GenAIPABench-Dataset

GenAIPABench is a specialized dataset designed to evaluate Generative AI-based Privacy Assistants (GenAIPAs). These assistants aim to simplify complex privacy policies and data protection regulations, making them more accessible and understandable to users. The dataset provides a comprehensive framework for assessing the performance of AI models in interpreting and explaining privacy-related documents.

Components of the Dataset:

Privacy Documents:

Privacy Policies: The dataset includes five privacy policies from various organizations or services. These policies are selected to represent a range of industries and complexity levels.
Data Protection Regulations: It also contains two major data protection regulations (such as the EU's GDPR and California's CCPA), providing a legal context for evaluation.
Question Corpus:

Privacy Policy Questions: Contains 32 questions related to the privacy policies. These questions address key topics like data collection practices, data sharing, user rights, data security, and retention policies.
Regulation Questions: Includes 6 questions about data protection regulations, focusing on compliance requirements, user rights under the law, and organizational obligations.
Question Variations: Each question comes with paraphrased versions and variations to test the AI's ability to handle different phrasings and nuances.
Annotated Answers:
Expert-Curated Responses: Each question is accompanied by meticulously crafted answers provided by privacy experts.
Cross-Verification: Answers are cross-verified for accuracy and completeness, ensuring they align precisely with the source documents.
Purpose and Objectives:

Benchmarking GenAIPAs: Provides a standardized dataset for evaluating and comparing the effectiveness of different AI-based privacy assistants.
Improving AI Understanding of Privacy: Helps identify strengths and weaknesses in AI models regarding comprehension of privacy policies and regulations.
Enhancing User Experience: Aims to improve how AI assistants communicate complex privacy information to users, making it more accessible and actionable.
Usage Scenarios:

Academic Research: Researchers can use the dataset to study how AI models interpret and summarize legal and policy documents.
AI Development: Developers can train and fine-tune AI models to better handle privacy-related queries.
Policy Analysis Tools: Organizations can leverage the dataset to create tools that help users understand and navigate privacy policies.
Key Features:

Diverse Content: Covers a range of privacy documents and questions to ensure a comprehensive evaluation.
Expert Validation: Responses are verified by privacy experts, ensuring high-quality benchmarks.
Robust Testing Framework: The evaluator tool allows systematic testing under different scenarios and prompts.
Focus on Real-world Applicability: Questions are derived from user inquiries, FAQs, and online forums to reflect genuine user concerns.
Benefits:

Enhances Trustworthiness: The dataset helps improve user trust in AI assistants by promoting accuracy and clarity.
Supports Regulatory Compliance: Helps organizations ensure their AI tools provide information consistent with legal requirements.
Facilitates Transparency: Encourages AI models to provide transparent and reference-backed responses.

Introducing paper: GenAIPABench: A Benchmark for Generative AI-based Privacy Assistants