Three Ways AI Helps City Add Human Touch to Service Delivery

As cities explore ways to adopt artificial intelligence in more advanced ways, some of the most promising uses aren't about public tools like chatbots. Instead, the real breakthrough is finding a new way to use AI to handle some of the most time-consuming tasks of defeating government workers. That is one of the focuses of Professor Daniel Ho's work at Stanford. He is affiliated with cities like San Francisco Offload both internal tasks to AI and For example, we will completely reduce the number of these tasks by identifying unnecessary reporting rules built into city regulations. the goal? They spend more time on some of the work that really helps the residents by releasing staff.

City has been struggling with rules and red tapes for a long time, slowing things down. However, AI may ultimately provide a way.

“Many rules are written with good intentions, but they can't solve problems and create a deficit,” explains Carrie Bishop, who works on the government innovation team at Bloomberg's charity and previously served as San Francisco's first Chief Digital Services Officer. “Reevaluating the entire city code is an overwhelming task of no one having any time or desire to do. With the help of AI, there is a small subset of rules so that mechanical work can be done and efforts to make human decisions can be started.”

The next three lessons from Ho's work provide practical guidance for local leaders interested in realizing this possibility.

Insert AI at key moments to unlock the action.

Most urban leaders already know about the more common applications of AI. However, Ho argues that being more ambitious about this technology doesn't necessarily mean, as he explains, developing new “end-to-end solutions.” In fact, local leaders can have a huge impact when inserting technology in a strategic way at key points in complex workflows. And it can create space for civil servants to provide what people need.

For example, Ho and his team as part of a partnership with Santa Clara County in California. Stanford Regrav I adapted a big language Model This allows for immediate analysis of millions of records. the purpose? Identify property certificates containing discriminatory language intended to limit who could purchase a particular home several decades ago. Doing so manually can consume nearly a decade of staff time. The new tool performed initial analysis with near perfect accuracy in just a few days. And while the work still calls for human reviews, the lesson from HO is that this kind of approach helps ensure that civil servants are less consumed by the bureaucracy and are not available for frontline service delivery.

“If you're spending time on these internal tasks, there are other services that you'll inevitably need to hit,” explains Ho. Instead, tools like these will help ensure local leaders have the ability to maintain proper staffing at frontline service counters that provide everything from birth certificates to public interest.

Create space for human discretion.

Please note that HO relies on AI to independently manage the main responsibility of public interest, which is part of the local government's most sensitive responsibility. But even in these complex areas, he argues, AI can play a valuable support role by streamlining delivery workflows and releasing civil servants and focusing on the human side of service.

“If you can build a system that can easily identify information in a resident case file related to eligibility for a particular form of assistance, you will not only improve delivery. “You will also release what may be the most important part of the actual resident interaction.”

Take the benefits of housing, for example. When staff spend less time processing documents, they listen, resolve issues and meet the wider needs of residents.

“It's still important that we have to have people who can actually help explain and engage citizens who are interacting with these systems,” says Ho.

Trigger conversations about how to improve your policy.

Ho's work doesn't just show how cities can free their teams from Red-Tape tasks. It also shows how cities can reach the roots of these problems. This is to improve overly complex policies and programs over the long term.

As part of his team collaboration Focusing on the San Francisco Local Government Code, the city uses a search system developed by Reglab to identify all cases where the law requires agency staff to produce potentially time-consuming reports. Some of the findings are comical when there is no risk of using valuable abilities, such as rules for regular updates to urban newspaper racks that no longer exist.

The ultimate goal is strategic. It is to assert laws that eliminate unnecessary requirements. The value of AI in this example serves as a tool to launch the necessary conversations between city leaders, civil servants and local councillors and change how cities work.

“Emphasing rules that are not meaningful or contradictory should assess policymakers' policy intentions, question whether the rules meet the objectives, and discuss changes with appropriate stakeholders,” explains the bishop of Bloomberg's charity.

Ho said the next step in this work might involve using AI to create a kind of scorecard to assess how much administrative effort is actually needed to implement the new law. Another possible use case: Map all fees collected by the city to determine whether it costs more to collect than it generates revenue.

Of course, local leaders continue to develop AI solutions that provide concrete improvements in people's lives in themselves. However, as Ho highlights and the example above shows, some of the most transformative uses of AI may be invisible. These quiet interventions can rebuild the systems behind service delivery, free up time, adjust policies with values, and improve how government works at its core.