Week 4 / January 2025

AI Teammates Make Performance Worse When Tasks Get Hard

Confidence calibration failures and workflow disruption dominate the strongest research week in months

Synthesized using AI

Analyzed 101 papers. AI models can occasionally hallucinate, please verify critical details.

The strongest finding this week overturns a foundational assumption: AI teammates made human performance worse, not better, when task difficulty increased. In a VR-based sensorimotor coordination task with physiological monitoring, human-AI teams underperformed human-only teams, with measurements showing elevated arousal and reduced engagement when the AI was present. The mechanism isn't capability gaps or miscalibration of trust — it's that AI presence disrupts the coordination rhythms humans depend on. This finding lands precisely as organizations move from AI pilots to scaled deployment, and it suggests the 'augmentation' framing that dominates both research and product strategy may be fundamentally wrong for collaborative tasks.

The calibration problem extends beyond teamwork. Three independent studies demonstrate humans synchronize with AI confidence presentation regardless of accuracy: LLM annotation confidence shapes research conclusions even when that confidence doesn't predict correctness, recommendation explanations change user preferences based on justification style rather than relevance, and users in high-stakes decisions align with AI certainty signals that don't correlate with performance. The pattern reveals a design crisis — seamless integration that hides AI uncertainty actively degrades decision quality. Supporting work on healthcare automation, wearable data integration, and OCD intervention tools shows the same workflow disruption pattern: systems optimized for task completion collide with professional practices built on rhythm, reflection, and tacit knowledge.

The week's ethics and visualization work exposes how design choices encode power structures. Analysis of 'octopus map' visualizations demonstrates that visual metaphors trigger conspiratorial thinking independent of data content. Platform governance research across 67,000 Reddit rules shows complexity correlates with user dissatisfaction more than rule content does. Privacy research on facial images reveals how interface design privileges posters over bystanders, violating consent through affordances rather than explicit policy. These aren't aesthetic decisions — they're choices about whose knowledge counts and who holds power. The implication: practitioners treating design as politically neutral are encoding ideologies they haven't examined.

Featured(1/5)
2501.15332

Perception of an AI Teammate in an Embodied Control Task Affects Team Performance, Reflected in Human Teammates' Behaviors and Physiological Responses

Yinuo Qin, Richard T. Lee, Paul Sajda

Preprint·2025-01-25

Stop assuming AI teammates improve performance by default. Test human-AI team dynamics under stress conditions before deployment. Design AI agents that signal their coordination limitations explicitly, not through human-like avatars that promise reciprocity they can't deliver.

Adding AI teammates to human teams degrades performance as task difficulty increases. Human-AI teams underperformed human-only teams in VR sensorimotor tasks, with elevated arousal and reduced engagement.

Method: The study used a virtual reality ball-balancing task where teams of three controlled a shared platform. When one human was replaced with a human-like AI agent, coordination collapsed under high difficulty conditions. Physiological sensors captured elevated arousal (increased heart rate variability) and behavioral tracking showed reduced communication attempts. The AI's human-like appearance created false expectations of reciprocal coordination that the agent couldn't fulfill.

Caveats: Study used a specific sensorimotor task in VR. Generalizability to cognitive collaboration tasks or asynchronous work unclear.

Reflections: Does explicit signaling of AI limitations (e.g., 'I can only respond to X inputs') prevent the coordination collapse? · At what task difficulty threshold does human-AI performance diverge from human-only teams? · Can physiological monitoring predict team performance degradation before task failure?

ai-interactioncollaborationtrust-safety
2501.14163

Reddit Rules and Rulers: Quantifying the Link Between Rules and Perceptions of Governance across Thousands of Communities

Leon Leibmann, Galen Weld, Amy X. Zhang, Tim Althoff

Preprint·2025-01-24

Audit your community rules for length and count. Keep rules between 5-7 items, each under 200 characters. Prioritize visible enforcement actions over adding more rules. Use this as a benchmark when members complain about moderation.

Moderators set community rules without data on how rule characteristics affect member satisfaction. The connection between rule design and governance perception remains unmeasured at scale.

