ABOUT THIS EPISODE
Most cities respond to infrastructure problems after residents report them. What if they could detect and prevent them first, while serving every neighborhood fairly?
Host Stephen Goldsmith sits down with Daniel Pelaez (CEO of CYVL), Khahlil Louisy (Public Innovation Institute), and Mike Dennehy (former Boston Public Works Commissioner) to explore how artificial intelligence and computer vision are revolutionizing infrastructure management, closing equity gaps, and helping cities shift from reactive operations to predictive maintenance.
In this episode, you'll learn:
- How computer vision detects infrastructure problems before citizens report them
- Why traditional complaint-based systems can miss concerns in lower-income neighborhoods
- How natural language queries democratize access to infrastructure data for city managers
- Why a "multi-modal" approach combining AI, citizen input, and external data delivers better equity outcomes
- What cities can expect from predictive infrastructure systems
Paper referenced: When Residents and Algorithms See Different Problems
Listener Survey: bit.ly/datasmartpod
Music credit: Summer-Man by Ketsa
About Data-Smart City Solutions
Data-Smart City Solutions, housed at the Bloomberg Center for Cities at Harvard University, is working to catalyze the adoption of data projects on the local government level by serving as a central resource for cities interested in this emerging field. We highlight best practices, top innovators, and promising case studies while also connecting leading industry, academic, and government officials. Our research focus is the intersection of government and data, ranging from open data and predictive analytics to civic engagement technology. We seek to promote the combination of integrated, cross-agency data with community data to better discover and preemptively address civic problems. To learn more visit us online and follow us on LinkedIn.
