Annual Review of Political Science - Volume 27, 2024

We review recent contributions to the modeling of democratic backsliding. We organize these theories according to (a) the source of constraints on the executive (vertical or horizontal restrainers) and (b) the target of backsliding (electoral manipulation or executive aggrandizement), and then use these concepts to build a scaffold for a meta-model of democratic backsliding. This meta-model allows us to describe and compare the premises and insights of this scholarship. We further apply our two-dimensional classification to more than 30 empirical papers and show how these theories can guide research design. We conclude by highlighting open issues for future research.

The impact of money on politics—whether through campaign finance, lobbying, or independent expenditure—raises key normative questions about democratic representation and accountability. In recent years there has been a deluge of new data allowing researchers to study money in politics from new and exciting perspectives. As a complement to this trend, there has also been a resurgence of interest in developing formal models to provide new theoretical insights that can help us understand how, why, and when money influences politics and policy. We review several major themes in this literature, focusing specifically on exchange-based models, informational models, and subsidy-based models. We compare and contrast the key contributions, and limitations, of each approach in understanding the role of lobbying and campaign finance in politics. We also discuss future avenues for research that incorporate aspects of each approach, which, we believe, will introduce new theoretical insights for understanding special interest influence.

One key question driving innovations in corruption studies is how anticorruption reforms can be more impactful and sustainable. This is critical to understand due to the detrimental impact of corrupt practices on equality, human rights, peace, and the rule of law. A significant body of research has shown that many anticorruption initiatives do not produce the expected effect, or they achieve results that fade after the intervention ceases. Seeking to understand how to improve anticorruption outcomes, scholars have turned to causal explanations of the persistence of corruption ranging from institutional settings and individual motives to informal practices and social norms. This article explores the intersection of social norms and corruption as a contribution to improving anticorruption programming. It explains how norms impact our conceptual understanding of corruption and the vicious cycle that exists between corrupt practices and norms. Grounded in the belief that programming and social norm diagnosis need to be contextually driven, we lay out the nascent research on changing social norms that drive corruption and the consequences of ignoring them.

While generative AI shares some similarities with previous technological breakthroughs, it also raises unique challenges for containing social and economic harms. State approaches to AI governance vary; some lay a foundation for transnational governance whereas others do not. We consider some technical dimensions of AI safety in both open and closed systems, as well as the ideas that are presently percolating to safeguard their future development. Examining initiatives for the global community and for the coalition of open societies, we argue for building a dual-track interactive strategy for containing AI's potentially nightmarish unintended consequences. We conclude that AI safety is AI governance, which means that pluralist efforts to bridge gaps between theory and practice and the STEM–humanities divide are critical for democratic sustainability.