Data-Smart City Pod

AI is being deployed across courts, police departments, and corrections systems. Without the right guardrails, it could amplify existing biases. But, with care and attention, there are opportunities to improve the experience of people within these same systems.

Host Stephen Goldsmith speaks with Dr. Andrea Headley from Georgetown University's Evidence for Justice Lab about what governments need to know about AI in criminal justice, how to identify and reduce bias, why transparency matters for public trust, and the devastating consequences when humans aren't in the loop.

Guest:

Dr. Andrea Headley – Associate Professor, Georgetown University McCourt School of Public Policy; Director, Evidence for Justice Lab

References:

The Justice and Artificial Intelligence Tracker

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.

Lead pipes aren't just a water infrastructure problem—they're connected to poverty, violence, and lost opportunity. Milwaukee's mayor explains why removing them matters to public safety and economic mobility.

Host Stephen Goldsmith speaks with Milwaukee Mayor Cavalier Johnson about how his city is accelerating lead pipe removal, creating family-supporting jobs, and why environmental justice is central to breaking cycles of poverty and incarceration.

In this episode, you'll learn:

   Why lead exposure affects public safety, not just health

   How lead removal becomes an economic development opportunity for neighborhoods

   Why early interventions in kids' lives prevent long-term harm

   How Milwaukee prioritizes removal in the most under-resourced neighborhoods

   What federal funding will enable Milwaukee to remove 5,000 lead pipes in a single year

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.

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.

Why do cities struggle to adopt AI at scale despite exponential improvements in the technology? Host Stephen Goldsmith speaks with Boston CIO Santi Garces and Harvard Business School Professor Mitch Weiss to explore the "growing gap" between AI capability and organizational adoption. Plus, hear how the city of Boston improved user satisfaction 3x with an AI-powered web search, why MCP servers are powerful and transparent tools for government, and how to move from pilot to production.

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.

What does a city government owe its residents? Host Stephen Goldsmith speaks with Eyal Feder-Levy, CEO of Zencity, to explore how GenAI is fundamentally transforming the way cities measure, understand, and respond to resident needs.

For decades, performance management in government has relied on operational metrics like crime numbers, pothole repairs, traffic flow. But what happens when the data looks good, yet residents feel less safe? When efficiency improves, but trust declines?

In this episode, Feder-Levy argues that citizen satisfaction and perception should be the true North Star for city government. Using social sentiment analysis, AI-powered data agents, and real-world examples, he explores how GenAI is cutting response times, revealing hidden patterns, and closing the gap between statistics and lived experience.

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.