My research agenda focuses on the power, regulation, and self-governance of Big Tech companies. The prevalence of artificial intelligence (AI) and growth of Big Tech make understanding the global role of these companies a pressing issue within the global political economy. I join this timely conversation by analyzing mechanisms operating behind the platform power of Big Tech firms in my dissertation project. I then focus on two mechanisms, issue salience and geopolitical reasons, to analyze the self-governance actions of Big Tech companies. The level of internal formalization varies across self-governance actions. I find that the level of formalization increases as the level of geopolitical demand from a Big Tech firm's home state increases and as the public salience of a Big Tech-relevant issue increases.
In "Local Data Policies, Global Data Politics: How Citizens Evaluate Data Localization Policies and Political Responses," published at Foreign Policy Analysis, Dr. Tyler Girard and I find that Americans oppose data localization in other countries when framed in terms of negative economic impacts to the United States, but their attitudes are unaffected by frames emphasizing the sovereignty implications for other countries. Further, we find evidence that ethnocentric valuations shape how Americans evaluate the adoption of data localization policies abroad.
In other projects, I explore the ethical certification regimes for companies using AI, the integration of public values into U.S. AI policies, and the use of human rights language by Big Tech firms. I aim to contribute to the expanding scholarly work on Big Tech regulation and practitioners’ understanding of these firms’ environmental, human rights, and ethics impacts.
My training allows me to apply a variety of methodological approaches based on the questions at hand. This includes both qualitative and quantitative tools, such as process tracing, text analysis, and survey experiments. I have pursued advanced methodological training from the Inter-university Consortium for Political and Social Research (ICPSR) summer program and the Advanced Methods at Purdue (AMAP) Graduate Certificate through the Methodology Center at Purdue (MCAP).
Girard, Tyler, and Alexander Wilhelm. 2025. “Local Data Policies, Global Data Politics: How Citizens Evaluate Data Localization Policies and Political Responses.” Foreign Policy Analysis 21(4). doi: https://doi.org/10.1093/fpa/oraf007.
Enwereazu, Ogadinma, Kaylyn Jackson Schiff, Daniel S. Schiff, Tyler Girard, and Alexander Wilhelm. “A Public Value Framework for AI Governance: Evidence from US Federal AI Policymaking." R & R, Public Administration.
Srivastava, Swati, Alexander Wilhelm, and Yaosheng Xu. “From Frontline Defenders to Rights Indifferent: Combining Qualitative and Computational Analysis to Study Big Tech’s Private Governance on Human Rights.” Submitted for review.
Wilhelm, Alexander. “Big Tech’s Environmental Emphases: Platform Power and Issue Salience.”
Schiff, Daniel S., Kaylyn Jackson Schiff, Alexander Wilhelm, and Tyler Girard. “Responsible AI Commitments Build Consumer Trust.”
Wilhelm, Alexander. “Between Pirate and Privateer: Conceptualizing Big Tech’s Authority.”
Wilhelm, Alexander, Daniel S. Schiff, Tyler Girard, and Kaylyn Jackson Schiff. “The Influence of Information and Audits on the Public’s Perception of AI.”
Wilhelm, Alexander. August 5, 2025. “AI Policy Corner: AI for Good Summit 2025.” Montreal AI Ethics Institute.
Wilhelm, Alexander. April 28, 2025. “AI Policy Corner: Frontier AI Safety Commitments, AI Seoul Summit 2024.” Montreal AI Ethics Institute.
Lead Research Assistant, International Politics and Responsible Tech (iPART) Lab, Dr. Swati Srivastava
Compiled “Big Tech Transparency Database” on human rights-related disclosures
Assisted on grant application for NSF CAREER
Managed undergraduate research assistants and led lab meetings
Research Affiliate, Governance & Responsible AI Lab (GRAIL)
Research Assistant, Dr. Kaylyn Jackson Schiff (2024)
Constructed conjoint and vignette survey experiments on AI ethics labeling