About

Anita Williams Woolley is a Professor of Organizational Behavior and Theory at the Tepper School of Business. Her work is centered on research related to organizational behavior, leadership, and teamwork. She is involved in the academic community at Carnegie Mellon University, contributing to the faculty profile and engaging in research activities that explore how artificial intelligence and machine learning intersect with business, management science, and organizational behavior. Woolley's expertise and research focus on understanding the dynamics of organizations and the impact of technological advancements on business practices.

Research topics

• Computer Science
• Artificial Intelligence
• Psychology
• Knowledge management
• Social psychology
• Computer Security
• Political Science
• Sociology
• Microeconomics
• Management
• Epistemology
• Communication
• Cognitive psychology
• Data science
• Biology
• Economics
• Engineering

Selected publications

• Toward a science of human–AI teaming for decision making: A complementarity framework

  PNAS Nexus · 2026-02-19

  articleOpen accessSenior author

  As artificial intelligence (AI) becomes embedded in critical decisions involving health, safety, finance, and governance, the key challenge is no longer whether humans and AI will collaborate, but rather how to structure this collaboration to achieve true complementarity. Human-AI complementarity refers to the conditions under which human-AI teams outperform either humans alone or AI systems alone. This paper advances the science of human-AI teaming for decision making by integrating insights from cognitive science, AI, human factors, organizational behavior, and ethics. We propose a framework grounded in collective intelligence and anchored in the foundational cognitive processes-reasoning, memory, and attention-to understand and engineer effective human-AI teams. We examine the sociotechnical factors that shape team effectiveness, including team composition, trust calibration, shared mental models, training, and task structure. We then outline design principles for achieving complementarity: defining goals and constraints, partitioning roles, orchestrating attention and interrogation, building knowledge infrastructures, and establishing continuous training and evaluation. We conclude with theoretical, practical, and policy implications, emphasizing alignment with human values, accountability, and equity. Together, these insights offer a roadmap for building human-AI teams that are not only high-performing and adaptive, but also transparent, trustworthy, and fundamentally human-centered.

  PublisherDOI

• AI as Climate Technology: When AI Strengthens Collective Intelligence-and When it Does Not 1

  SSRN Electronic Journal · 2026-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Exploring the effects of real-time feedback on collaborative processes to enhance collective intelligence in teams

  Collective Intelligence · 2026-01-01

  articleOpen access

  Background A growing body of research has identified behavioral markers of collective intelligence to diagnose the quality of group collaboration in real time. Prior work suggests that real-time collaborative process metrics may be used not only to measure and predict the collective intelligence of a group, but also to improve team performance through targeted interventions. Purpose This research examines whether providing teams with real-time feedback on their collaborative process metrics improves performance and collective intelligence in a dynamic team task. Research Design We conducted two experiments in which teams collaborated on an online search and rescue task. Experimental conditions varied whether teams were exposed to real-time displays of their collaborative process metrics. Study Sample Participants were organized into teams and completed the task in an online experimental environment. Data Collection and/or Analysis We collected behavioral trace data capturing team interactions and computed real-time collaborative process metrics. Team performance and collective intelligence were analyzed across experimental conditions to assess the effects of feedback exposure. Results Contrary to expectations, real-time feedback did not uniformly improve collaboration quality or performance. In several instances, teams over-corrected their behavior in response to feedback on a single dimension, which negatively impacted other dimensions of collaboration and reduced task performance. Conclusion These findings suggest that real-time digital nudges aimed at improving collaboration can produce unintended consequences. Caution is warranted when deploying process-level, real-time interventions, as optimizing one collaborative dimension may disrupt others and undermine collective intelligence.

  PublisherDOI

• Capturing Emotions in an Era of AI: From Experiments to Ethnography

  Academy of Management Proceedings · 2025-07-01

  article

  Emotions are critical to understanding and managing organizational dynamics, whether in traditional human teams or in contexts involving advanced technologies. Extant research demonstrates that working with algorithms can elicit strong feelings such as stress or frustration. While leaders may at times prefer to sidestep emotions and dismiss them as “irrational,” in doing so they ignore the important signals emotions convey to their peril as well as the unintended negative consequences that unregulated emotions may cause. This panel symposium will engage an interdisciplinary group of scholars pioneering the study of emotions around AI in an interactive discussion. We will discuss (1) the methodological and ethical challenges associated with measuring emotions around AI and in vulnerable groups; (2) different qualitative and quantitative approaches on how to measure emotion and (3) provide an outlook on how emotional dynamics will change due to the increasing technological advancements of AI in the coming years. Scholars attending our symposium will gain a multidisciplinary understanding of the current challenges and methods for studying emotions and AI, as well as learn about appropriate ways to measure emotions around AI. Applying the gained knowledge in their future studies will enable management scholars to study how to preserve human-centric qualities such as emotional well-being, empathy and dignity when organizational stakeholders team-up with AI.

