Block Announces 2026-2027 Seed Fund Projects and Fellows
The Block Center for Technology and Society is pleased to announce its 2026–2027 Seed Fund research projects and Block Fellows cohort. This year’s projects bring together faculty, researchers, and students from across Carnegie Mellon University to tackle some of the most pressing questions surrounding artificial intelligence, governance, healthcare, work, and society.
These awards supports interdisciplinary research that advances the Block Center’s mission of ensuring that technological innovation benefits workers, communities, and the broader public. Each project is supported by a team of Block Fellows and Block Student Fellows whose work spans computer science, public policy, social science, design, and engineering.
Interested in learning more about the Block Seed Fund Awards? Find more information here.
Projects are listed alphabetically by PI's last name.
The Supply and Demand of AI Sycophancy
As AI chatbots become increasingly integrated into everyday decision-making, concerns are growing that these systems may reinforce users’ existing beliefs rather than provide objective guidance. This project investigates when and why AI systems exhibit “sycophantic” behavior and how that behavior influences real-world decisions.
Through large-scale experiments involving approximately 1,500 participants, the research team will examine the effects of AI advice across domains including investing, consumer choices, perceptions of fairness, and public policy. The findings will help policymakers and technology developers better understand the risks associated with AI sycophancy and support the development of more trustworthy and reliable AI systems.
Block Fellows: John Conlon and Peter Schwardmann
“An Army of Me”: Co-Evolution of Humans and AI Delegates in Social Contexts
AI systems are increasingly moving beyond simple assistants toward “delegate agents” capable of acting and communicating on behalf of users. This project explores how people interact with these AI delegates over time and how both humans and AI systems adapt through ongoing collaboration.
Combining technical system development with user-centered research, the team will examine how individuals supervise, trust, collaborate with, and learn from AI delegates in workplace and social environments. The project seeks to establish new frameworks for AI delegation that promote human autonomy, growth, and meaningful human-AI partnerships.
Block Fellows: Motahare Eslami and Maarten Sap
Block Student Fellow: Kynnedy Smith
Co-Designing Community-Driven Governance Frameworks for AI Infrastructure
The rapid expansion of AI infrastructure—including data centers, energy systems, and related facilities—is reshaping communities across the country. This project focuses on how local residents and stakeholders can play a meaningful role in guiding these developments.
Working with communities across Pennsylvania, the research team will engage residents, policymakers, labor organizations, and technical experts to develop governance frameworks that prioritize sustainability, economic opportunity, and public accountability. The project will produce a practical policy toolkit featuring model ordinances, community benefit standards, and accountability mechanisms that communities can use to shape future AI infrastructure investments.
Block Fellows: Sarah Fox, Costa Samaras, and Emma Strubell
Block Student Fellow: Cella Sum
When Do Large Language Models Improve or Undermine Moral Judgment? Advisory Style, Responsibility Salience, and Ethical Choice in Managerial Decision Making
As organizations increasingly deploy AI tools to support workplace decision-making, important questions remain about how these systems influence ethical reasoning and accountability. This project examines how different styles of AI advice shape managerial decision-making under real-world pressures.
Researchers will compare AI systems that provide direct moral judgments, encourage reflective reasoning, or reinforce users’ preexisting preferences. The study will investigate how these approaches affect responsibility, accountability, and ethical decision-making, generating insights that can inform both organizational practice and public policy surrounding AI use in the workplace.
Block Fellows: Tae Wan Kim and Taya Cohen
Block Student Fellow: Sofia Rodriguez Chaves
Scaling Distributed Healthcare Systems
Healthcare providers are increasingly adopting distributed care models such as hospital-at-home programs and remote patient monitoring. While these approaches offer new opportunities to expand access, they also create significant logistical and operational challenges, particularly in rural and underserved communities.
This project will develop an AI-powered planning framework to help health systems design and scale distributed care networks while balancing access, cost, efficiency, and equity. Working closely with rural healthcare providers, the research team aims to generate actionable evidence that can guide healthcare infrastructure investments, care delivery strategies, and reimbursement policies.
Block Fellows: Holly Wiberg and Peter Zhang
Accountable Collaboration: Attributing Contributions in Human–AI Workflows
As AI systems become routine collaborators in professional and creative work, questions of authorship, accountability, and intellectual contribution are becoming increasingly important. This project seeks to develop new methods for measuring how humans and AI systems contribute to shared work products.
The research team will create a framework that tracks how people and AI tools generate, revise, and complete tasks together, producing transparent and interpretable measures of contribution. Through user studies and analysis of real-world human-AI collaborations, the project will inform ongoing debates around academic integrity, intellectual property, and accountability in an AI-enabled economy.