AI Audit Tool Database Analysis

Authors: Victor Ojewale, Ryan Steed, Briana Vecchione, Abeba Birhane, Deb Raji

Developers: Ryan Steed, Victor Ojewale

This tool powers the landscape visualization and data analysis for the Open Source Audit Tooling project, including results in our paper, "Towards AI Accountability Infrastructure: Gaps and Opportunities in AI Audit Tooling."

Installation

make venv

Analysis & Replication

All code used to analyze our database and produce the results in our paper can be found in analysis.Rmd, drawing on code from utils.R. All analysis requires a cleaned and pre-processed version of our Airtable database, produced using the instructions below. data/airtable.csv contains the most recent copy of our Airtable database.

Generating data for analysis

1. [Optional] Download most recent Airtable CSV -> data/airtable.csv.
2. [Optional] Obtain access to Crunchbase and Github data using the instructions below. If you do not have access to Crunchbase data or a Github personal access token, you can skip this data with the options --no-crunchbase or --no-github. You will also need to skip code chunks requiring variables beginning with gh_ or cb_ in analysis.Rmd.
3. Run python clean.py pivot data/airtable.csv. This endpoint explodes the Taxonomy field into three levels, cleans the data, and joins in Github and Crunchbase data. Cleaned output is stored in output/airtable_for_pivot.csv.

Github data

Add your Github personal access token to secrets.json, with permission to read public repositories. The file should look like this:

{
    "github_token": "YOUR_TOKEN"
}

clean.py pivot will scrape from Github using this token.

Crunchbase data

Crunchbase does not allow us to redistribute the data we used for our analysis. To obtain a copy of your own, request access to the Crunchbase Research Access program or buy a subscription to Crunchbase Pro.

Note that you may need to change variable names in analysis.Rmd if the Crunchbase schema has changed.

Crunchbase Pro (Recommended)

1. Create a file of organization names and domains needed, based on data/airtable.csv (e.g., crunchbase/cb_query.csv).
2. Use Crunchbase's import function to create a List of those organizations.
3. Download all available columns to crunchbase, creating separate files (crunchbase/cb-query_*.csv) for companies, investors, and schools.

Research Access (does not include some variables---e.g. revenue data)

With research access, download the Daily CSV Export. Using sqlite3, import organizations.csv:

.mode csv
.import PATH_TO_CSV organizations
.save organizations.db

Then, use the flag --from-sql in calls to clean.py pivot.

Landscape Visualization

To generate YAML for the landscape visualization,

1. [Optional] Download most recent Airtable CSV -> data/airtable.csv.
2. Run python clean.py yaml data/airtable.csv ../landscape.yml.

For Mac, you may need to additionally install graphviz:

brew install graphviz
pip install --global-option=build_ext --global-option="-I/usr/local/Cellar/graphviz/8.0.5/include/"  --global-option="-L/usr/local/Cellar/graphviz/8.0.5/lib/" pygraphviz

Contents

• clean.py: Script for cleaning and joining data from Airtable, Github, and Crunchbase.
  • clean.py pivot: Clean and join data for analysis.
  • clean.py yaml: Clean and join data used to generate landscape visualization.
• analysis.Rmd: R Markdown file for generating plots and results used in our paper.
• utils.R: R utility functions for generating plots and results used in our paper.
• data/
  • airtable.csv: Most recent copy of our Airtable database.
  • taxonomy.json: JSON copy of our taxonomy tree.
• output/
  • airtable_for_pivot.csv: Cleaned and joined data for analysis.Rmd.
  • landscape.yml: YAML for landscape visualization.
• crunchbase/: Directory for storing Crunchbase data and (optionally) Crunchbase query file.