AIMII Workshop at IASEAI'26

Generative AI models like LLMs are transforming how we create and access information while also raising concerns about manipulation, deception, and the integrity of public discourse at unprecedented scale.

The AI Manipulation and Information Integrity (AIMII) workshop will bring together researchers from computer science, cognitive science, philosophy, political science, and policy to clarify core concepts, evaluate the evidence on AI's persuasive and manipulative capabilities, and explore implications for society and democracy.

The workshop will feature three panel discussions with leading researchers as well as a poster/lightning talk session showcasing new work from the broader community.

Call for submissions + Apart Research hackathon

We are soliciting submissions to present a poster or lightning talk at the first workshop on AI, Manipulation, & Information Integrity (AIMII) Workshop at IASEAI'26. There are two routes to presenting:

Abstract. You may submit a short (max 300 word) abstract using the form below. For submissions sent before January 10th, notifications will be returned by January 17th in order to allow time for registration and booking travel. We will consider submissions sent after this soft deadline but we cannot guarantee that they will be reviewed before the deadline for registration to the main IASEAI conference (Jan 27).

We welcome abstracts describing new work, work-in-progress, position statements, or summaries of recently published work. There are no formal proceedings.

Hackathon. To encourage work in the field, Apart Research will be hosting an AI Manipulation Hackathon on January 9th to 11th. The most promising projects from this hackathon will be awarded $2000 in cash prizes, invited to continue development through Apart Research's Fellowship and to present at their work at AIMII workshop. Details of the hackathon can be found here.

Topics

We welcome submissions on topics including (but not limited to):

Conceptual & Philosophical Foundations

• Definitions and taxonomies of persuasion, manipulation, and deception
• Moral and epistemic dimensions of AI influence
• Autonomy, consent, and the ethics of personalized persuasion
• Boundary cases and edge cases (e.g. when does influence become manipulation?)

Measurement & Evaluation

• Benchmarks and evaluations of persuasive or manipulative capabilities
• Ecological validity of current measurement approaches
• Sycophancy, reward hacking, and training dynamics that produce manipulative behaviors
• Detecting deception, sandbagging, or strategic behavior in AI systems
• Human studies of AI persuasion (attitude change, belief updating, behavioral effects)

Psychology & Cognitive Science

• Human susceptibility to AI-generated persuasion
• Trust, overreliance, and calibration in human-AI interaction
• Cognitive and affective mechanisms of AI influence
• Individual differences in vulnerability to AI manipulation

Societal & Political Impacts

• AI and misinformation/disinformation
• Effects on journalism, media ecosystems, and information environments
• Implications for democratic deliberation and political discourse
• Manipulation in AI companions, chatbots, and productivity tools
• Targeted advertising, recommender systems, and algorithmic influence

Mitigations & Governance

• Technical approaches to reducing manipulative capabilities or behaviors
• Transparency, disclosure, and labeling interventions
• Regulatory frameworks (EU AI Act, DSA, etc.) and their effectiveness
• Red-teaming, auditing, and third-party evaluation
• Platform governance and content moderation

Broader Perspectives

• Historical and comparative perspectives on information manipulation
• Human-AI co-evolution in communication
• Manipulation in multi-agent and agentic AI systems
• Dual-use concerns and beneficial applications of persuasive AI

Organizers

• Beba Cibralic
• Tijl De Bie
• Fosca Giannotti
• Luke Hewitt
• Cameron Jones
• Anna Monreale
• Dino Pedreschi