Sector: urban planning

Article by Samuel Greengard: “As concerns over artificial intelligence (AI) grow and angst about its potential impact increase, political leaders and government agencies are taking notice. In November, U.S. president Joe Biden issued an executive order designed to build guardrails around the technology. Meanwhile, the European Union (EU) is currently developing a legal framework around responsible AI.

Yet, what is often overlooked about artificial intelligence is that it’s more likely to impact people on a local level. AI touches housing, transportation, healthcare, policing and numerous other areas relating to business and daily life. It increasingly affects citizens, government employees, and businesses in both obvious and unintended ways.

One city attempting to position itself at the vanguard of AI is New York. In October 2023, New York City announced a blueprint for developing, managing, and using the technology responsibly. The New York City Artificial Intelligence Action Plan—the first of its kind in the U.S.—is designed to help officials and the public navigate the AI space.

“It’s a fairly comprehensive plan that addresses both the use of AI within city government and the responsible use of the technology,” says Clifford S. Stein, Wai T. Chang Professor of Industrial Engineering and Operations Research and Interim Director of the Data Science Institute at Columbia University.

Adds Stefaan Verhulst, co-founder and chief research and development officer at The GovLab and Senior Fellow at the Center for Democracy and Technology (CDT), “AI localism focuses on the idea that cities are where most of the action is in regard to AI.”…(More)”.

Article by Santiago Garces and Stephen Goldsmith: “…we see the possible advances of generative AI as having the most potential. For example, Boston asked OpenAI to “suggest interesting analyses” after we uploaded 311 data. In response, it suggested two things: time series analysis by case time, and a comparative analysis by neighborhood. This meant that city officials spent less time navigating the mechanics of computing an analysis, and had more time to dive into the patterns of discrepancy in service. The tools make graphs, maps, and other visualizations with a simple prompt. With lower barriers to analyze data, our city officials can formulate more hypotheses and challenge assumptions, resulting in better decisions.

Not all city officials have the engineering and web development experience needed to run these tests and code. But this experiment shows that other city employees, without any STEM background, could, with just a bit of training, utilize these generative AI tools to supplement their work.

To make this possible, more authority would need to be granted to frontline workers who too often have their hands tied with red tape. Therefore, we encourage government leaders to allow workers more discretion to solve problems, identify risks, and check data. This is not inconsistent with accountability; rather, supervisors can utilize these same generative AI tools, to identify patterns or outliers—say, where race is inappropriately playing a part in decision-making, or where program effectiveness drops off (and why). These new tools will more quickly provide an indication as to which interventions are making a difference, or precisely where a historic barrier is continuing to harm an already marginalized community.  

Civic groups will be able to hold government accountable in new ways, too. This is where the linguistic power of large language models really shines: Public employees and community leaders alike can request that tools create visual process maps, build checklists based on a description of a project, or monitor progress compliance. Imagine if people who have a deep understanding of a city—its operations, neighborhoods, history, and hopes for the future—can work toward shared goals, equipped with the most powerful tools of the digital age. Gatekeepers of formerly mysterious processes will lose their stranglehold, and expediters versed in state and local ordinances, codes, and standards, will no longer be necessary to maneuver around things like zoning or permitting processes. 

Numerous challenges would remain. Public workforces would still need better data analysis skills in order to verify whether a tool is following the right steps and producing correct information. City and state officials would need technology partners in the private sector to develop and refine the necessary tools, and these relationships raise challenging questions about privacy, security, and algorithmic bias…(More)”