Pith. sign in

REVIEW 3 cited by

User Privacy Harms and Risks in Conversational AI: A Proposed Framework

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2402.09716 v1 pith:I4OMOQD5 submitted 2024-02-15 cs.HC cs.CY

User Privacy Harms and Risks in Conversational AI: A Proposed Framework

Ece Gumusel , Kyrie Zhixuan Zhou , Madelyn Rose Sanfilippo This is my paper
classification cs.HC cs.CY
keywords privacyframeworkchatbotstext-basedconcernsconversationalexistingharms
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
read the original abstract

This study presents a unique framework that applies and extends Solove (2006)'s taxonomy to address privacy concerns in interactions with text-based AI chatbots. As chatbot prevalence grows, concerns about user privacy have heightened. While existing literature highlights design elements compromising privacy, a comprehensive framework is lacking. Through semi-structured interviews with 13 participants interacting with two AI chatbots, this study identifies 9 privacy harms and 9 privacy risks in text-based interactions. Using a grounded theory approach for interview and chatlog analysis, the framework examines privacy implications at various interaction stages. The aim is to offer developers, policymakers, and researchers a tool for responsible and secure implementation of conversational AI, filling the existing gap in addressing privacy issues associated with text-based AI chatbots.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Tracking Conversations: Measuring Content and Identity Exposure on AI Chatbots

    cs.CR 2026-04 unverdicted novelty 7.0

    17 of 20 AI chatbots share conversation content or identifiers with third parties, including plaintext text sent to Microsoft Clarity via session replay in three cases.

  2. Tracking Conversations: Measuring Content and Identity Exposure on AI Chatbots

    cs.CR 2026-04 accept novelty 7.0

    17 of 20 AI chatbots share conversation content or identifiers with third parties, including plaintext prompt and response text with Microsoft Clarity in three cases.

  3. Security Considerations for Multi-agent Systems

    cs.CR 2026-03 unverdicted novelty 6.0

    No existing AI security framework covers a majority of the 193 identified multi-agent system threats in any category, with OWASP Agentic Security Initiative achieving the highest overall coverage at 65.3%.