Episode 2219 - July 3 - Tiếng Anh - Phần 2 của 2 - Cách AI Đe Dọa Nền Dân Chủ - Vina Technology at AI time - Lê Quang Văn | Podcast - Nhac.vn
How AI Threatens Democracy – Part 2 of 2
Sarah KrepsDoug Kriner. Journal of Democracy. October 2023.
Threats to Democratic Representation
Democracy, as Robert Dahl wrote in 1972, requires “the continued responsiveness of the government to the preferences of its citizens.” For elected officials to be responsive to the preferences of their constituents, however, they must first be able to discern those preferences. Public-opinion polls—which (at least for now) are mostly immune from manipulation by AI-generated content—afford elected officials one window into their constituents’ preferences. But most citizens lack even basic political knowledge, and levels of policy-specific knowledge are likely lower still. As such, legislators have strong incentives to be the most responsive to constituents with strongly held views on a specific policy issue and those for whom the issue is highly salient. Written correspondence has long been central to how elected officials keep their finger on the pulse of their districts, particularly to gauge the preferences of those most intensely mobilized on a given issue.
In an era of generative AI, however, the signals sent by the balance of electronic communications about pressing policy issues may be severely misleading. Technological advances now allow malicious actors to generate false “constituent sentiment” at scale by effortlessly creating unique messages taking positions on any side of a myriad of issues. Even with old technology, legislators struggled to discern between human-written and machine-generated communications.
In a field experiment conducted in 2020 in the United States, we composed advocacy letters on six different issues and then used those letters to train what was then the state-of-the-art generative AI model, GPT-3, to write hundreds of left-wing and right-wing advocacy letters. We sent randomized AI- and human-written letters to 7,200 state legislators, a total of about 35,000 emails. We then compared response rates to the human-written and AI-generated correspondence to assess the extent to which legislators were able to discern (and therefore not respond to) machine-written appeals. On three issues, the response rates to AI- and human-written messages were statistically indistinguishable. On three other issues, the response rates to AI-generated emails were lower—but only by 2 percent, on average. This suggests that a malicious actor capable of easily generating thousands of unique communications could potentially skew legislators’ perceptions of which issues are most important to their constituents as well as how constituents feel about any given issue.
In the same way, generative AI could strike a double blow against the quality of democratic representation by rendering obsolete the public-comment process through which citizens can seek to influence the actions of the regulatory state. Legislators necessarily write statutes in broad brushstrokes, granting administrative agencies considerable discretion not only to resolve technical questions requiring substantive expertise (e.g., specifying permissible levels of pollutants in the air and water), but also to make broader judgements about values (e.g., the acceptable tradeoffs between protecting public health and not unduly restricting economic growth). Moreover, in an era of intense partisan polarization and frequent legislative gridlock on pressing policy priorities, U.S. presidents have increasingly sought to advance their policy agendas through administrative rulemaking.
Moving the locus of policymaking authority from elected representatives to unelected bureaucrats raises concerns of a democratic deficit. The U.S. Supreme Court raised such concerns in West Virginia v. EPA (2022), articulating and codifying the major questions doctrine, which holds that agencies do not have authority to effect major changes in policy absent clear statutory authorization from Congress.
