{"id":162,"date":"2021-09-01T20:09:04","date_gmt":"2021-09-01T20:09:04","guid":{"rendered":"https:\/\/lsi.asulaw.org\/softlaw\/?post_type=report&p=162"},"modified":"2021-10-18T16:19:23","modified_gmt":"2021-10-18T16:19:23","slug":"2-2-geography","status":"publish","type":"report","link":"https:\/\/lsi.asulaw.org\/softlaw\/report\/2-results\/2-2-geography\/","title":{"rendered":"2.2 Geography"},"content":{"rendered":"\n
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2.2 Geography<\/h1>\n <\/div>\n <\/div>\n\n \n<\/div>\n\n\n
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Programs in the database represent 64 unique geographic areas. In reviewing the World Bank\u2019s classification for the regions and wealth of countries in this list, a concentration is detected in terms of the type of nation(s) interested in dedicating resources to the creation of AI soft law programs [28]. Out of the 64 locations, 70% of these countries are either in Europe and Central Asia or East Asia and Pacific, and 80% are considered high-income (see Table 4).<\/p>\n\n\n\n

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Further information was found in the two types of geographic relationships examined in the database (see Table 5). Country of origin describes the location where a soft law program was created, while jurisdiction represents its intended geographical area of application (\u201cjurisdiction\u201d). The U.S. is an important influence in this database as it is home to a large proportion of technology-based organizations that develop AI products and services, ~45% of which operate internationally. Compared to the US, countries such as Canada and China appear to have a mostly insular scope of geographic influence.<\/p>\n\n\n\n

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