{"id":160,"date":"2021-09-01T20:08:27","date_gmt":"2021-09-01T20:08:27","guid":{"rendered":"https:\/\/lsi.asulaw.org\/softlaw\/?post_type=report&p=160"},"modified":"2021-10-18T16:15:18","modified_gmt":"2021-10-18T16:15:18","slug":"2-results","status":"publish","type":"report","link":"https:\/\/lsi.asulaw.org\/softlaw\/report\/2-results\/","title":{"rendered":"2. Results"},"content":{"rendered":"\n
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2. Results<\/h1>\n <\/div>\n <\/div>\n\n \n<\/div>\n\n\n
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This project\u2019s methodology generated a database of 634 soft law programs coded with up to 107 variables or themes. The insights uncovered through the analysis of this data is divided into seven sections:<\/p>\n\n\n\n

  1. Year of publication<\/strong>: despite finding a 20-year range of programs governing AI through soft law, its usage is a relatively recent phenomenon since over 90% of programs in our sample were published between 2016-2019.<\/li>
  2. Geography<\/strong>: higher income countries dominate the generation of soft law, with a large concentration coming from the US and Europe.<\/li>
  3. Influence<\/strong>: the vast majority of programs are created to influence the organization that created them and external stakeholders.<\/li>
  4. Type of program<\/strong>: about 79% of programs are classified either as principles, recommendations, or strategies<\/li>
  5. Stakeholders<\/strong>: by organizing stakeholders into governance triangles we found that government entities led the highest number of programs (~36%).<\/li>
  6. Enforcement<\/strong>: only 30% of programs publicly mention an enforcement or implementation mechanisms.<\/li>
  7. Themes<\/strong>: we identified 15 themes and 78 sub-themes in the soft law governance of AI. The most prevalent theme was education \u2013 displacement of labor (identified in 815 excerpts) and the sub-theme was \u201cgeneral transparency\u201d (present in ~43% of programs).<\/li><\/ol>\n\n\n\n

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