Academic Writing

Under Review

2025

1. How deceptive online networks reached millions in the US 2020 elections

   Ruth E. Appel, Young Mie Kim, Jennifer Pan, Yiqing Xu, Daniel Thomas, and 27 more authors

   2025
2. Psychological inoculation improves resilience to and reduces willingness to share vaccine misinformation

   Ruth E. Appel, Jon Roozenbeek, Rebecca Rayburn-Reeves, Jonathan Corbin, Melisa Basol, and 2 more authors

   2025

   Full Text
3. Bridging the empathy gap: Reducing empathy misperceptions increases social connectedness

   Rui Pei, Samantha J. Grayson, Ruth E. Appel, Serena Soh, Sydney Garcia, and 4 more authors

   2025
4. Generative AI Regulation Can Learn from Social Media Regulation

   Ruth E. Appel

   2025

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5. Political Neutrality in AI is Impossible — But Here Is How to Approximate It

   Jillian Fisher, Ruth E. Appel, Chan Young Park, Yujin Potter, Liwei Jiang, and 7 more authors

   2025

   Abstract Full Text

   AI systems often exhibit political bias, influencing users’ opinions and decision-making. While political neutrality—defined as the absence of bias—is often seen as an ideal solution for fairness and safety, this position paper argues that true political neutrality is neither feasible nor universally desirable due to its subjective nature and the biases inherent in AI training data, algorithms, and user interactions. However, inspired by Joseph Raz’s philosophical insight that "neutrality [...] can be a matter of degree" (Raz, 1986), we argue that striving for some neutrality remains essential for promoting balanced AI interactions and mitigating user manipulation. Therefore, we use the term "approximation" of political neutrality to shift the focus from unattainable absolutes to achievable, practical proxies. We propose eight techniques for approximating neutrality across three levels of conceptualizing AI, examining their trade-offs and implementation strategies. In addition, we explore two concrete applications of these approximations to illustrate their practicality. Finally, we assess our framework on current large language models (LLMs) at the output level, providing a demonstration of how it can be evaluated. This work seeks to advance nuanced discussions of political neutrality in AI and promote the development of responsible, aligned language models.

6. In-House Evaluation Is Not Enough. Towards Robust Third-Party Evaluation and Flaw Disclosure for General-Purpose AI

   Shayne Longpre, Kevin Klyman, Ruth E. Appel, Sayash Kapoor, Rishi Bommasani, and 29 more authors

   2025

   Abstract Full Text

   The widespread deployment of general-purpose AI (GPAI) systems introduces significant new risks. Yet the infrastructure, practices, and norms for reporting flaws in GPAI systems remain seriously underdeveloped, lagging far behind more established fields like software security. Based on a collaboration between experts from the fields of software security, machine learning, law, social science, and policy, we identify key gaps in the evaluation and reporting of flaws in GPAI systems. We call for three interventions to advance system safety. First, we propose using standardized AI flaw reports and rules of engagement for researchers in order to ease the process of submitting, reproducing, and triaging flaws in GPAI systems. Second, we propose GPAI system providers adopt broadly-scoped flaw disclosure programs, borrowing from bug bounties, with legal safe harbors to protect researchers. Third, we advocate for the development of improved infrastructure to coordinate distribution of flaw reports across the many stakeholders who may be impacted. These interventions are increasingly urgent, as evidenced by the prevalence of jailbreaks and other flaws that can transfer across different providers’ GPAI systems. By promoting robust reporting and coordination in the AI ecosystem, these proposals could significantly improve the safety, security, and accountability of GPAI systems.

