# Question Answering for Privacy Policies: Combining Computational and Legal Perspectives

Source: [Question Answering for Privacy Policies: Combining Computational and Legal Perspectives](<https://www.sota2.com/research/paper/68fa669b1e209131cea115f7>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:54:21.040Z

Authors: Abhilasha Ravichander, Alan W Black, Shomir Wilson, Thomas Norton, Norman Sadeh

Published: 2019-11-03T06:25:17.000Z

## Abstract

Privacy policies are long and complex documents that are difficult for users to read and understand, and yet, they have legal effects on how user data is collected, managed and used. Ideally, we would like to empower users to inform themselves about issues that matter to them, and enable them to selectively explore those issues. We present PrivacyQA, a corpus consisting of 1750 questions about the privacy policies of mobile applications, and over 3500 expert annotations of relevant answers. We observe that a strong neural baseline underperforms human performance by almost 0.3 F1 on PrivacyQA, suggesting considerable room for improvement for future systems. Further, we use this dataset to shed light on challenges to question answerability, with domain-general implications for any question answering system. The PrivacyQA corpus offers a challenging corpus for question answering, with genuine real-world utility.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1911.00841>)

[PDF](<https://arxiv.org/pdf/1911.00841.pdf>)