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Recent Submissions
A Democracy, If We Can Teach It: Educating the Next Generation of Citizens
Rendell, Marjorie O. (Cornell University Press, 2025-12-05)
The Rendell Center for Civics and Civic Engagement advances nonpartisan civic learning by working with K–12 teachers and students. Founded in 2014 by Judge Marjorie O. Rendell and former Pennsylvania Governor Ed Rendell, the center's programs include statewide read-alouds, where judges, lawyers and other professionals read books to elementary school students across the state, as well as the Citizenship Challenge, an essay contest for fourth- and fifth-graders with prompts based on Pennsylvania civics standards. This book tells the story of how the center equips young people with knowledge about the workings of our constitutional system and prepares them to step into public life as leaders and active, engaged citizens. For this important work, the Rendell Center for Civics and Civic Engagement is the recipient of the 2025 Brown Democracy Medal from the McCourtney Institute for Democracy.
The Sins of the Parents are to be Laid upon the Children: Biased Humans, Biased Data, Biased Models
Osborne, Merrick R.; Omrani, Ali; Dehghani, Morteza (SAGE, 2023)
Technological innovations have become a key driver of societal advancements. Nowhere is this more evident than in the field of machine learning (ML), which has developed algorithmic models that shape our decisions, behaviors, and outcomes. These tools have widespread use, in part, because they can synthesize massive amounts of data to make seemingly objective recommendations. Yet, in the past few years, the ML community has been drawing attention to the need for caution when interpreting and using these models. This is because these models are created by humans, from data generated by humans, whose psychology allows for various biases that impact how the models are developed, trained, tested, and interpreted. As psychologists, we thus face a fork in the road: Down the first path, we can continue to use these models without examining and addressing these critical flaws and rely on computer scientists to try to mitigate them. Down the second path, we can turn our expertise in bias toward this growing field, collaborating with computer scientists to reduce the models’ deleterious outcomes. This article serves to light the way down the second path by identifying how extant psychological research can help examine and curtail bias in ML models.
Me vs. the Machine? Subjective Evaluations of Human- and AI- Generated Advice
Osborne, Merrick R.; Bailey, Erica R. (Nature Research, 2025)
Artificial intelligence (“AI”) has the potential to vastly improve human decision-making. In line with this, researchers have increasingly sought to understand how people view AI, often documenting skepticism and even outright aversion to these tools. In the present research, we complement these findings by documenting the performance of LLMs in the personal advice domain. In addition, we shift the focus in a new direction—exploring how interacting with AI tools, specifically large language models, impacts the user’s view of themselves. In five preregistered experiments (N = 1,722), we explore evaluations of human- and ChatGPT-generated advice along three dimensions: quality, effectiveness, and authenticity. We find that ChatGPT produces superior advice relative to the average online participant even in a domain in which people strongly prefer human-generated advice (dating and relationships). We also document a bias against ChatGPT-generated advice which is present only when participants are aware the advice was generated by ChatGPT. Novel to the present investigation, we then explore how interacting with these tools impacts self-evaluations. We manipulate the order in which people interact with these tools relative to self-generation and find that generating advice
before interacting with ChatGPT advice boosts the quality ratings of the ChatGPT advice. At the same time, interacting with ChatGPT-generated advice before self-generating advice decreases self-ratings of authenticity. Taken together, we document a bias towards AI in the context of personal advice. Further, we identify an important externality in the use of these tools—they can invoke social comparisons of me vs. the machine.
The role of GCN2 in the regulation of pro-inflammatory and anti-inflammatory immune responses during influenza virus infection
Anronikov, Alexandra Sasha (2025-12-02)
General control nonderepressible 2 (GCN2) is a protein kinase activated by amino acid deprivation in the body. It acts as a checkpoint where cells commit to apoptosis, autophagy, or releasing exosomes. Manipulating this checkpoint can open up novel therapeutic opportunities for inflammatory diseases. Our study seeks to understand how GCN2 regulates pro-inflammatory and anti-inflammatory responses following influenza virus infection, specifically, in the regulation of expression of inflammatory (IL17) and anti-inflammatory (IL10) cytokines by T cells. We find that GCN2 does not significantly affect recruitment of T cells or granulocytes to the lungs during flu infection. Our results also suggest that GCN2 does not play an important role in regulating IL10 and IL17 cytokines during the immune response to influenza virus infection in mice.
From City Streets to Statewide Sustainability: Assessing the Impact of New York City's Residential Curbside Composting Program on Greenhouse Gas Emissions & Potential for Statewide Extension
Elizabeth S. Taber (2024-05)
New York City's residential curbside composting program, initially piloted in Queens, is set to expand citywide. The initiative attempts to address the significant methane emissions linked to landfilling food scraps by embracing alternative management methods such as composting and anaerobic digestion. This research assesses the potential impact of implementing a composting program modeled off of New York City’s program in each municipality in NYS. It first considers whether NYC’s curbside composting program has effectively reduced the greenhouse gas emissions associated with food waste through increasing the quantity of waste redirected from landfill disposal. Then, analysis of the city’s program is then used to consider the potential impact of the municipal food scrap recycling program on greenhouse gas emissions in the remainder of New York State. To evaluate the program's effect on greenhouse gas emissions, this study designs an evaluation methodology based on the availability of food scrap recycling facilities, emissions associated with transporting waste to recycling facilities, municipal population, and diversion rates. These measures provide insight into expansion and efficacy barriers in the area outside of NYC. Ultimately, this research seeks to determine if composting has the potential to become as integral to household habits and municipal policies as recycling has become.