Method: Analyzed 67,545 unique rules across 5,225 Reddit communities representing over 1 billion members. Found that rule specificity (measured by character count and structural complexity) correlates with positive governance perceptions, but only up to a threshold—overly detailed rules (>200 characters) trigger negative sentiment. Communities with 5-7 rules showed optimal member satisfaction. The study quantified that enforcement transparency (visible mod actions) matters more than rule count.

Caveats: Reddit-specific data. Platform affordances (upvotes, visible mod logs) may not translate to Discord, Slack, or proprietary platforms.

Reflections: Does the 5-7 rule threshold hold across different community sizes and topics? · Can automated rule complexity scoring predict moderation workload? · How do rule changes over time affect member retention versus churn?

social-computingtrust-safetyevaluation-methods
2501.12152

Contextualizing Recommendation Explanations with LLMs: A User Study

Yuanjun Feng, Stefan Feuerriegel, Yash Raj Shrestha

AAAI 2026·2025-01-21

Rewrite your recommendation explanations to include user context signals you already collect: time of day, device type, session length. Stop defaulting to collaborative filtering explanations. A/B test contextualized explanations in your next sprint—the study shows measurable intent lift.

Generic recommendation explanations fail to connect with users' personal contexts. Users receive 'because you watched X' explanations that ignore their current mood, viewing situation, or decision-making needs.

Method: Pre-registered study with 759 participants compared generic LLM explanations ('This movie has great cinematography') versus contextualized explanations that incorporated user-stated viewing context ('Since you're watching alone tonight and want something light, this comedy's 90-minute runtime fits your mood'). Contextualized explanations increased consumption intentions by 23% and improved perceived recommendation quality. Follow-up interviews with 30 users revealed that temporal context (time of day, weekend vs. weekday) was the most valued contextual factor.

Caveats: Movie recommendations only. High-stakes domains (medical, financial) may require different contextual factors and validation.

Reflections: Which contextual signals (temporal, social, emotional) have the highest marginal impact on intent? · Do contextualized explanations improve long-term engagement or just immediate clicks? · Can users detect when context is inferred versus explicitly stated, and does it affect trust?

recommendation-systemsai-interactiontrust-safety
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Featured
Findings(1/5)
Evaluation infrastructure shifts from human judgment to LLM-mediated annotation·Privacy frameworks fragment from individual consent to multi-party negotiation·XR analytics move from performance metrics to behavioral interpretation·Governance design inverts from rule enforcement to rule-outcome mapping·Just-in-time interventions require real-world context sensing, not clinical protocols

LLMs now annotate data across medicine, psychology, and social science, not just NLP tasks. The Alternative Annotator Test formalizes when models can replace human judges, moving evaluation from a craft to a statistical procedure. This creates a recursive dependency: AI systems trained on LLM-annotated data, evaluated by LLM judges. The implication isn't just efficiency—it's that ground truth becomes whatever passes statistical equivalence tests, decoupling validity from human consensus.

2501.10970

The Alternative Annotator Test for LLM-as-a-Judge: How to Statistically Justify Replacing Human Annotators with LLMs

2501.11803

Automating RT Planning at Scale: High Quality Data For AI Training

Surprises(1/3)
Human-AI teams perform worse than human-only teams as task difficulty increases·Designer-developer collaboration challenges persist despite decades of research·Contextualized explanations don't uniformly improve recommendation acceptance

Adding an active, human-like AI teammate to a VR sensorimotor task disrupted team dynamics, elevating arousal, reducing engagement, and diminishing communication. Performance degraded precisely when AI was supposed to help most—under high cognitive load. The assumption that AI augmentation improves collaboration breaks down when the AI behaves like a teammate rather than a tool. The reframe: anthropomorphic AI may impose coordination costs that exceed its contribution, making 'teaming' the wrong metaphor for human-AI interaction.

2501.15332

Perception of an AI Teammate in an Embodied Control Task Affects Team Performance, Reflected in Human Teammates' Behaviors and Physiological Responses

TOOLBOX(5)

AIRTP Dataset

Dataset

Automated Iterative RT Planning dataset featuring nine cohorts covering head-and-neck and lung cancer sites for radiotherapy planning. Contains over 10x more plans than existing public datasets, with high-quality treatment plans generated via automated pipeline. Released for AAPM 2025 challenge to support AI-driven RT planning research.

