Machine Advisors: Integrating Large Language Models into Democratic Assemblies

A preprint by Petr Špecián available via SSRN

Abstract: Large language models (LLMs) represent the currently most relevant incarnation of artificial intelligence with respect to the future fate of democratic governance. Considering their potential, this paper seeks to answer a pressing question: Could LLMs outperform humans as expert advisors to democratic assemblies? While bearing promise of enhanced expertise availability and accessibility, they also present challenges of hallucinations, misalignment, or value imposition. Weighing LLMs’ benefits and drawbacks compared to their human counterparts, I argue for their careful integration to augment democracy’s ability to address complex policy issues. The paper posits that time-tested democratic procedures like deliberation and voting provide safeguards effective against both human and machine advisor imperfections. Additional protective layers, such as boosting representatives’ competencies in query formulation or implementation of adversarial proceedings (expert debates and dissenting opinions), could further mitigate the risks that LLMs present in advisory role. While my exploration remains conceptual, it sets the stage for an empirical research agenda and offers a roadmap for the co-evolution of AI and democratic institutions. The stakes are high: success or failure in integration of the transformative AI technologies could spell the difference between the current democratic backsliding and a new wave of democratic efflorescence driven by an improved ability to tackle the difficult challenges of our time.

A Case for Democracy’s Digital Playground

Petr Špecián has published the outline of his idea on using digital worlds to expedite institutional innovation on The Loop, a blog by The European Consortium for Political Research (ECPR). You can read his essay here