Soft law is defined as a program that sets substantive expectations, but is not directly enforceable by government. Because soft law is not bound by a geographic jurisdiction and can be developed, amended, and adopted by any entity, it will be the dominant form of artificial intelligence (AI) governance for the foreseeable future. The objective of this document is to compile and analyze global trends on how this governance tool is used by government, non-profits, and the private sector to manage AI’s methods and applications.
Each program conforms to the following eligibility criteria:
- complies with the definition of soft law
- emphasizes the governance or management of a method or applicatino of AI
- were published by December 31st, 2019
The information in this database is a snapshop of the programs found from 2001-2019. The information is divided into four sheets:
- Codebook: contains a description of each variable and theme within the database
- Dashboard – Variables: compiles the statistics for a program’s variables – data that provides information on how it is organized, functions, and its general characteristics.
- Dashboard – Themes: compiles the frequency of themes – labels that communicate the subject matter discussed within a program’s text.
- Master Table Softlaw: contains all of the information from the 634 soft law programs
Further information on the methodology and analysis of this database’s trends can be found in our project report.
This project was led by: Carlos Ignacio Gutierrez and Gary Marchant.
The following individuals were key contributors to the project: Joshua Abbott, Matthew Ruth, Kaylee Hoffner, Alexander Kearl, Alec Carden, Timmy Lee, and Morgan Stevens