Digital Government

In US State government legislatures, most activity occurs in committees made of lawmakers discussing bills. This paper presents systems to extract legislators' engagement and absence during committee meetings and the stance and affiliation of non-lawmakers making public comments. We propose a system to track the affiliation of organizations in public comments and whether the organizational representative supports or opposes the bill.

Government transparency challenges exist in many forms but at the level of US State legislatures, but they are particularly egregious as public hearings are not documented in text. No official written record of public legislative meetings exists in any state legislature in America. Although a number of states have close captioned videos, these are not accurate or searchable. They are only in English. There is also no identification of speakers, bills, votes, locations, committees, etc.

This White Paper describes the research and design of the software system called Digital Democracy. The Digital Democracy system will provide its users: individuals with interest in public policy, journalists and media organizations, public interest and government watchdog groups, and lawmakers with direct access to videos and transcripts of State Legislative Committee hearings, as well as analytical tools enabling users of the system to conduct in-depth research of public policy issues and the attitudes of state legislators towards them.

We present a study using the Digital Democracy transcription tool. Human transcribers work to up-level and annotate California state legislative proceedings using the tool. Four phases of UI and functionality improvements are introduced and for each phase, the resulting change in efficiency is measured and presented.

In this work, we seek to quantify the extent to which a legislator's spoken language indicates their degree of alignment toward an organization that has a taken a documented position on some legislation. To perform this study, we use a corpus of bill discussion transcripts provided by Digital Democracy1. We then apply proven learning methods in the field of natural language processing to predict alignment scores between each member of the California state legislature and a select set of state-recognized organizations.

Government relationships can be complex and difficult to understand. The relationships between members of a legislature, bills, votes and lobbyists who promote various causes are important to understand in representative democracies, but difficult to retrieve using current methods. In this paper, we propose a 3D visualization system to explore such legislative relationships for users. We use real data from California state legislature obtained from the Digital Democracy project.

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.