# Polisis: Automated Analysis and Presentation of Privacy Policies Using Deep Learning

arXiv: https://arxiv.org/abs/1802.02561 · HTML: https://mlsanity.com/paper/1802.02561/

## Key facts

- Cited by 50 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Feb 2018.

## Built on by (most cited first, 30 of 50)

- [A Statutory Article Retrieval Dataset in French](https://mlsanity.com/paper/2108.11792/) (2021)
- [Perturbing Inputs for Fragile Interpretations in Deep Natural Language Processing](https://mlsanity.com/paper/2108.04990/) (2021)
- [NoiseQA: Challenge Set Evaluation for User-Centric Question Answering](https://mlsanity.com/paper/2102.08345/) (2021)
- [Intent Classification and Slot Filling for Privacy Policies](https://mlsanity.com/paper/2101.00123/) (2021)
- [PolicyGPT: Automated Analysis of Privacy Policies with Large Language Models](https://mlsanity.com/paper/2309.10238/) (2023)
- [Large Language Models: A New Approach for Privacy Policy Analysis at Scale](https://mlsanity.com/paper/2405.20900/) (2024)
- [An In-Depth Investigation of Data Collection in LLM App Ecosystems](https://mlsanity.com/paper/2408.13247/) (2024)
- [Retrieval Enhanced Data Augmentation for Question Answering on Privacy Policies](https://mlsanity.com/paper/2204.08952/) (2022)
- [Empowering Users in Digital Privacy Management through Interactive LLM-Based Agents](https://mlsanity.com/paper/2410.11906/) (2024)
- [Privacy Policy Question Answering Assistant: A Query-Guided Extractive Summarization Approach](https://mlsanity.com/paper/2109.14638/) (2021)
- [PLUE: Language Understanding Evaluation Benchmark for Privacy Policies in English](https://mlsanity.com/paper/2212.10011/) (2022)
- [Helping Johnny Make Sense of Privacy Policies with LLMs](https://mlsanity.com/paper/2501.16033/) (2025)
- [SkillScope: Toward Fine-Grained Least-Privilege Enforcement for Agent Skills](https://mlsanity.com/paper/2605.05868/) (2026)
- [Creation and Analysis of an International Corpus of Privacy Laws](https://mlsanity.com/paper/2206.14169/) (2022)
- [Automating Governing Knowledge Commons and Contextual Integrity (GKC-CI) Privacy Policy Annotations with Large Language Models](https://mlsanity.com/paper/2311.02192/) (2023)
- [Unfair TOS: An Automated Approach using Customized BERT](https://mlsanity.com/paper/2401.11207/) (2024)
- [PolicyLR: A Logic Representation For Privacy Policies](https://mlsanity.com/paper/2408.14830/) (2024)
- [Privacy Policies Across the Ages: Content and Readability of Privacy Policies 1996--2021](https://mlsanity.com/paper/2201.08739/) (2022)
- [A Human-in-the-Loop Approach for Information Extraction from Privacy Policies under Data Scarcity](https://mlsanity.com/paper/2305.15006/) (2023)
- [Entailment-Driven Privacy Policy Classification with LLMs](https://mlsanity.com/paper/2409.16621/) (2024)
- [A Longitudinal Measurement of Privacy Policy Evolution for Large Language Models](https://mlsanity.com/paper/2511.21758/) (2025)
- [Legally Binding but Unfair? Towards Assessing Fairness of Privacy Policies](https://mlsanity.com/paper/2403.08115/) (2024)
- [C3PA: An Open Dataset of Expert-Annotated and Regulation-Aware Privacy Policies to Enable Scalable Regulatory Compliance Audits](https://mlsanity.com/paper/2410.03925/) (2024)
- [Towards Safer Chatbots: Automated Policy Compliance Evaluation of Custom GPTs](https://mlsanity.com/paper/2502.01436/) (2025)
- [Word-level Annotation of GDPR Transparency Compliance in Privacy Policies using Large Language Models](https://mlsanity.com/paper/2503.10727/) (2025)
- [Let's Measure the Elephant in the Room: Facilitating Personalized Automated Analysis of Privacy Policies at Scale](https://mlsanity.com/paper/2507.14214/) (2025)
- [Jurisdiction as Structural Barrier: How Privacy Policy Organization May Reduce Visibility of Substantive Disclosures](https://mlsanity.com/paper/2601.20792/) (2026)
- [PrivPRISM: Automatically Detecting Discrepancies Between Google Play Data Safety Declarations and Developer Privacy Policies](https://mlsanity.com/paper/2603.09214/) (2026)
- [Measuring Google AI Overviews: Activation, Source Quality, Claim Fidelity, and Publisher Impact](https://mlsanity.com/paper/2605.14021/) (2026)
- [Detecting Compliance of Privacy Policies with Data Protection Laws](https://mlsanity.com/paper/2102.12362/) (2021)

## Abstract (arXiv)

Privacy policies are the primary channel through which companies inform users about their data collection and sharing practices. These policies are often long and difficult to comprehend. Short notices based on information extracted from privacy policies have been shown to be useful but face a significant scalability hurdle, given the number of policies and their evolution over time. Companies, users, researchers, and regulators still lack usable and scalable tools to cope with the breadth and depth of privacy policies. To address these hurdles, we propose an automated framework for privacy policy analysis (Polisis). It enables scalable, dynamic, and multi-dimensional queries on natural language privacy policies. At the core of Polisis is a privacy-centric language model, built with 130K privacy policies, and a novel hierarchy of neural-network classifiers that accounts for both high-level aspects and fine-grained details of privacy practices. We demonstrate Polisis' modularity and utility with two applications supporting structured and free-form querying. The structured querying application is the automated assignment of privacy icons from privacy policies. With Polisis, we can achieve an accuracy of 88.4% on this task. The second application, PriBot, is the first freeform question-answering system for privacy policies. We show that PriBot can produce a correct answer among its top-3 results for 82% of the test questions. Using an MTurk user study with 700 participants, we show that at least one of PriBot's top-3 answers is relevant to users for 89% of the test questions.