The Threat
Fresh off its selection as the Intelligent Community Forum’s 2014 Intelligent Community of the Year, Toronto, Ontario’s leadership began discussions with Google (Alphabet) spin off Sidewalk Labs to transform its waterfront. Their vision was to integrate ubiquitous sensors and data analysis into the architecture of a new urban district that would enhance the quality of life for its inhabitants. By October 2017, the preliminary deal was signed and planning began.
Almost immediately, various groups objected to the massive collection of data that would be required to operate “Sidewalk Toronto”. By 2019, the Canadian Civil Liberties Association had sued governments at all levels over the project, and Sidewalk Labs’ CEO Dan Doctoroff was making frequent appearances to assure the public that privacy would be a paramount consideration. He proposed an independent “Urban Data Trust” to oversee the implementation of guidelines for “privacy by design”. On May 7, 2020, just two weeks short of a development board vote on continuation, Sidewalk Labs withdrew its plans, citing “unprecedented economic uncertainty”.
How did the Sidewalk Toronto vision glide so easily off the rails? As with a host of new business concepts, from Uber to Airbnb, technology has generally outpaced government’s ability to integrate or regulate it. In this case, the primary issue echoes the “liberty vs. security” debate that has emerged in the wake of government’s response to the pandemic.
Fundamental to the smart cities concept is using “big data” technology to understand in depth how our communities work, and leveraging that understanding to drive planning, decision-making and interactions with citizens. Broad-based, intense number-crunching sits at the center of our ability to do this. Yet it has only been in the last decade that the confluence of supercomputer-level processing power, specialized software and pervasive data collection has enabled data analysis on a scale previously reserved for science fiction stories.
The benefits of data analytics are clear – traffic patterns can be adjusted in real-time to speed commuters on their way; health care resources can be allocated to focus on high impact initiatives; law enforcement can be deployed to reduce or respond more effectively to crime. The trade off, however, is that each of these applications vacuums up information that has historically been considered personal or private, from smartphones, vehicles, video and acoustic surveillance systems, health care data bases, “smart light poles”, etc. The automatic toll collection system that lets you speed down the interstate also takes your picture in case you don’t have a transponder or the one you have doesn’t register. Your phone’s location is continuously captured in case the police want to know where you are or where you’ve been.
So what can smart government leaders do to find the right balance?
- First, know what limits are placed by federal and state laws on your ability to collect or use potentially protected data. These will vary by jurisdiction and will cover issues such as health record privacy and unauthorized video or audio recording.
- Second, regularly tap into your constituents’ feelings on the topic, and consider setting up a standing privacy advisory group of businesses and individual citizens to test new concepts and keep you well within the bounds of community tolerance.
- Third, set written guidelines for your organization that codify what is expected of government employees as they act to improve services or enforce the law without violating individual privacy protections.
- Finally, with every proposed new capability that employs data analytics, require that developers find the least intrusive means of accomplishing what they seek to do. Avoid mega-solutions that sound utopian in theory but are likely to provoke the same response that Sidewalk Labs encountered in its effort to build a new citizen-friendly cityscape.
Coming soon… Part Two looks at Cyber-defense: housekeeping and more