On the Road to Responsible AI
AI has come a long way in the past few years, and agencies have learned a lot about best practices. The good news is there’s a growing body of guidance.
AI has come a long way in the past few years, and agencies have learned a lot about best practices. The good news is there’s a growing body of guidance.
Processing data locally speeds up the time for decision making. Edge data can better support mission context and relevance. But distributed data brings with it new challenges. Here’s how to meet them.
It can be hard for state and local governments to get started with AI. Here are some questions to ask and steps to take to begin.
Whether you’re planning to use more AI or just want to improve analytics and tighten cybersecurity, good data management must be the foundation for your efforts.
You can’t apply AI to your data until you’ve taken care of some fundaments of data governance and hygiene. Here are some tips on how to get ready.
To make evidence-based policy, takes more than information–it requires the ability to turn information into knowledge and to base decisions on it.
When Pittsburg failed to get certification from Bloomberg Philanthropies’ What Works Cities Certification Program, the city dusted itself off and worked hard to change its entire data mindset. Here’s how they did it.
Agencies are launching new and innovative data initiatives, but many of their employees are being left behind. Creating a culture of analytics can bring everyone into the fold.
Government agencies have a responsibility to protect our personally identifiable information, but that’s easier said than done. Strong data management and cultural mindsets are important, and there’s a role for automation as well.
“Adaptive data governance shifts the onus from IT to a place of collaboration with business users, to enable people regardless of technical background to communicate around data to achieve outcomes with data.”