Psychological Science and Society Presidential Plenary Panel Session
Saturday, 30 May 2026, 19:00 – 20:00 (7:00 pm – 8:00 pm)
New Directions in AI and Large Language Models
In the last three years, most of us have felt our scientific world shift. Artificial intelligence and LLMs are changing the ways we think about statistics, predictions, theories, and human understanding. Let’s have a chat about this.
The Psychological Science and Society plenary session is made possible by generous support from the Alan Kraut-Jane Steinberg Family Fund (KSFF).
Moderator: Jamie Pennebaker, APS President, The University of Texas at Austin, USA
Speakers:
Collective Behavior of AI Agents: Risks and Opportunities
David Garcia, University of Konstanz, Germany
AI agents use large language models to guide their behavior, acting on behalf of humans and interacting with other AI agents in unregulated crowds. Garcia and colleagues’ recent research has unveiled how the behavior of AI agents is influenced by their interactions with other agents—and how that can lead to the emergence of spontaneous coordination at scales larger than what humans can informally achieve. This helped the researchers find issues of collective misalignment of AI agents in which, given the right conditions, they can follow trends that go against the values they were originally trained for. The collective behavior of AI agents brings risks, such as potential manipulation of democracies by AI swarms, but also opportunities, including the simulation of human collective behavior to design better online platforms and policies.
Shaping the Machines Shaping Ourselves: New Frontiers Across the Psychological Sciences
Ryan Boyd, University of Texas at Dallas, USA
Large language models (LLMs) have moved from novelty to infrastructure in just a few years: embedded in search, writing, education, healthcare, workplaces, and daily decision-making. As they permeate human experience, their role in daily life extends far beyond providing information; they shape attention, habits, judgments, and relationships, creating new psychological environments at scale. In this session, Boyd will argue that the key question is not whether LLMs are impressive, but how psychology can help steer what they become and what they do to us. Today’s artificial intelligence (AI) is powerful enough that it should be used both for psychological science and as a central object of psychological science: as tools that can accelerate measurement and theory testing, and as social-cognitive systems that people increasingly rely on, learn from, and form relationships with. Studying the intersection of humans and AI is not a niche topic but, rather, the study of psychology itself.
How AI Sees Us: Psychological and Societal Implications of AI
Nuria Oliver, ELLIS Alicante Foundation, Spain
Edward Thorndike identified the “halo effect” in 1920: A single positive trait, like physical attractiveness, biases broader judgments. A century of research confirms that attractive individuals are perceived as more intelligent, trustworthy, and capable. Despite this awareness, the bias persists, and it is now embedded in AI systems at scale. Recent work shows that AI beauty filters amplify, rather than reduce, attractiveness bias by aligning faces with dominant beauty norms. Multimodal AI models also exhibit the halo effect, rating more attractive faces as more competent and sociable. Generative AI goes further, producing more attractive faces for positive traits and less attractive ones for negative traits, embedding bias in content creation itself. These distortions propagate into downstream systems, worsening performance for underrepresented groups. The issue is systemic: AI inherits human biases and amplifies them. Psychology’s extensive bias research remains largely unused in AI governance, raising urgent questions about accountability.
