{"id":30332,"date":"2023-10-04T12:11:16","date_gmt":"2023-10-04T16:11:16","guid":{"rendered":"https:\/\/peterlevine.ws\/?p=30332"},"modified":"2023-10-06T13:10:31","modified_gmt":"2023-10-06T17:10:31","slug":"can-ai-help-governments-and-corporations-identify-political-opponents","status":"publish","type":"post","link":"https:\/\/peterlevine.ws\/?p=30332","title":{"rendered":"can AI help governments and corporations identify political opponents?"},"content":{"rendered":"\n

In “Large Language Model Soft Ideologization via AI-Self-Consciousness<\/a>,” Xiaotian Zhou, Qian Wang, Xiaofeng Wang, Haixu Tang, and Xiaozhong Liu use ChatGPT to identify the signature of “three distinct and influential ideologies: \u201c’Trumplism’ (entwined with US politics), ‘BLM (Black Lives Matter)’ (a prominent social movement), and ‘China-US harmonious co-existence is of great significance’ (propaganda from the Chinese Communist Party).” They unpack each of these ideologies as a connected network of thousands of specific topics, each one having a positive or negative valence. For instance, someone who endorses the Chinese government’s line may mention US-China relationships and the Nixon-Mao summit as a pair of linked positive ideas.<\/p>\n\n\n\n

The authors raise the concern that this method would be a cheap way to predict the ideological leanings of millions of individuals, whether or not they choose to express their core ideas. A government or company that wanted to keep an eye on potential opponents wouldn’t have to search social media for explicit references to their issues of concern. It could infer an oppositional stance from the pattern of topics that the individuals choose to mention.<\/p>\n\n\n\n

I saw this article because the authors cite my piece, “Mapping ideologies as networks of ideas,” Journal of Political Ideologies<\/em> (2022): 1-28. (Google Scholar notified me of the reference.) Along with many others, I am developing methods for analyzing people’s political views as belief-networks. <\/p>\n\n\n\n

I have a benign motivation: I take seriously how people explicitly articulate and connect their own ideas and seek to reveal the highly heterogeneous ways that we reason. I am critical of methods that reduce people’s views to widely shared, unconscious psychological factors. <\/p>\n\n\n\n

However, I can see that a similar method could be exploited to identify individuals as targets for surveillance and discrimination. Whereas I am interested in the whole of an individual’s stated belief-network, a powerful government or company might use the same data to infer whether a person would endorse an idea that it finds threatening, such as support for unions or affinity for a foreign country. If the individual chose to keep that particular idea private, the company or government could still infer it and take punitive action.<\/p>\n\n\n\n

I’m pretty confident that my technical acumen is so limited that I will never contribute to effective monitoring. If I have anything to contribute, it’s in the domain of political theory. But this is something–yet another thing–to worry about.<\/p>\n\n\n\n

See also: Mapping Ideologies as Networks of Ideas<\/a> (talk); Mapping Ideologies as Networks of Ideas<\/a> (paper); what if people\u2019s political opinions are very heterogeneous?<\/a>; how intuitions relate to reasons: a social approach<\/a>; the difference between human and artificial intelligence: relationships<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

In “Large Language Model Soft Ideologization via AI-Self-Consciousness,” Xiaotian Zhou, Qian Wang, Xiaofeng Wang, Haixu Tang, and Xiaozhong Liu use ChatGPT to identify the signature of “three distinct and influential ideologies: \u201c’Trumplism’ (entwined with US politics), ‘BLM (Black Lives Matter)’ (a prominent social movement), and ‘China-US harmonious co-existence is of great significance’ (propaganda from the […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[43,29,8,1],"tags":[],"class_list":["post-30332","post","type-post","status-publish","format-standard","hentry","category-artificial-intelligence","category-moral-network-mapping","category-internet-and-public-issues","category-uncategorized"],"acf":[],"_links":{"self":[{"href":"https:\/\/peterlevine.ws\/index.php?rest_route=\/wp\/v2\/posts\/30332","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/peterlevine.ws\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/peterlevine.ws\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/peterlevine.ws\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/peterlevine.ws\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=30332"}],"version-history":[{"count":15,"href":"https:\/\/peterlevine.ws\/index.php?rest_route=\/wp\/v2\/posts\/30332\/revisions"}],"predecessor-version":[{"id":30348,"href":"https:\/\/peterlevine.ws\/index.php?rest_route=\/wp\/v2\/posts\/30332\/revisions\/30348"}],"wp:attachment":[{"href":"https:\/\/peterlevine.ws\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=30332"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/peterlevine.ws\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=30332"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/peterlevine.ws\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=30332"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}