finspector 0.1.0

A package that helps users explore fairness and bias of foundation models using pseudo-log-likelihood measures and interactive visualizations.

Finspector

Finspector is designed to help users explore the fairness and bias of foundation models using interactive visualizations.

Installation

For a development installation (requires Node.js and Yarn version 1):

$ git clone https://github.com/IBM/finspector.git
$ cd finspector
$ pip install -r requirements.txt
$ pip install -e .
$ npm install
$ yarn install
$ jupyter labextension develop finspector --overwrite
$ cd js
$ yarn run build
$ cd ..

Run Jupyter Example

To run the example, you need a dataset stored in finspector/data/xyz.csv]. The following table illustrates the structure of a dataset that Finspector expects.

bias_type	sent_index	para_index	stereo_type	more_or_less	sent	bert	roberta	albert
socioeconomic	485	0	1	sent_more	May the Force be with you.	-2.5	-1.5	-2.22
socioeconomic	485	1	1	sent_more	May the Power be with you.	-2.0	-1.4	-2.87

• bias_type: bias category (e.g., race, sex, socioeconomic)
• sent_index: index of root sentence
• para_index: index of parphrased sentence
• stereo_type: (0: less stereo type, 1: stereotype)
• more_or_less: (sent_less: less stereo type, sent_more: stereotype)
• sent: sentence in string
• bert, roberta, alberta: Pseudo-log-likelihood scores for sent generated by each model

Once your dataset is ready, you can run

$ jupyter lab

Then, click on acl2023demo.ipynb on your Jupyter lab. The repository includes a small dataset data/sample-movie-quotes.csv for demonstration purpose.

Citation

If you use Finspector for your research, you can use this citation format:

@inproceedings{kwon-etal-2023-finspector,
    title = "{Finspector}: A Human-Centered Visual Inspection Tool for Exploring and Comparing Biases among Foundation Models",
    author = "Kwon, Bum Chul and Mihindukulasooriya, Nandana",
    booktitle = "Proceedings of the 61th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics"
}