Work in Progress

2025

1. How partisanship affects preferences for content moderation in large language models

   Ruth E. Appel, Jennifer Pan, and Margaret E. Roberts

   2025
2. Combating misinformation on social media

   Ruth E. Appel, Susan Athey, Dean Karlan, Kristine Koutout, Michael Luca, and 3 more authors

   2025

Workshop Papers

2024

1. Generative AI Regulation Can Learn from Social Media Regulation

   Ruth E. Appel

   2024

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Journal Articles

2023

1. Partisan conflict over content moderation is more than disagreement about facts

   Ruth E. Appel, Jennifer Pan, and Margaret E. Roberts

   Science Advances, 2023

   Abstract Full Text

   Social media companies have come under increasing pressure to remove misinformation from their platforms, but partisan disagreements over what should be removed have stymied efforts to deal with misinformation in the United States. Current explanations for these disagreements center on the “fact gap”—differences in perceptions about what is misinformation. We argue that partisan differences could also be due to “party promotion”—a desire to leave misinformation online that promotes one’s own party—or a “preference gap”—differences in internalized preferences about whether misinformation should be removed. Through an experiment where respondents are shown false headlines aligned with their own or the opposing party, we find some evidence of party promotion among Democrats and strong evidence of a preference gap between Democrats and Republicans. Even when Republicans agree that content is false, they are half as likely as Democrats to say that the content should be removed and more than twice as likely to consider removal as censorship.

2020

1. Privacy in the age of psychological targeting

   Sandra C. Matz, Ruth E. Appel, and Michal Kosinski

   Current Opinion in Psychology, 2020

   Abstract Full Text

   Psychological targeting describes the practice of extracting people’s psychological profiles from their digital footprints (e.g., their Facebook Likes, Tweets or credit card records) in order to influence their attitudes, emotions or behaviors through psychologically-informed interventions at scale. We discuss how the increasingly blurred lines between public and private information, and the continuation of the outdated practices of notice and consent, challenge traditional conceptualizations of privacy in the context of psychological targeting. Drawing on the theory of contextual integrity, we argue that it is time to rethink privacy and move beyond the questions of who collects what data to how the data are being used. Finally, we suggest that regulations of psychological targeting should be accompanied by a mindset that fosters (1) privacy by design to make it easy for individuals to act in line with their privacy goals, as well as (2) disclosure by choice in which individuals can freely decide whether and when they might be willing to forsake their privacy for better service.

Book Chapters

2022

1. Privacy and ethics in the age of Big Data

   Sandra C. Matz, Ruth E. Appel, and Brian Croll

   In The psychology of technology: Social science research in the age of Big Data, 2022

   Abstract Full Text

   This chapter discusses the new ethical challenges introduced by the age of Big Data. Although there are many other ethical challenges related to technology and data (e.g., addiction, inequality), it reviews the topic of privacy as one of the major challenges associated with Big Data. The chapter introduces the concept of privacy, briefly discussing its history, universality, and core assumptions that lie at the heart of privacy protections. It then moves on to the questions of how Big Data threatens our privacy in unprecedented ways and challenges current approaches to privacy protection. Next, the chapter discusses how placing the burden of privacy protection on users alone is misguided and provide a number of potential systemic solutions related to regulation, collaboration, design principles, and technological tools. It concludes with concrete practical guidelines for researchers and practitioners of how to design studies, products, and services that protect individuals’ privacy.

2021

1. Psychological targeting in the age of Big Data

   Ruth E. Appel, and Sandra C. Matz

   In Measuring and Modeling Persons and Situations, 2021

   Abstract Full Text

   Advances in the collection, storage, and processing of large amounts of user data have given rise to psychological targeting, which we define as the process of extracting individuals’ psychological characteristics from their digital footprints in order to target them with psychologically-informed interventions at scale. In this chapter, we introduce a two-stage framework of psychological targeting consisting of (1) psychological profiling and (2) psychologically-informed interventions. We summarize the most important research findings in relation to the two stages and discuss important methodological opportunities and pitfalls. To help researchers make the most of the opportunities, we also provide practical advice on how to deal with some of the potential pitfalls. Finally, we highlight ethical opportunities and challenges and offer some suggestions for addressing these challenges. If done right, psychological targeting has the potential to advance our scientific understanding of human nature and to enhance the well-being of individuals and society at large.