Papers for

ai interface designers

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Human GenAI collaboration shows unique memory and trust patterns

The GenAI Who Knew Too Little: Revisiting Transactive Memory Systems in Human GenAI Collaboration

Abstract: Generative artificial intelligence (GenAI) is rapidly transforming knowledge work, creating new collaborative dynamics that challenge tradi-tional human-human interaction patterns. This study investigates how Transactive Memory Systems (TMS) manifest in human-GenAI collabora-tion. Through qualitative interpretive interviews with 14 knowledge work-ers and thematic analysis, we identified distinct TMS manifestations across its three main dimensions: (1) regarding specialization, expertise recogni-tion exhibited asymmetry. Humans developed awareness of GenAI capabili-ties while GenAI demonstrated only temporary recognition of human exper-tise; (2) in terms of credibility, humans formed ambivalent trust relation-ships with GenAI, simultaneously attributing expertise to the latter while implementing additional verification processes; and (3) concerning coor-dination, where humans primarily directed more passive GenAI systems lacking initiative, resulting in hierarchical interaction patterns and unidi-rectional information flow. Our findings reveal that TMS manifests differ-ently in human-GenAI compared to human-human collaboration, necessitat-ing theoretical adaptations including trust markers, acknowledging GenAI's absent metamemory, and identifying its temporary memory.

Tue 20 OctHuman-Computer InteractionComputers and Society
The gist
Working together with generative AI changes how people share knowledge and trust each other. The study found that humans understand what AI is good at, but AI only briefly remembers human skills. People partly trust AI but always double-check its outputs. Also, humans lead the interaction while AI mostly follows orders. The researchers say existing ideas about shared memory systems need adjusting to fit human-AI teamwork.
Open → 2610.28343v1

Users and AI share reasoning tasks with varying involvement preferences

What Did the AI Take On? Characterizing Cognitive Delegation in LLM Reasoning

Abstract: Large language models (LLMs) often perform intermediate cognitive work while carrying out users' requests, yet it remains unclear which parts users intended to delegate and how they wanted to remain involved. This matters because consequential choices may go unnoticed, limiting users' ability to steer the process, while reviewing every step would make delegation burdensome. We examined this with 24 LLM users across three knowledge-work tasks, collecting 992 retrospective annotations of reasoning steps. From this, we developed taxonomies of LLM cognitive work, delegation enactment, and desired delegation protocols at the reasoning-step level. Our analysis revealed that participants viewed about half of all steps (48.6%) as AI-initiated, meaning the AI took on work they had not requested. Desired involvement varied with cognitive work and delegation enactment, even when contributions matched participants' intent. We propose design implications and sketches for supporting more deliberate cognitive delegation through flexible protocols and inspectable, revisable AI-initiated decisions.

Mon 5 OctHuman-Computer Interaction
The gist
When people use large language models (LLMs) like AI helpers, the AI often does extra thinking steps that users didn’t directly ask for. The authors studied how users feel about which thinking tasks the AI should do and how much they want to be involved. They found that users saw almost half of the AI’s actions as taking initiative without being told. People want different levels of participation depending on the type of work and how the AI took charge. The authors suggest ways to design AI systems so users can better control and review what the AI does.
Open → 2610.06328v1

Prompts shape how AI models follow default choices in group games

Prompt framing governs LLM default following in collective-action

Abstract: Large language models are increasingly deployed as agents that make or recommend decisions on behalf of users, often operating through interfaces that pre-fill suggested values or default options. Whether models treat such defaults as merely informational or as suggestions that systematically alter their choices remains unclear. We study default deference in two one-shot social dilemmas: a common-pool resource (CPR) extraction game and a threshold public-good (TPG) contribution game. We measure how defaults shift each model's choice distribution relative to its no-default baseline, across default values, wordings, and action-space granularities. We find that pre-filled defaults pull probability mass on the default value in both games, but the magnitude depends strongly on wording: the same model can show high pull under one formulation and near-zero pull under another. Permission-style wording reduces default pull in both games, more strongly in CPR than in TPG. Default pull is weaker in coarse action spaces, and conflict defaults attract more mass than agreement ones. These results indicate that default deference depends on the model, the wording of the interface, and the structure of available choices. For agentic systems, evaluating model behaviour without controlling the surrounding choice architecture can miss an important source of behavioural variation.

Fri 2 OctComputer Science and Game Theory
The gist
When AI language models are given a pre-filled choice, like a suggested answer in a game, how much they follow that choice depends on how it's worded and what options they have. The authors tested this with games where people share resources or work together. They found the AI is more likely to pick the default if it's presented as a suggestion rather than just information. This means the way choices are shown affects AI decisions, which is important when these models help make or recommend decisions.
Open → 2610.03253v1