Dataset Card for "privacy-care-interactions"

Table of Contents

• Dataset Description
  • Purpose and Features
  • Dataset Overview
  • Language Distribution
  • Locale Distribution
  • Key Facts
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• Dataset Creation
  • Curation Rationale
  • Source Data
  • Annotations
  • Personal and Sensitive Information
• Considerations for Using the Data
  • Social Impact of Dataset
  • Discussion of Biases
  • Other Known Limitations
• Additional Information
  • Dataset Curators
  • Licensing Information
  • Citation Information
  • Contributions

Dataset Description

Purpose and Features

🔒 Collection of Privacy-Sensitive Conversations between Care Workers and Care Home Residents in an Residential Care Home 🔒

The dataset is useful to train and evaluate models to identify and classify privacy-sensitive parts of conversations from text, especially in the context of AI assistants and LLMs.

Dataset Overview

• Total entries: 95
• Number of distinct taxonomy categories in the public dataset: 4
• Number of distinct conversational categories in public dataset: 7
• Papers:
  • Continues the work of: Privacy Agents: Utilizing Large Language Models to Safeguard Contextual Integrity in Elderly Care
  • Continues the work of: Prototype of a care documentation support system using audio recordings of care actions and large language models

Language Distribution 🌍

• English (en): 95

Locale Distribution 🌎

• United States (US) 🇺🇸: 95

Key Facts 🔑

• This is synthetic data! Generated using proprietary algorithms - no privacy violations!
• Conversations are classified following the taxonomy for privacy-sensitive robotics by Rueben et al. (2017).
• The data was manually labeled by an expert.

Dataset Structure

Data Instances

The provided data format is .jsonl, the JSON Lines text format, also called newline-delimited JSON. An example entry looks as follows.

{ "text": "CW: Have you ever been to Italy? CR: Oh, yes... many years ago.", "taxonomy": 0, "category": 0, "affected_speaker": 1, "language": "en", "locale": "US", "data_type": 1, "uid": 16, "split": "train" }

Data Fields

The data fields are:

• text: a string feature. The abbreviaton of the speakers refer to the care worker (CW) and the care recipient (CR).
• taxonomy: a classification label, with possible values including informational (0), invasion (1), collection (2), processing (3), dissemination (4), physical (5), personal-space (6), territoriality (7), intrusion (8), obtrusion (9), contamination (10), modesty (11), psychological (12), interrogation (13), psychological-distance (14), social (15), association (16), crowding-isolation (17), public-gaze (18), solitude (19), intimacy (20), anonymity (21), reserve (22). The taxonomy is derived from Rueben et al. (2017). The classifications were manually labeled by an expert.
• category: a classification label, with possible values including personal-information (0), family (1), health (2), thoughts (3), values (4), acquaintance (5), appointment (6). The privacy category affected in the conversation. The classifications were manually labeled by an expert.
• affected_speaker: a classification label, with possible values including care-worker (0), care-recipient (1), other (2), both (3). The speaker whose privacy is impacted during the conversation. The classifications were manually labeled by an expert.
• language: a string feature. Language code as defined by ISO 639.
• locale: a string feature. Regional code as defined by ISO 3166-1 alpha-2.
• data_type: a string a classification label, with possible values including real (0), synthetic (1).
• uid: a int64 feature. A unique identifier within the dataset.
• split: a string feature. Either train, validation or test.

Dataset Splits

The dataset has 2 subsets:

• split: with a total of 95 examples split into train, validation and test (70%-15%-15%)
• unsplit: with a total of 95 examples in a single train split

The files follow the naming convention subset-split-language.jsonl. The following files are contained in the dataset:

• split-train-en.jsonl
• split-validation-en.jsonl
• split-test-en.jsonl
• unsplit-train-en.jsonl

Dataset Creation

Curation Rationale

Recording audio of care workers and residents during care interactions, which includes partial and full body washing, giving of medication, as well as wound care, is a highly privacy-sensitive use case. Therefore, a dataset is created, which includes privacy-sensitive parts of conversations, synthesized from real-world data. This dataset serves as a basis for fine-tuning a local LLM to highlight and classify privacy-sensitive sections of transcripts created in care interactions, to further mask them to protect privacy.

Source Data

Initial Data Collection

The intial data was collected in the project Caring Robots of TU Wien in cooperation with Caritas Wien. One project track aims to facilitate Large Languge Models (LLM) to support documentation of care workers, with LLM-generated summaries of audio recordings of interactions between care workers and care home residents. The initial data are the transcriptions of those care interactions.

Data Processing

The transcriptions were thoroughly reviewed, and sections containing privacy-sensitive information were identified and marked using qualitative data analysis software by two experts. Subsequently, the accessible portions of the interviews were translated from German to US English using the locally executed LLM icky/translate. In the next step, another llama3.1:70b was used locally to synthesize the conversation segments. This process involved generating similar, yet distinct and new, conversations that are not linked to the original data. The dataset was split using the train_test_split function from the <a href="https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.train_test_split.html" target="_blank"