City-University Collaboration in Community Oriented Data Re-Use and Responsible Civic Artificial Intelligence with Andrew Young (GovLab)
This blog post is part of our ‘Data, Privacy, and the Future of Trust in Public Institutions’ series, which is penned by Garrett Morrow, who was MetroLab’s Experiential Research Fellow during Fall 2020. To learn more about this series and to access other posts in this series, check out our first post here.
I interviewed Andrew Young of GovLab on January 7, 2021 about the group’s recent work, including their AI Localism project. Andrew started a master’s degree in media culture at NYU (where the GovLab is housed) in 2011 with a focus on digital politics and related issues. This was during President Obama’s second term campaign run so there was a focus on political data collection and micro-targeting advertising for voters. However, instead of getting into presidential politics, Andrew’s focus was on Congress and using tracking technologies on congressional campaign websites. As part of his work at NYU, his thesis advisor was Stefaan Verhulst who Andrew also worked with at the Markle Foundation, with a focus on economic security. After Andrew finished his master’s program, Stefaan and Beth Simone Noveck were establishing GovLab, and Andrew joined soon after as one of the group’s first employees.
Andrew told me that the overarching goal of GovLab is to improve people’s lives and change the way that we govern using technology. One of GovLab’s first foci was establishing and defining how to define governance innovation. To this end, the group created research networks and connected with groups like the MacArthur Foundation to look at open governance and convene experts from many different disciplines. The GovLab was really focused on moving beyond citizen complaint or petition platforms like 311 to a larger, more holistic view of technological governance including open data initiatives and creating public value out of data that is typically siloed and distributed throughout numerous municipal institutions.
A recent outgrowth of the GovLab’s work around public data is their AI Localism project that launched in Fall 2020. Stefaan and Mona Sloane, also of NYU and the GovLab, published a short commentary that framed the issues of governance via artificial intelligence with a focus on local, city AI programs because that level of government is where most of the policy implementation will have a direct effect on residents’ lives. The project is currently trying to figure out the state of AI governance and is building a collaborative repository of local AI projects. By creating this repository, the GovLab can help better define and understand an area of governance that is still not very understood. To that end, the group is collecting examples of AI governance and how they frame the supposed benefits to the public good. In other words, what are the value propositions of these AI programs? The goal of the AI Localism project is to support more systematic, sustainable, and responsible efforts of studying the governance programs, but also develop and implement them.
Some of the questions posed by AI Localism align closely with my own dissertation work. For example, it is not just a question of how these programs are being implemented, but how they are being governed, audited, modified, and kept accountable. Additionally, Andrew talked about policy elements like established shared principles and overarching rights that can form a more responsible AI governance framework that considers issues of public data literacy or communicating how the AI program is benefiting the public. Another challenge of building the AI Localism project is going beyond criminal justice or police department applications of automation and including a diversity of AI programs. Part of this challenge goes back to the common problem of defining the programs. It is difficult to really analyze value propositions for the public good without getting a good sense of AI application programs.
As part of their work on responsible data reuse in different contexts, including at the city level, GovLab is also engaging in a public citizen deliberation process like what the group did in their Summer 2020 program that solicited public responses to the use and re-use of COVID-19 data in New York City. As I have seen throughout my conversations with civic technology experts, engaging with the community is an essential, yet challenging process for the development and implementation of technology-based programs. As part of its broader COVID-19 initiative The GovLab partnered with the New York mayor’s office and public libraries to try to translate some of their findings into policy action and help the public to better understand privacy issues about their data. While engaging with the public, Andrew spoke about the dynamics of the pandemic limiting the representativeness of their sample due to the need for remote engagement, so he does not think they got the entire range of perspectives, but he does believe that many important concerns and on-the-ground realities were well represented and made their way into the project. Andrew also believes that the project provides a methodology for tapping into public and stakeholder perceptions in a more structured and nuanced manner which can provide a social license for data reuse in the public interest. The challenges of getting a representative sample of New York City residents also speaks to the inequities of COVID-19. In terms of process, the GovLab was able to leverage existing connections and institutional capacity to reach out to the New York city public. Additionally, outreach was conducted through an online platform that helped with recruiting people throughout the city.
My conversation with Andrew was a good look at how a cross-sector collaboration, including specifically university-city collaboration, can work to great effect, especially in terms of the COVID-19 data use and re-use program outreach process. Our talk also underlined many of the common themes around data and AI policy programs: definitional issues, responsible governance, community engagement, transparency and accountability, and communication of public value. The AI Localism project is still early in its development but it is a necessary step in understanding the governance of automated government decision making, especially as long-term effects of COVID-19 on our cities will lead to more technology use across government institutions.
Garrett Morrow is a Ph.D. candidate in Political Science at Northeastern University. His dissertation looks at the politics and public trust of smart city policies, data, and algorithms. During Fall 2020, Garrett was also an Experiential Research Fellow with us here at MetroLab. His work was funded through the College of Social Sciences and Humanities at Northeastern. Garrett can be reached at morrow.g@northeastern.edu.
