An Empirical Study on Compliance with Ranking Transparency in the Software Documentation of EU Online Platforms

Welcome to the replication package for the ICSE-SEIS 2024 paper titled "An Empirical Study on Compliance with Ranking Transparency in the Software Documentation of EU Online Platforms".

Preprint available at: http://arxiv.org/abs/2312.14794

Abstract

Compliance with the EU's Platform-to-Business (P2B) Regulation is challenging for online platforms, and the assessment of their compliance is difficult for public authorities. This is partly due to the lack of automated tools for assessing the information platforms provide in their terms and conditions (i.e., software documentation), in relation to ranking transparency. That gap also creates uncertainty regarding the usefulness of such documentation for end-users. Our study tackles this issue in two ways. First, we empirically evaluate the compliance of six major platforms, revealing substantial differences in their documentation. Second, we introduce and test automated compliance assessment tools based on ChatGPT and information retrieval technology. These tools are evaluated against human judgments, showing promising results as reliable proxies for compliance assessments. Our findings could help enhance regulatory compliance and align with the United Nations Sustainable Development Goal 10.3, which seeks to reduce inequality, including business disparities on these platforms.

Repository Contents

This repository comprises various tools, scripts, and data sets essential for replicating the findings of our ICSE-SEIS 2024 paper. Here's a detailed breakdown:

setup_virtualenv.sh: A script designed to establish a virtual environment for the project, ensuring isolation and specific dependency versions.
run_automated_assessments.sh: A shell script crafted to execute the automated assessments elucidated in the research paper.
prolific_survey: This folder contains the data resulting from the large-scale manual assessment on Prolific involving 134 participants. For more details, read Section 6.2 of the paper.
expert_vs_gpt_vs_doxpy: This folder contains the code of DoXpert and the ChatGPT-based assessment tool. It also contains the data assessment results produced by the three experts and the the software documentation object of this study. For more details, read Sections 6.1 and 6.3 of the paper.
- code/data_analysis: Contains scripts dedicated to the analysis of the research data.
- code/doxpert: Houses the source code of the DoXpert tool.
- code/gpt_based_approach: Directory with scripts that implement the baseline tool leveraging ChatGPT. Inside the marketplaces and search_engines directories, you will find some pkl files containing the cached outputs of the queries to ChatGPT (v4 and v3.5). If you want to regenerate those outputs, cancel the pkl files and run the assessments.
- code/packages: Includes custom Python packages used throughout the project which are forked versions of:
  - DoXpy
  - DiscoLQA
- data/assessment_results: Contains the outcomes of the automated and experts evaluations of the technical documentation.
- data/platform_docs: This directory houses the software documentation data from three major online intermediation services (Amazon, Tripadvisor, and Booking) and three online search engines (Google, Bing, and Yahoo). Our selection was driven by representativeness and audience profile. For details on the number of links and average word count per document, refer to the table below.
- data/checklist: Features the checklist instrumental in evaluating the compliance of platform documentation with the P2B Regulation.

    Platform       | No. of Links | Avg. Words/Doc 
    ---------------|--------------|---------------
    Amazon         | 5            | 434.4
    Bing           | 16           | 964.06
    Booking        | 7            | 579.42
    Google         | 52           | 1679.5
    Tripadvisor    | 10           | 1653.9
    Yahoo          | 3            | 174

System Specifications

This repository is tested and recommended on:

OS: Linux (Debian 5.10.179 or newer) and macOS (13.2.1 Ventura or newer)
Python version: 3.7 or newer

Forked Repositories

This package uses forked versions of two repositories. The original repositories can be found at:

Environment Setup

In order to run the automated assessments, you need to install a proper environment. To do so, we offer two solutions.

Docker-based Solution

The first solution relies on Docker. Use the following command to download the image from Docker Hub:

docker pull francescosovrano/dox4p2bcompliance

After the download is complete, you can verify that the image is downloaded by using the command docker images, which will list all the Docker images available on your system.

Once the download is complete, run the following commands to create and start a new container:

docker run -it francescosovrano/dox4p2bcompliance bash

Shell Script Solution

Instead, if you don't want to use Docker, in order to create the necessary virtual environment and install the necessary dependencies, run the following script:

./setup_virtualenv.sh

If you cannot download the pytorch_model.bin file from Git LFS, download pytorch_model.bin from https://doi.org/10.5281/zenodo.10555603 and manually move it inside expert_vs_gpt_vs_doxpy/code/doxpert/question_extractor/data/models/distilt5-disco-qaamr-multi.

Installation of OpenAI Keys

To use this package, you must set up two environment variables: OPENAI_ORGANIZATION and OPENAI_API_KEY. These variables represent your OpenAI organization identifier and your API key respectively.

On UNIX-like Operating Systems (Linux, MacOS):

Open your terminal.

To set the OPENAI_ORGANIZATION variable, run:

export OPENAI_ORGANIZATION='your_organization_id'

To set the OPENAI_API_KEY variable, run:
```
export OPENAI_API_KEY='your_api_key'
```
These commands will set the environment variables for your current session. If you want to make them permanent, you can add the above lines to your shell profile (~/.bashrc, ~/.bash_profile, ~/.zshrc, etc.)

To ensure you've set up the environment variables correctly:

In your terminal or command prompt, run:
```
echo $OPENAI_ORGANIZATION
```
This should display your organization ID.
Similarly, verify the API key:
```
echo $OPENAI_API_KEY
```

Ensure that both values match what you've set.

Run the Automated Assessments

After setting up the environment, you can run the automated assessments using:

./run_automated_assessments.sh

The GPT-based assessments, specifically from line 9 to line 17 of the run_automated_assessments.sh script, will be the quickest due to the pre-cached output of GPT. On the other hand, DoXpert's assessments may take more time, depending on the number of CPUs and GPUs at your disposal. While a GPU is recommended, it is not mandatory.

We are currently using ChatGPT version 0613, which OpenAI may soon discontinue. If you wish to use different versions, you can easily modify the Python scripts found in the marketplaces and search_engines folders. Additionally, you should update the GPT model referenced in the instruct_model function within the model_manager.py script.

Upon completion of the script, you will find the assessment results in the directory code/doxpert/logs. This directory contains log files detailing the outcomes of the assessments.

Note: To reduce script verbosity and manage log output, please comment out lines 18 to 22 in the file located at expert_vs_gpt_vs_doxpy/code/doxpert/doxpert_assessment.py. Then, for a more readable format, you can convert these log files into CSV files. Move the log files to expert_vs_gpt_vs_doxpy/data/assessment_results/dox-based/log_files and then run the script expert_vs_gpt_vs_doxpy/data/assessment_results/dox-based/format_assessment_to_csv.py to perform the conversion.

Conclusion

We hope this repository serves as a valuable resource for researchers and practitioners aiming to understand and automate the assessment of regulatory compliance, particularly concerning the EU's P2B Regulation. Feedback and contributions are always welcome!

Citations

This code is free. So, if you use this code anywhere, please cite us:

@inproceedings{sovrano2024p2b,
  title={An Empirical Study on Compliance with Ranking Transparency in the Software Documentation of EU Online Platforms},
  author={Sovrano, Francesco and Lognoul, Michaël and Bacchelli, Alberto},
  booktitle={2024 IEEE/ACM 46th International Conference on Software Engineering: Software Engineering in Society (ICSE-SEIS)},
  year={2024},
  organization={IEEE}
}

Thank you!

Support

For any problem or question, please contact me at cesco.sovrano@gmail.com

Francesco-Sovrano / Automating-Regulatory-Compliance-An-Empirical-Study-on-Ranking-Transparency-of-EU-Online-Platforms