ArtifexAI

Want to understand who really holds power in state politics and how laws actually get made? This project dives into the hidden networks of the Massachusetts State Legislature using real bill data from the past 5-10 years. Rather than just looking at official party lines, we’ll use network science, graphing algorithms and ML models to uncover the true alliances and power structures that shape our laws. By analyzing how legislators team up to cosponsor bills, we can reveal the unofficial ‘tribes’ within the legislature - who consistently work together, who bridge different groups, and who might be more influenced by special interests than party loyalty. Understanding the dynamics of legislative power and collaboration is critical for transparency and accountability. This project leverages Massachusetts State Legislature bill data (2017–present) to uncover hidden patterns in cosponsorships, voting behavior, and policy influence. By applying network science and graph-based methodologies, the team will identify key players, alliances, and power dynamics that may not align with traditional party lines. Using tools like graph analysis, clustering, and machine learning, the team will explore the following questions: • How do legislators align based on bill sponsorship or topics? • Are there influential legislators who shape bipartisan collaborations? • Can we identify outliers or groups influenced by funding sources or lobbyists? • Are there legislators who suddenly break from their usual allies on specific topics like climate change or healthcare? • Do certain groups only come together for particular types of bills? The deliverables aim to enhance transparency by producing actionable insights that advocacy groups, journalists, and citizens can use to understand legislative processes better.