You want to hear a secret? City governments are still at the beginning stages of understanding how best to optimize the use of machine learning (ML) algorithms to make city services more efficient. Here’s why.
Algorithms are made up of someone’s opinion and experience. Therefore, we should ensure that the opinion and experience of people who will be impacted by the algorithm is captured in the design of the algorithm such that we can remove as much bias as humanly possible.
Getting feedback directly from your customers through a focus group could be highly beneficial to the success of your project.
We need more than just data scientists doing city work; we need a strong ecosystem that will support a smart team of urban data scientists as they assist city leadership in driving successful outcomes for citizens.
From renewing a driver’s license to filing federal tax returns, there is a full spectrum of services citizens expect from government. It’s up to agencies to figure out how to ensure that each touchpoint meets growing citizen expectations and aligns with their organizational missions.
Web-based GIS tools are already in use by governments and they have already proved their worth by providing citizens increased transparency and accountability. Now is the time to apply the same patterns and practices to redistricting, ultimately rebuilding trust in the process.
Geography is destiny.” It is a phrase that owes its origin to early theories of geopolitics. It’s originally credited to Napoleon, prior to his army invading Russia. Here’s what it has to do with redistricting.
The root of this angst, or mistrust about redistricting, has to do with a lack of trust. Like laws and sausage, we never really see it get made.
With so many channels of engagement available, agencies have struggled to develop best practices and standard processes. One tool that is proving popular, however, is text messaging campaigns that reach a broader audience.
There is quite a bit of writing these days about how data is the catalyst to government innovation. Over the last several years, the conversation has turned towards how the innovation needed to face the 21st-century challenges requires us to get these data conversations right.
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