In a crucial election year, political maps may determine the outcomes, highlighting the role of gerrymandering in shaping electoral districts. Gerrymandering manipulates district boundaries to favor political parties or demographic groups, influencing whose voices are heard. Tyler Simko at the University of Michigan is challenging this by leveraging artificial intelligence and big data to mitigate biases that hinder equal representation.
Simko, an affiliate with the Center for Political Studies at U-M’s Institute for Social Research, applies computational methods to address ongoing issues in state and local politics. His initiatives include tackling systemic inequality in school districts and developing an open-source tool that enhances transparency in local governance.
Having joined U-M in August, Simko brings a unique perspective, informed by his experience serving two terms on the local board of education in South Amboy, New Jersey. “Service in local politics taught me how local government decisions shape our lives,” Simko said, emphasizing the impact of local governance on daily life decisions.
AI and Fair Representation Mapping
Simko employs computational tools and AI to redefine political boundaries equitably, directly addressing gerrymandering. As a co-principal investigator of the Algorithm-Assisted Redistricting Methodology (ALARM) Project, he aims to reveal the processes behind boundary creation and promote transparency.
The ALARM project has developed open-source software that uses algorithms to simulate redistricting processes, like the redist software. This tool generates numerous district maps adhering to legal standards, providing a neutral benchmark. Consistent equitable outcomes from these maps highlight how politically drawn maps often favor partisan interests.
The AI-generated maps demonstrate that states with independent redistricting commissions, such as Michigan, produce fairer maps. “In recent work, we show that Michigan’s redistricting process is one of the fairest in the country,” Simko stated.
Redrawing School Segregation Lines
Simko uses computational tools to address school district boundaries, having observed the disparities caused by fragmented districts. “New Jersey has one of the most racially segregated school systems,” he noted. His algorithms model scenarios to balance various factors while promoting diversity and integration without necessitating new construction or longer commutes.
This work provides school boards with data to tackle systemic inequality, showing that segregation often results from political decisions rather than geographic limitations.
Open-Source Tool for Transparency
Simko addresses the lack of transparency in local government decisions through his third major project. Recognizing the decentralized nature of U.S. governance, he co-created LocalView with Soubhik Barari from NORC at the University of Chicago. This open-source database compiles meeting videos and transcripts from local governments.
LocalView powers CivicSearch, allowing users to easily search local government discussions on issues like housing and sustainability. This tool fills a vital information gap as local newsrooms shrink, providing the public and journalists with accessible government data.
“This is a place to learn about local government actions,” Simko explained, emphasizing the transformative power of AI and computational tools in democratizing information.
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