Digital Services to APIs to ML

Hanging out with Brandon Williams and Lilo Santos on the Public Sector Community’s Coffee

#CivicTech is having a moment. As government gets better at delivering digital services to residents, they face challenges ahead from building APIs to wrestling with machine learning.

Digital Services

The delivery of digital services across government is maturing.  In some Federal and State Agencies as well as local governments, you will see teams working in an agile way, using human-centered design to better understand the problems they are solving and shipping software that works fine on mobile devices. Organizations like 18F, United States Digital Service, Code for America and many more are driving the conversion on building good government using technology. There is still much work to be done for government to evolve into high-performing software delivery teams and to use agile budgeting to better align team funding with how teams want to work, strengthening product management across all layers of government, modernizing legacy systems and so much more.

Open Data and APIs

We have also seen the rise of open data published by cities, counties, and state and federal agencies. This drives innovation and transparency, keys to good government.  Open datasets are typically aggregated data that do not include personal information.

Next, we will see governments making APIs available so residents, citizens, patients, taxpayers, etc can have an option to easy share the information their government holds with the apps, services, businesses and other government agencies they choose.

For example, a Medicaid patient can share their insurance claims history with a home-delivery prescription drug service or a parent can share their children’s immunization history with a summer camp that requires this information to register a child.

Offering these types of APIs will be a big challenge for most governments as they have never built information services that software developers are consuming to then provide a service to the constituent. With open data, governments mostly took a “build it and they will come” approach in which they simply made the data available and let the market figure out what to do with it. However, when building APIs on top of protected, personal information, governments will need to play an important role in helping developers successful adopt the APIs.

With API delivery, value is only realized when the developer is able to leverage the API to deliver something meaningful to the constituent.

In parallel with delivering APIs for 3rd party software developers to consume, governments will also get more sophisticated on how to exchange data within government itself. Should a State Medicaid agency be able to exchange personal health information about a patient with the State Human Services agency that offers mental health benefits that patient could leverage? Where do you draw the line and when must the constituent give their consent? These are big challenges and need people working for government that understand both the technology and business of APIs as well as policy related to data sharing.

Machine Learning

Fraud detection, robotic process automation, public health outbreak prediction and so much more is coming to government. Understanding how to manage ML pipelines, train models, and incorporate all of the ethical considerations that this technology will raise for a government into the product management process will be critical.

Books like Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor by Virginia Eubanks demonstrates how bias is introduced into systems and is super relevant for government leaders working on or thinking about building prediction or filtering systems.

Whereas vendors can offer off-the-shelf solutions like chatbots that introduce some AI into government systems without the government agency teams needing any understanding of the underlying technology, the government teams building enterprise applications will need ML engineers and experienced product managers. Government will once again need to compete with the incredible demand for this type of technologist as well as the high salaries their skills command.

If government teams still struggle with web apps, mobile experiences and stability of systems, how will they possible handle the sophistication of ML pipelines?

In government, the ROI is social impact. Residents leveraging easy-to-use digital services to pay their taxes, register a car or access a benefit helps them focus on what matters, their daily life. APIs and machine learning enable these digital services to get that much better.

We are living in an era where trust in government is low and the demand for competency and transparency in government is high. As government continues to deliver for the people, this trust improves over the long-run. We must keep working, all of us.

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