Our aim with this special issue on the future of artificial intelligence (AI) politics, policy, and business is to give space to considering how the balalnce between risk and reward from AI technologies is and perhaps should be pursued by the public and private sectors. Ultimately, private firms and regulators will need to work collaboratively, given the complex networks of actors involved in AI development and deployment and the potential for the technology to alter existing policy regimes. We begin the introduction of this special issue of Business & Politics with a discussion of the growth in AI technology use and discussions of appropriate governance, followed by a consideration of how AI-related politics, policy, and business intersect. We then summarize the contributions of the authors in this issue and conclude with thoughts about how political science, public administration, and public policy scholars have much to offer, as well as much to study, the establishment of effective AI governance.

When and how does stakeholder credibility matter in shaping public opinion? We explore this question in a real-world setting: in order to fight its citizens’ financial exclusion—a key barrier to development in Indian Country—American Indian Nation “A” negotiated the first entry of the first bank to its reservation. The bank is owned by American Indian Nation “B.” To the Federal Reserve, the bank branch is a potential proof-of-concept for the capacity of tribe-to-tribe investment to improve capital access in underserved Native communities. The bank’s success ultimately depends on whether Nation A’s citizens use its services; in the months before its opening, all three stakeholders independently attempted to influence public opinion toward the bank. We collaborated to conduct a first-of-its-kind survey of Nation A’s tribal members, finding high baseline buy-in especially given the bank’s nationality, but weak and even counterproductive treatment effects of pro-banking cues provided by Nation A and the Federal Reserve. Our results make clear the practical benefits of theory-building around stakeholder credibility, and the crucial role of individual attitudes in the political economy of development.

Companies often donate to support public service delivery in US cities. Although this can help alleviate budgetary struggles for those governments, it is unclear what effect it may have on the individual residents receiving the services. In this paper, we argue that people who receive services funded in part by corporate donations are less likely to hold their local governments accountable if the services are of poor quality, because they no longer conceive of themselves as being the sole set of interests the government is catering to. We test our theory using a survey experiment with a realistic fictional government email and find evidence that, when compared with people receiving strictly taxpayer-funded services, people who are told services are provided in part by companies are less likely to take the quality of services into account when they vote.

The myriad applications of artificial intelligence (AI) by the private and public sectors have exploded in the public consciousness in the postpandemic period. However, researchers and businesses have been working on AI technology applications for decades, and in many ways, governments are rushing to catch up. This article presents an argument that the future of AI policy in the United States will be driven in large part by current and future state-level policy experiments. This argument is presented by drawing on scholarship surrounding federalism, regulatory fragmentation, and the effects of fragmentation on business and social equity. The article then presents the case of autonomous vehicle policy in the states to illustrate the degree of current fragmentation and considers the effects of layering new AI policies on top of existing rules surrounding privacy, licensing, and more. Following this consideration of existing research and its application of AI policy, the article presents a research agenda for leveraging state differences to study the effects of AI policy and develop a cohesive framework for governing AI.

Public decision-makers incorporate algorithm decision aids, often developed by private businesses, into the policy process, in part, as a method for justifying difficult decisions. Ethicists have worried that over-trust in algorithm advice and concerns about punishment if departing from an algorithm’s recommendation will result in over-reliance and harm democratic accountability. We test these concerns in a set of two pre-registered survey experiments in the judicial context conducted on three representative U.S. samples. The results show no support for the hypothesized blame dynamics, regardless of whether the judge agrees or disagrees with the algorithm. Algorithms, moreover, do not have a significant impact relative to other sources of advice. Respondents who are generally more trusting of elites assign greater blame to the decision-maker when they disagree with the algorithm, and they assign more blame when they think the decision-maker is abdicating their responsibility by agreeing with an algorithm.

I present a theoretical framework that links different configurations of organized violence to global patterns in foreign direct investment (FDI). Insurgents, states, and rogue government agents all use violence for political purposes (i.e., incapacitating rivals), but they vary in how they use violence for economic purposes (i.e., generating income). Applying Olson’s (1993) concepts of “roving” and “stationary” banditry, I hypothesize that violence perpetrated by rebels and rogue agents indeed depresses a host country’s commercial appeal, but that violence perpetrated willfully by the state doesn’t. This claim is tested against data on FDI “entry” by several thousand multinational corporations between 1994 and 2018.

Political scientists know surprisingly little about the political behavior of inventors, or those who produce new technologies. I therefore merged US patent and campaign contribution (DIME) data to reveal the donation behavior of 30,603 American inventors from 1980 through 2014. Analysis of the data produces three major findings. First, the Democratic Party has made significant inroads among American inventors, but these gains increasingly come from only a few regions and flow to a relatively small number of candidates. Second, deeper geographic trends explain most of the change in aggregate donation patterns. Third, inventors do not strategically donate to candidates outside their own district and, since 2006, inventors increasingly contribute to relatively centrist employer PACs with weak ties to the Democratic Party. These findings suggest that the interaction between market-oriented policy and American electoral institutions may inhibit the formation of broad cross-regional coalitions to support the knowledge economy.

