Expert-Generated Privacy Q&A Dataset for Conversational AI and User Study Insights

TL;DR

This paper introduces a new privacy Q&A dataset created by experts to improve transparency in conversational AI, demonstrating enhanced clarity and legal preciseness over existing solutions through linguistic analysis and user studies.

Contribution

The paper presents a novel expert-generated privacy Q&A dataset designed for conversational AI, developed with legal and design input, and validated through linguistic and user evaluations.

Findings

Proposed answers improve usability and clarity.

Dataset achieves legal preciseness.

Enhances accessibility of data processing info.

Abstract

Conversational assistants process personal data and must comply with data protection regulations that require providers to be transparent with users about how their data is handled. Transparency, in a legal sense, demands preciseness, comprehensibility and accessibility, yet existing solutions fail to meet these requirements. To address this, we introduce a new human-expert-generated dataset for Privacy Question-Answering (Q&A), developed through an iterative process involving legal professionals and conversational designers. We evaluate this dataset through linguistic analysis and a user study, comparing it to privacy policy excerpts and state-of-the-art responses from Amazon Alexa. Our findings show that the proposed answers improve usability and clarity compared to existing solutions while achieving legal preciseness, thereby enhancing the accessibility of data processing information for Conversational AI and Natural Language Processing applications.