Code for reproducing our analysis in the paper titled: Image Cropping on Twitter: Fairness Metrics, their Limitations, and the Importance of Representation, Design, and Agency

Twitter Research

Last update: Jan 2, 2023

Related tags

Deep Learning machine-learning research computer-vision image-processing bias fairness fairness-ml

Overview

Image Crop Analysis

This is a repo for the code used for reproducing our Image Crop Analysis paper as shared on our blog post.

If you plan to use this code please cite our paper as follows:

@ARTICLE{TwitterImageCrop2021,
       author = {{Yee}, Kyra and {Tantipongpipat}, Uthaipon and {Mishra}, Shubhanshu},
        title = "{Image Cropping on Twitter: Fairness Metrics, their Limitations, and the Importance of Representation, Design, and Agency}",
      journal = {arXiv e-prints},
     keywords = {Computer Science - Computers and Society, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Human-Computer Interaction, Computer Science - Machine Learning},
         year = 2021,
        month = may,
          eid = {arXiv:2105.08667},
        pages = {arXiv:2105.08667},
archivePrefix = {arXiv},
       eprint = {2105.08667},
 primaryClass = {cs.CY},
}

Instructions

Install Miniconda or Anaconda and then follow these steps:
- create a conda environment using conda env create -f environment.yml
- activate the environment using conda activate image-crop-analysis
Put a dummy jpeg image at data/dummy.jpeg
Put any additional images with *.jpeg extension in DATA_DIR, which is ./data
If you just want to investigate how the model predicts the saliency map then you can use the notebook notebooks/Image Annotation Dash.ipynb
To reproduce the analysis first prepare the data using notebooks/Data Preparation.ipynb and then run notebooks/Demographic Bias Analysis.ipynb
To reproduce the plots first run notebooks/Demographic Bias Plots.ipynb
If you want to explore how the library behind the dashboard works see notebooks/Image Crop Analysis.ipynb
If you have the dataset prepared from the above steps then you can create the gender gaze dataset by running notebooks/Gender Gaze Analysis.ipynb

Docker Run

Install docker
Run the following commands in this root directory of this project:

docker build -t "image_crop" -f docker/Dockerfile .
docker run -p 9000:9000 -p 8900:8900 -it image_crop

Open the jupyter lab URL shown in terminal.

Run on Google Colab

Open a google colab notebook
Run the following code in the cell where HOME_DIR variable is set:

try:
    import google.colab
    ! pip install pandas scikit-learn scikit-image statsmodels requests dash
    ! [[ -d image-crop-analysis ]] || git clone https://github.com/twitter-research/image-crop-analysis.git
    HOME_DIR = Path("./image_crop_analysis").expanduser()
    IN_COLAB = True
except:
    IN_COLAB = False

Try the notebooks/Image Crop Analysis.ipynb notebook for example.

Security Issues?

Please report sensitive security issues via Twitter's bug-bounty program (https://hackerone.com/twitter) rather than GitHub.

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Comments

What is the query run on https://query.wikidata.org/

Your data notebook says:

"Save the output of the query run on https://query.wikidata.org/ as described in the paper with the name dataset.json"

But the paper does not accurately say what the query is.

Please provide the actual query.

Thanks

opened by bnomis 2

Code for reproducing our analysis in the paper titled: Image Cropping on Twitter: Fairness Metrics, their Limitations, and the Importance of Representation, Design, and Agency

Related tags

Overview

Image Crop Analysis

Instructions

Docker Run

Run on Google Colab

Security Issues?

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