There are many substantial barriers to finding trustworthy and meaningful sources of political information. Poor decision-making at the ballot box based on bias or un-contextualized information decouples government political action and voter accountability. These decisions spur higher rates of local corruption, greater cost for municipal bonds, lower voter turnout, signs of weakening political institutions, polarization of the electorate, and the rise of disinformation campaigns.

Turin Horse, according to Founder Nima Sarrafan, is an organization that seeks to identify statistically significant and explanatory measures for legislative efficacy. Legislative efficiency is defined as the power to facilitate the passage or blockage of a bill through the entire legislative process. Being able to measure legislative efficacy will allow us to judge the impact of individual legislators. Identifying the inner workings of a legislature through data analytics and machine learning can provide the electorate with trustworthy and meaningful information to exercise political accountability.

At the hackathon, the Turin Horse team was able to identify a core group of volunteers that will help them establish their database, as well as begin development on their platform’s front and back end. This new project is just getting started and the team continues to build their repository of election and legislation data. They continue to look for data practitioners that can help them model, structure, and analyze the data they are gathering. Additionally, they are looking for UX designers and developers that can shape and build the platform to socialize this wealth of data. Learn more about their project and how you can get involved by visiting their project profile on DemocracyLab.