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Experts-in-the-Loop: Establishing an Effective Workflow in Crafting Privacy Q&A

arXiv 2311.11161 ↗First posted 18 Nov 2023cs.CLcited by 0

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Abstract (arXiv)

Privacy policies play a vital role in safeguarding user privacy as legal jurisdictions worldwide emphasize the need for transparent data processing. While the suitability of privacy policies to enhance transparency has been critically discussed, employing conversational AI systems presents unique challenges in informing users effectively. In this position paper, we propose a dynamic workflow for transforming privacy policies into privacy question-and-answer (Q&A) pairs to make privacy policies easily accessible through conversational AI. Thereby, we facilitate interdisciplinary collaboration among legal experts and conversation designers, while also considering the utilization of large language models' generative capabilities and addressing associated challenges. Our proposed workflow underscores continuous improvement and monitoring throughout the construction of privacy Q&As, advocating for comprehensive review and refinement through an experts-in-the-loop approach.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 18 of its 44 references that are in the map. First posted on arXiv 18 Nov 2023.

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References · 18

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  11. 2019Question Answering for Privacy Policies: Combining Computational and Legal Perspectivescited by 65
  12. 2023Prompted LLMs as Chatbot Modules for Long Open-domain Conversationcited by 61
6 more
  1. 2018Polisis: Automated Analysis and Presentation of Privacy Policies Using Deep Learningcited by 50
  2. 2023LLM-empowered Chatbots for Psychiatrist and Patient Simulation: Application and Evaluationcited by 33
  3. 2020PolicyQA: A Reading Comprehension Dataset for Privacy Policiescited by 31
  4. 2022Retrieval Enhanced Data Augmentation for Question Answering on Privacy Policiescited by 10
  5. 2021Privacy Policy Question Answering Assistant: A Query-Guided Extractive Summarization Approachcited by 5
  6. 2021Fairness-aware Summarization for Justified Decision-Makingcited by 3

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