Digital Government

This research concerns the perceived need for and benefits of an algorithmically generated, personalizable tip sheet that could be used by journalists to improve and expand coverage of state legislatures. This study engaged in two research projects to understand if working journalists could make good use of such a tool and, if so, what features and functionalities they would most value within it. This study also explored journalists’ perceptions of the role of such tools in their newswork.

Legislative proceedings present a rich source of multidimensional information that is crucial to citizens and journalists in a democratic system. At present, no fully automated solution exists that is capable of capturing all the necessary information during such proceedings. Even if professional-quality automated transcriptions existed, other tasks such as speaker or rhetorical position identifications are not fully automatable. This work focuses on improving and evaluating the transcription software used by the Digital Democracy initiative, named Transcription Tool.

Algorithmic journalism refers to automatic AI-constructed news stories. There have been successful commercial implementations for news stories in sports, weather, financial reporting and similar domains with highly structured, well defined tabular data sources. Other domains such as local reporting have not seen adoption of algorithmic journalism, and thus no automated reporting systems are available in these categories which can have important implications for the industry.

California Proposition 54, 2016 (Prop 54) attempted to address gaps in government transparency at the state level by requiring legislative proceedings to be published on the internet, but failed to consider the traditional barriers that limit citizen participation in policy.

As most dedicated observers of voting bodies like the U.S. Supreme Court can attest, it is possible to guess vote outcomes based on statements made during deliberations or questioning by the voting members. We show this is also possible to do automatically using machine learning, potentially providing a powerful tool to ordinary citizens. Our working hypothesis is that verbal utterances made during the legislative process by elected representatives can indicate their intent on a future vote, and therefore can be used to automatically predict said vote to a significant degree.

A first-of-its-kind platform, Digital Democracy1 offers a searchable archive of all statements made in US state legislative hearings in four American states (California, New York, Texas and Florida) covering one third of the US population. The purpose of the platform is to increase government transparency in state legislatures. It allows citizens to follow state lawmakers, lobbyists, and advocates as they debate, craft, and vote on policy proposals. State hearings in the U.S. are typically recorded on video and broadcast on cable TV stations, but they are not transcribed or indexed.

The Digital Democracy platform obtains data about the legislative committee hearings: the video archives, the information about the state legislature and so on. Figure 1 shows the design of the DD system. The main source of information for the DD platform is the Cal Channel video archive of legislative sessions, a service provided courtesy of cable TV companies that operate in California.