2019-03-31 —

Di-Ann Eisnor, the former Google executive who helped grow Waze into a traffic-data juggernaut with 90 million monthly users, will lead the recently rebranded We Company's efforts to build data-driven products and partnerships with cities and community groups, aimed at tackling barriers to jobs, housing, education, and other problems related to urbanization.


But the initiative is likely to include a survey of the firm's many data sources to see how they might be applied in the service of entire communities. Quartz reported that Eisnor is charged with taking "what We has already done inside the building, take it outside, and reimagine a sort of connective tissue for 21st-century cities."


the We Company isn't a novice in the urban data department, so it may be useful to look at what it has tinkered with in the past. At one point, the company experimented with applying machine learning to information about neighborhood attributes and demographics, in order to inform its real-estate leasing decisions. Such tools could be useful to city builders in the public and private sectors. But they may also raise some eyebrows.


What Anderson described, in other words, was a kind of automated taste-tracker for a specific set of affluent people. A city planner or housing developer could find such a tool handy, especially if it was more up-to-the-minute and detailed than, say, the census and business registration data that tends to show how wealth and amenities spread around cities. An automated neighborhood-profiling widget could very easily go awry, however, if it isn't attuned to who and what is being screened out. Imagine, for example, a local government basing planning and investment decisions on the ratio of check-cashing places to upscale cashless coffee shops in a certain neighborhood. One could see this particular "smart city" innovation leading to algorithmic redlining.

The We Company is no longer actively pursuing these types of applications for machine learning. According to a spokesperson, it still pays WalkScore for various data-points about the surroundings of its potential locations, including proximity to transit. The company has largely refocused its data-driven efforts toward assessing the attributes that give office interiors good "vibes"--think algorithmically optimized arrangements of desks, chairs, and lighting, and the like. And it hopes to distance itself from the "smart city" label, which has become fraught with suspicion as other tech companies have attempted to turn urban data into dollars (see: Sidewalk Labs' efforts to develop a Toronto neighborhood "from the internet up.")

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