As the development and use of artificial intelligence (AI) continues to grow, policymakers are increasingly grappling with the question of how to regulate this technology. The most far-reaching international initiative is the European Union (EU) AI Act, which aims to establish the first comprehensive, binding framework for regulating AI. In this article, we offer the first systematic analysis of non-state actor preferences toward international regulation of AI, focusing on the case of the EU AI Act. Theoretically, we develop an argument about the regulatory preferences of business actors and other non-state actors under varying conditions of AI sector competitiveness. Empirically, we test these expectations using data from public consultations on European AI regulation. Our findings are threefold. First, all types of non-state actors express concerns about AI and support regulation in some form. Second, there are nonetheless significant differences across actor types, with business actors being less concerned about the downsides of AI and more in favor of lax regulation than other non-state actors. Third, these differences are more pronounced in countries with stronger commercial AI sectors. Our findings shed new light on non-state actor preferences toward AI regulation and point to challenges for policymakers balancing competing interests in society.

What strategies work best for enforcing sanctions? Sanctions enforcement agencies like the US Office of Foreign Assets Control (OFAC) face resource limitations and political constraints in punishing domestic firms for violating sanctions. Beyond monetary fines, sanctions enforcement actions also serve a “naming and shaming” function that tarnishes violators’ reputations. Larger, higher-profile companies tend have much more at stake in terms of their reputations than smaller or less well-known firms. At the same time, punishing higher-profile companies for sanctions violations is likely to generate more publicity about the risks and potential consequences of not complying with sanctions. We theorize that OFAC should impose larger fines on high-profile companies to draw attention to those cases, make the enforcement actions more memorable, and enhance the reputational costs that they inflict. We conduct a statistical analysis of OFAC enforcement actions from 2010 to 2021 and find support for our theory.

The rapid emergence of artificial intelligence (AI) technology and its application by businesses has created a potential need for governmental regulation. While the federal government of the United States has largely sidestepped the issue of crafting law dictating limitations and expectations regarding the use of AI technology, US state legislatures have begun to take the lead in this area. Nonetheless, we know very little about how state legislatures have approached the design, pursuit, and adoption of AI policy and whether traditional political fault lines have manifested themselves in the AI issue area. Here, we gather data on the state-level adoption of AI policy, as well as roll call voting on AI bills (classified on the basis of consumer protection versus economic development), by state legislatures and analyze the political economy of AI legislation. We find that rising unemployment and inflation are negatively associated with a state’s AI policymaking. With respect to individual legislator support, we find that liberal lawmakers and Democrats are more likely to support bills establishing consumer protection requirements on AI usage. The results suggest that economic concerns loom large with AI and that traditional political fault lines may be establishing themselves in this area.

This paper explores the motives and mechanisms behind data localization implemented by states to protect data, which is essential to emerging technologies such as Artificial Intelligence. Despite the significant negative aspects of data localization for states, the practice has become increasingly prevalent, leading to the unexplored question of why states choose to implement it. This suggests that data localization is a form of economic means derived from digital technologies and employed by states to serve political objectives. Focusing on the data in platforms, the theoretical mechanism of data localization is captured in light of two factors: network perception and security externality. Network perception pertains to a state’s perception of the positive network effect generated by platforms, while security externality refers to a state’s consideration of the security implications in relation to the economic benefits derived from the positive network effect, serving the national interest in domestic and/or international contexts. To substantiate these theoretical propositions, the paper employs a comparative case study approach where Vietnam, Singapore, and Indonesia have been chosen as empirical cases based on the selection strategy. The paper bridges the concept of economic statecraft with digital technologies, fosters interdisciplinary discussions, and offers policy implications.

We argue that the everyday language distinction drawn between power and influence is meaningful and significant. There is good reason to believe that much corporate lobbying activity which is currently described under the heading of business power is better understood as attempts to secure negotiated agreements based on exerting influence rather than power and that the latter is usually used only when attempts to use influence have failed. We develop an analytical distinction between influence, understood as successful efforts at persuasion, and power using Keith Dowding’s work on power. Drawing upon findings from interviews with corporate professionals operating at the coalface of business and government interaction in Australia, we show that lobbyists generally seek “quiet” behind-the-scenes accommodations with governments via attempts to exert influence rather than power.

Why do firms demand antidumping protectionism? Contemporary literature highlights a plethora of causal mechanisms within the data-generating process, including retaliatory motives, exchange rate appreciations, business cycles, and deindustrialization. I argue that countries that are economically integrated into global markets should be associated with less demand for antidumping trade remedies. In particular, countries with higher levels of trade and financial flows should receive fewer petitions for antidumping trade remedies from firms overall, ceteris paribus. I test this theoretical argument with a series of de facto globalization indicators collected from thirty-three countries between 1978 and 2022, finding support for these arguments.