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

• URL: http://arxiv.org/abs/2311.11161v1
• Date: Sat, 18 Nov 2023 20:32:59 GMT
• Title: Experts-in-the-Loop: Establishing an Effective Workflow in Crafting Privacy Q&A
• Authors: Zahra Kolagar, Anna Katharina Leschanowsky, Birgit Popp
• Abstract summary: We propose a dynamic workflow for transforming privacy policies into privacy question-and-answer (Q&A) pairs. Thereby, we facilitate interdisciplinary collaboration among legal experts and conversation designers. Our proposed workflow underscores continuous improvement and monitoring throughout the construction of privacy Q&As.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

Related papers

• Collection, usage and privacy of mobility data in the enterprise and public administrations [55.2480439325792]
  Security measures such as anonymization are needed to protect individuals' privacy. Within our study, we conducted expert interviews to gain insights into practices in the field. We survey privacy-enhancing methods in use, which generally do not comply with state-of-the-art standards of differential privacy.
  arXiv  Detail & Related papers  (2024-07-04T08:29:27Z)
• Privacy Risks of General-Purpose AI Systems: A Foundation for Investigating Practitioner Perspectives [47.17703009473386]
  Powerful AI models have led to impressive leaps in performance across a wide range of tasks. Privacy concerns have led to a wealth of literature covering various privacy risks and vulnerabilities of AI models. We conduct a systematic review of these survey papers to provide a concise and usable overview of privacy risks in GPAIS.
  arXiv  Detail & Related papers  (2024-07-02T07:49:48Z)
• Centering Policy and Practice: Research Gaps around Usable Differential Privacy [12.340264479496375]
  We argue that while differential privacy is a clean formulation in theory, it poses significant challenges in practice. To bridge the gaps between differential privacy's promises and its real-world usability, researchers and practitioners must work together.
  arXiv  Detail & Related papers  (2024-06-17T21:32:30Z)
• GoldCoin: Grounding Large Language Models in Privacy Laws via Contextual Integrity Theory [44.297102658873726]
  Existing research studies privacy by exploring various privacy attacks, defenses, and evaluations within narrowly predefined patterns. We introduce a novel framework, GoldCoin, designed to efficiently ground LLMs in privacy laws for judicial assessing privacy violations. Our framework leverages the theory of contextual integrity as a bridge, creating numerous synthetic scenarios grounded in relevant privacy statutes.
  arXiv  Detail & Related papers  (2024-06-17T02:27:32Z)
• Advancing Differential Privacy: Where We Are Now and Future Directions for Real-World Deployment [100.1798289103163]
  We present a detailed review of current practices and state-of-the-art methodologies in the field of differential privacy (DP) Key points and high-level contents of the article were originated from the discussions from "Differential Privacy (DP): Challenges Towards the Next Frontier" This article aims to provide a reference point for the algorithmic and design decisions within the realm of privacy, highlighting important challenges and potential research directions.
  arXiv  Detail & Related papers  (2023-04-14T05:29:18Z)
• PLUE: Language Understanding Evaluation Benchmark for Privacy Policies in English [77.79102359580702]
  We introduce the Privacy Policy Language Understanding Evaluation benchmark, a multi-task benchmark for evaluating the privacy policy language understanding. We also collect a large corpus of privacy policies to enable privacy policy domain-specific language model pre-training. We demonstrate that domain-specific continual pre-training offers performance improvements across all tasks.
  arXiv  Detail & Related papers  (2022-12-20T05:58:32Z)
• The Evolving Path of "the Right to Be Left Alone" - When Privacy Meets Technology [0.0]
  This paper proposes a novel vision of the privacy ecosystem, introducing privacy dimensions, the related users' expectations, the privacy violations, and the changing factors. We believe that promising approaches to tackle the privacy challenges move in two directions: (i) identification of effective privacy metrics; and (ii) adoption of formal tools to design privacy-compliant applications.
  arXiv  Detail & Related papers  (2021-11-24T11:27:55Z)
• Privacy in Open Search: A Review of Challenges and Solutions [0.6445605125467572]
  Information retrieval (IR) is prone to privacy threats, such as attacks and unintended disclosures of documents and search history. This work aims at highlighting and discussing open challenges for privacy in the recent literature of IR, focusing on tasks featuring user-generated text data.
  arXiv  Detail & Related papers  (2021-10-20T18:38:48Z)
• Differentially Private Multi-Agent Planning for Logistic-like Problems [70.3758644421664]
  This paper proposes a novel strong privacy-preserving planning approach for logistic-like problems. Two challenges are addressed: 1) simultaneously achieving strong privacy, completeness and efficiency, and 2) addressing communication constraints. To the best of our knowledge, this paper is the first to apply differential privacy to the field of multi-agent planning.
  arXiv  Detail & Related papers  (2020-08-16T03:43:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.