“Quayside,” a 12-acre slice of Toronto waterfront in line to be developed by Sidewalk Labs, the urban-tech-focused subsidiary of Google’s parent company Alphabet. Launched in 2015 by its CEO, Dan Doctoroff, and a number of other Michael Bloomberg affiliates, Sidewalk Labs makes much of its urbanist bona fides. The company is now primarily focused on turning the patch of Toronto-owned land into what it calls the “world’s first neighborhood built from the internet up.”
Quayside would test a novel “outcome-based” zoning code focused on limiting things like pollution and noise rather than specific land uses. If it doesn’t bother the neighbors, one might operate a whiskey distillery in the middle of an apartment complex.
a data-harvesting, wifi-beaming “digital layer” that would underpin each proposed facet of Quayside life. According to Sidewalk Labs, this would provide “a single unified source of information about what is going on” to an astonishing level of detail, as well as a centralized platform for efficiently managing it all.
Cavoukian was an adviser on the Quayside project, but she resigned after Waterfront Toronto and Sidewalk refused to unilaterally ban participating companies from collecting non-anonymous user data.
Nearly every city-fixing proposal from Sidewalk Labs combines civil engineering with some element of data collection—what the vision document calls “ubiquitous sensing.” Quayside reduces carbon not just via a thermal grid, but by embedding each home and office with Alphabet’s Nest smart thermostats, which use “occupancy sensors” and predictive modeling to autonomously adjust temperatures throughout the day.
The city is literally built to collect data about its residents and visitors, which Cavoukian was clear-eyed about when she signed on to be an adviser. She’s worried about Sidewalk using all these cameras and sensors to track people on an individual level, to create real-life versions of the personal profiles Google already uses to track people online. Without anonymization, she said, a single person’s activities could be connected across multiple sources and varying databases to track his movements over the course of the day.
“I think it’s important to note that this project seeks to accomplish many things,” he said,“including delivering large amounts of affordable housing, a highly sustainable neighborhood, and economic activity and new jobs. All of that needs to happen along with policies that protect the public interest, including with regard to data. But, data is just one piece of this conversation.”
Quayside may very well accomplish these things, remaking the city as we know it and setting precedent for future projects like it. But the controversy has shown that it may need to reimagine not just traffic patterns and thermostats, but a set of rules for data, privacy, and corporate “innovation” in a context that has never existed anywhere else on Earth. Thus far, at least, that’s proved the most difficult project to pull off yet.
Ten years ago, Facebook already had 15 billion photos in its database. As you uploaded pictures and tagged friends and added date and location data, the software got really, really good at recognizing people’s faces. This facial-recognition capability is mirrored at other companies—and some, such as Amazon, sell it to whoever wants it.
There isn’t some global corporate conspiracy to get you to post a photo of yourself from the old days and today. There has been a global corporate conspiracy to get you to post everything about yourself, continuously, for the past 15 years. Which many of us have done, providing the vast data sets that companies have already trained their neural networks with. If you think that not posting these two photos does anything to surveillance capitalism or the platforms that succeed through it, that’s just not right.
Vivimos rodeados de sistemas de toma de decisiones automáticos basados en datos, que usan algoritmos que no podemos verificar. Esto permite que se lleven a cabo operaciones engañosas, como sucedió en el caso de Volkswagen, en el que los algoritmos alteraban la medición de emisiones en los coches. Es necesario impulsar un nuevo modelo basado en dos principios clave: la transparencia algorítmica en los mecanismos de toma de decisiones y la rendición de cuentas algorítmica, que permite apelar una decisión tomada por un sistema automático.