Paper
AI-Wrapped: Participatory, Privacy-Preserving Measurement of Longitudinal LLM Use In-the-Wild
Authors
Cathy Mengying Fang, Sheer Karny, Chayapatr Archiwaranguprok, Yasith Samaradivakara, Pat Pataranutaporn, Pattie Maes
Abstract
Alignment research on large language models (LLMs) increasingly depends on understanding how these systems are used in everyday contexts. yet naturalistic interaction data is difficult to access due to privacy constraints and platform control. We present AI-Wrapped, a prototype workflow for collecting naturalistic LLM usage data while providing participants with an immediate ``wrapped''-style report on their usage statistics, top topics, and safety-relevant behavioral patterns. We report findings from an initial deployment with 82 U.S.-based adults across 48,495 conversations from their 2025 histories. Participants used LLMs for both instrumental and reflective purposes, including creative work, professional tasks, and emotional or existential themes. Some usage patterns were consistent with potential over-reliance or perfectionistic refinement, while heavier users showed comparatively more reflective exchanges than primarily transactional ones. Methodologically, even with zero data retention and PII removal, participants may remain hesitant to share chat data due to perceived privacy and judgment risks, underscoring the importance of trust, agency, and transparent design when building measurement infrastructure for alignment research.
Metadata
Related papers
Vibe Coding XR: Accelerating AI + XR Prototyping with XR Blocks and Gemini
Ruofei Du, Benjamin Hersh, David Li, Nels Numan, Xun Qian, Yanhe Chen, Zhongy... • 2026-03-25
Comparing Developer and LLM Biases in Code Evaluation
Aditya Mittal, Ryan Shar, Zichu Wu, Shyam Agarwal, Tongshuang Wu, Chris Donah... • 2026-03-25
The Stochastic Gap: A Markovian Framework for Pre-Deployment Reliability and Oversight-Cost Auditing in Agentic Artificial Intelligence
Biplab Pal, Santanu Bhattacharya • 2026-03-25
Retrieval Improvements Do Not Guarantee Better Answers: A Study of RAG for AI Policy QA
Saahil Mathur, Ryan David Rittner, Vedant Ajit Thakur, Daniel Stuart Schiff, ... • 2026-03-25
MARCH: Multi-Agent Reinforced Self-Check for LLM Hallucination
Zhuo Li, Yupeng Zhang, Pengyu Cheng, Jiajun Song, Mengyu Zhou, Hao Li, Shujie... • 2026-03-25
Raw Data (Debug)
{
"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2602.18415v1</id>\n <title>AI-Wrapped: Participatory, Privacy-Preserving Measurement of Longitudinal LLM Use In-the-Wild</title>\n <updated>2026-02-20T18:34:23Z</updated>\n <link href='https://arxiv.org/abs/2602.18415v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2602.18415v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>Alignment research on large language models (LLMs) increasingly depends on understanding how these systems are used in everyday contexts. yet naturalistic interaction data is difficult to access due to privacy constraints and platform control. We present AI-Wrapped, a prototype workflow for collecting naturalistic LLM usage data while providing participants with an immediate ``wrapped''-style report on their usage statistics, top topics, and safety-relevant behavioral patterns. We report findings from an initial deployment with 82 U.S.-based adults across 48,495 conversations from their 2025 histories. Participants used LLMs for both instrumental and reflective purposes, including creative work, professional tasks, and emotional or existential themes. Some usage patterns were consistent with potential over-reliance or perfectionistic refinement, while heavier users showed comparatively more reflective exchanges than primarily transactional ones. Methodologically, even with zero data retention and PII removal, participants may remain hesitant to share chat data due to perceived privacy and judgment risks, underscoring the importance of trust, agency, and transparent design when building measurement infrastructure for alignment research.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.HC'/>\n <published>2026-02-20T18:34:23Z</published>\n <arxiv:primary_category term='cs.HC'/>\n <author>\n <name>Cathy Mengying Fang</name>\n </author>\n <author>\n <name>Sheer Karny</name>\n </author>\n <author>\n <name>Chayapatr Archiwaranguprok</name>\n </author>\n <author>\n <name>Yasith Samaradivakara</name>\n </author>\n <author>\n <name>Pat Pataranutaporn</name>\n </author>\n <author>\n <name>Pattie Maes</name>\n </author>\n </entry>"
}