  PublisherDOI

• SOTOPIA-S4: a user-friendly system for flexible, customizable, and large-scale social simulation

  2025-01-01 · 4 citations

  articleOpen access

  Xuhui Zhou, Zhe Su, Sophie Feng, Jiaxu Zhou, Jen-tse Huang, Hsien-Te Kao, Spencer Lynch, Svitlana Volkova, Tongshuang Wu, Anita Woolley, Hao Zhu, Maarten Sap. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (System Demonstrations). 2025.

  PublisherOA PDFDOI

• Measuring Implicit Spatial Coordination in Teams: Effects on Collective Intelligence and Performance

  2025-07-29

  article
  PublisherDOI

• Beyond efficiency: Trust, AI, and surprise in knowledge work environments

  Computers in Human Behavior · 2025-02-12 · 5 citations

  articleOpen accessSenior author

  Contemporary management practices are often designed with the needs of knowledge-based workers in mind, but an increasingly pressing challenge today is how to manage and effectively handle non-routine work. This paper revisits the job characteristics model through the lens of self-determination theory, specifically in the context of algorithmic performance management. Non-routine work is inherently unpredictable, and individuals often struggle with prolonged uncertainty. However, automated interventions that help individuals make sense of their work in uncertain conditions may help overcome the challenges of non-routine work and increase worker performance. In a randomized, controlled experiment delivered in a novel online task environment, we find that automated, real-time feedback increases the perceived trustworthiness of an algorithmic performance rating under conditions of high task uncertainty. Our research demonstrates the potential of artificial intelligence to automate certain tasks in non-routine work environments that positively augment human work performance while simultaneously enhancing trust in these automated work systems. • Knowledge work is becoming more non-routine, making traditional management and motivation theories less applicable. •Non-routine work requires adaptive management strategies focused on sense-making. •Automated feedback can improve trust in algorithms by complementing human performance with real-time updates. •Reduced rating surprise through automated feedback increases perceived trustworthiness of algorithmic performance ratings, especially in uncertain tasks.

  PublisherDOI

• Teams

  2025-05-01 · 1 citations

  book-chapter1st authorCorresponding

  Research on teamwork is prominent or growing ... driven in part by the rapid rise in the use of teams in practically every area of work, as teams serve as engines of new idea development, problem-solving, and product and service delivery for organizations in every sector of society.

  PublisherDOI

• Machines in the Middle: Using Artificial Intelligence (AI) While Offering Help Affects Warmth, Felt Obligations, and Reciprocity

  Journal of Business and Psychology · 2025-10-31 · 2 citations

  articleOpen accessSenior author

  Abstract Theories of helping in the workplace are traditionally rooted in human interactions, often drawing from social exchange concepts. However, with the rise of artificial intelligence (AI), intelligent machines in work settings can now be leveraged in a growing number of areas. Given this change, we do not know how the use of AI-based tools will alter coworker perceptions and their subsequent responses to helping. Across two studies (and a replication), our results show that AI-assisted helping behavior, compared to unassisted help, is perceived as less warm, decreases felt obligations, and reduces one’s likelihood of reciprocating help. These results demonstrate how the integration of AI tools, which can enhance task efficiency and expand workers’ capabilities, might affect the social dynamics essential for effective cooperation. Theoretical and practical implications of these findings, as well as limitations, are discussed.

  PublisherOA PDFDOI

• Large-Scale Group Brainstorming and Deliberation Using Swarm Intelligence and Generative AI

  2025-01-01 · 3 citations

  articleOpen access
  PublisherDOI

Recent grants

• Collaborative Research: Measuring Collective Intelligence

  NSF · $188k · 2010–2012

• VOSS: Collaborative Research: Is Larger Smarter? Investigating the Effect of Group Size on Collective Intelligence

  NSF · $168k · 2013–2017

Frequent coauthors

• Christopher F. Chabris

  111 shared

• J. Richard Hackman

  Harvard Global Health Institute

  104 shared

• Stephen M. Kosslyn

  102 shared

• Sean L. Bennett

  Carnegie Mellon University

  101 shared

• Margaret E. Gerbasi

  Sage Therapeutics (United States)

  101 shared

• Jonathon P. Schuldt

  Cornell University

  101 shared

• Thomas E. Jerde

  Autism & Developmental Medicine Institute

  101 shared

• Jonathan Wai

  Arkansas Department of Education

  100 shared

Education

• Ph.D., Management Science

  Carnegie Mellon University

  2003

• M.S., Management Science

  Carnegie Mellon University

  1999

• B.S., Operations Research and Industrial Engineering

  University of North Carolina at Chapel Hill

  1995