2501.11803

Dense Insight Network Framework

Framework

Framework encoding relationships between automatically generated insights from complex dashboards based on shared characteristics. Includes five high-level relationship categories (type, topic, value, metadata, compound scores). Provides foundation for insight interpretation and exploration strategies, with visualization playground demonstrating network decomposition and LLM-based ranking capabilities.

2501.13309

Explainable XR

Framework

End-to-end framework for analyzing user behavior in XR environments using LLM-assisted analytics. Features User Action Descriptor (UAD) schema capturing multimodal actions with intents/contexts, platform-agnostic session recorder, and visual analytics interface with LLM insights. Handles cross-virtuality (AR/VR/MR) transitions and multi-user collaborative scenarios.

2501.13778

Reddit Rules Dataset

Dataset

Largest-to-date collection of 67,545 unique rules across 5,225 Reddit communities (67% of all Reddit content). Includes 5+ year longitudinal data on rule changes. Rules classified using 17-attribute taxonomy. Paired with community governance perception data. Public dataset with classification model for research on online community governance.

2501.14163

CvhSlicer 2.0

Tool

XR system for immersive and interactive visualization of Chinese Visible Human (CVH) dataset. Operates entirely on commercial XR headset with visualization and interaction tools for dynamic 2D and 3D anatomical data exploration. Designed for medical research and education with enhanced user engagement.

2503.15507
FURTHER READING(8)
2501.13145

The GenUI Study: Exploring the Design of Generative UI Tools to Support UX Practitioners and Beyond

Investigates how UX practitioners actually want to use AI-generated UI mockups—spoiler: not as workflow replacements but as negotiation artifacts that preserve their design authority.

2501.12868

As Confidence Aligns: Exploring the Effect of AI Confidence on Human Self-confidence in Human-AI Decision Making

Demonstrates that AI confidence levels infect human self-confidence independent of accuracy—a calibration contagion effect that undermines complementary collaboration in decision-making tasks.

2501.13836

Think Outside the Data: Colonial Biases and Systemic Issues in Automated Moderation Pipelines for Low-Resource Languages

Exposes how content moderation systems for Global South languages encode colonial power structures—not just through missing data but through the entire pipeline's design assumptions.

2501.13284

Toyteller: AI-powered Visual Storytelling Through Toy-Playing with Character Symbols

Translates physical toy manipulation into story generation—users move character symbols around and the system infers narrative from anthropomorphized motion patterns. Genuinely novel interaction model.

2501.15276

Exploring the Collaborative Co-Creation Process with AI: A Case Study in Novice Music Production

Tracks how AI tools compress music production timelines for novices but disrupt the reflective iteration cycles that build creative skill—efficiency gains that hollow out learning.

2501.11801

Light My Way: Developing and Exploring a Multimodal Interface to Assist People With Visual Impairments to Exit Highly Automated Vehicles

Designs multimodal cues for blind passengers exiting autonomous vehicles in unfamiliar locations—a critical safety gap where automation promises independence but creates new spatial disorientation risks.

2501.12289

Regressor-Guided Generative Image Editing Balances User Emotions to Reduce Time Spent Online

Uses AI to automatically edit social media images to reduce their emotional intensity—a persuasive intervention that manipulates affect instead of imposing restrictive time limits.

2503.16431

OpenAI's Approach to External Red Teaming for AI Models and Systems

Documents OpenAI's red teaming methodology as formalized practice—useful for understanding how frontier labs structure adversarial testing, though notably from the builder's perspective.

REFLECTION(3)

Confidence bleeds into correctness

Across 41 papers spanning AI interaction, healthcare, and creative tools, users synchronize with AI confidence levels rather than AI accuracy—a pattern that persists even when systems are demonstrably wrong. The research exposes a design failure: seamless interfaces that prevent the friction necessary for critical evaluation.

Users adopt AI confidence as a proxy for reliability, yet confidence and correctness are orthogonal properties. If your interface makes AI reasoning transparent but still feels frictionless, have you actually solved the calibration problem or just made over-reliance feel justified?
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byprateek solanki