Prototyping Civic Digital Trusts

Prototyping a civic digital trust has been identified as an opportunity to explore how a potential smart city civic data trust could operationalized in Toronto. A limited and narrowly defined civic digital trust prototype offers a unique opportunity to dramatically reduce the potential risks, costs and time required to extract the key learnings that are critical for advancing the exploration of a civic digital trust in Toronto. Workshop participants developed the following considerations.

Selection Criteria

Key criteria for selecting a use case for prototyping should include the following considerations:

Have a strong use case that drives the prototyping exercise (North Star use case)
Generate compelling public value and wide public interest
Connect both public and private sector datasets
Look at using high quality data-sets to manage costs
Use less sensitive datasets to reduce the risk of experimentation

Participants recommended developing a use case that explores data that is collected in the following four different scenarios:

Data collected from public spaces
Data collected in private spaces
Data collected from semi-public spaces (hotel lobby or private parking lots)
Data collected from cell phones (which was identified as a unique enough data source that it needed to be considered separately)

Possible Prototypes

Deciding what use case should be tested first was a challenging exercise to consider. Some ideas to consider were:

Understand critical issues that underly the dataset needed for the use case before selecting it
Limit the number of datasets used but consider going deep into the planned and unplanned uses of the data

Resourcing

Key resources that would be needed to develop a prototype include:

Key stakeholders with a well defined use case that can be unlocked through existing datasets
High quality standardized data sets
Key public and private sector stakeholders
Key users of data set for defined use case
Independent expertise across multi-disciplinary sectors (legal, data protection, public interest, etc.)
Funding for supporting all aspects of prototype design, execution and evaluation

Evaluation

Establishing whether a prototyping exercise was successful could be established through the following key factors:

The achievement of key metrics and design principles as defined at the start of the prototyping process
Establishing whether the:
- Data users still want to continue to use the data
- Data subjects still want to have their data used
Establishing whether the use case and associated architecture can be expanded beyond the prototype geography
Understanding whether citizens understand the value that a trust is providing

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Last updated 6 years ago

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Selection Criteria

Key criteria for selecting a use case for prototyping should include the following considerations:

Have a strong use case that drives the prototyping exercise (North Star use case)

Generate compelling public value and wide public interest

Connect both public and private sector datasets

Look at using high quality data-sets to manage costs

Use less sensitive datasets to reduce the risk of experimentation

Participants recommended developing a use case that explores data that is collected in the following four different scenarios:

Data collected from public spaces

Data collected in private spaces

Data collected from semi-public spaces (hotel lobby or private parking lots)

Data collected from cell phones (which was identified as a unique enough data source that it needed to be considered separately)

Possible Prototypes

Deciding what use case should be tested first was a challenging exercise to consider. Some ideas to consider were:

Understand critical issues that underly the dataset needed for the use case before selecting it

Limit the number of datasets used but consider going deep into the planned and unplanned uses of the data

Develop a use case that can leverage existing projects that are capturing and analyzing high quality data sets. An example used in the mobility sector was around the use of data that was being collected for the .

Resourcing

Key resources that would be needed to develop a prototype include:

Key stakeholders with a well defined use case that can be unlocked through existing datasets

High quality standardized data sets

Key public and private sector stakeholders

Key users of data set for defined use case

Independent expertise across multi-disciplinary sectors (legal, data protection, public interest, etc.)

Funding for supporting all aspects of prototype design, execution and evaluation

Evaluation

Establishing whether a prototyping exercise was successful could be established through the following key factors:

The achievement of key metrics and design principles as defined at the start of the prototyping process

Establishing whether the:

Data users still want to continue to use the data
Data subjects still want to have their data used

Establishing whether the use case and associated architecture can be expanded beyond the prototype geography

Understanding whether citizens understand the value that a trust is providing