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Read more about deon on the project homepage

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<h1><b>An ethics checklist for data scientists</b></h1>

deon is a command line tool that allows you to easily add an ethics checklist to your data science projects. We support creating a new, standalone checklist file or appending a checklist to an existing analysis in many common formats.

To help get started, deon includes a default Data Science Ethics Checklist along with a list of real-world examples connected with each item. Users can draw on the default list or develop their own.

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δέον • (déon) [n.] (Ancient Greek) <small><a href="https://en.wiktionary.org/wiki/%CE%B4%CE%AD%CE%BF%CE%BD#Ancient_Greek" target="_blank" style="text-decoration: none; color: #6d6d6d">wikitionary</a></small>

Duty; that which is binding, needful, right, proper.

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The conversation about ethics in data science, machine learning, and AI is increasingly important. The goal of deon is to push that conversation forward and provide concrete, actionable reminders to the developers that have influence over how data science gets done.

Quickstart

If you have uv available, you can use deon in one line! To write out the default checklist to a markdown file called ETHICS.md, simply run:

uvx deon -o ETHICS.md

Otherwise, first install deon:

pip install deon

Then, call deon to write out the default checklist to a markdown file called ETHICS.md:

deon -o ETHICS.md

Dig into the checklist questions to identify and navigate the ethical considerations in your data science project.

For more configuration details, see the sections on command line options, supported output file types, and custom checklists.

What is deon designed to do?

We created deon to help data scientists across the sector be more intentional in their choices, and more aware of the ethical implications of their work. We use that perspective to make decisions about contributions, issues, PRs, and other maintenance and support activities.

1. 🔓 Our goal is not to be arbitrators of what ethical concerns merit inclusion. We have a process for changing the default checklist, but we believe that many domain-specific concerns are not included and teams will benefit from developing custom checklists. Not every checklist item will be relevant. We encourage teams to remove items, sections, or mark items as N/A as the concerns of their projects dictate.

2. 📊 This checklist is designed to provoke conversations around issues where data scientists have particular responsibility and perspective. It's not up to data scientists alone to decide what the ethical course of action is. This has always been a responsibility of organizations that are part of civil society. Conversations should be part of a larger organizational commitment to doing what is right.

3. 💬 Items on the checklist are meant to provoke discussion among good-faith actors who take their ethical responsibilities seriously. We are working at a level of abstraction that cannot concretely recommend a specific action (e.g., "remove variable X from your model"). Because of this, most of the items are framed as prompts to discuss or consider. Teams will want to document these discussions and decisions for posterity.

4. 🌎 We believe in the power of examples to bring the principles of data ethics to bear on human experience. This repository includes a list of real-world examples connected with each item in the default checklist. We encourage you to contribute relevant use cases that you believe can benefit the community by their example. In addition, if you have a topic, idea, or comment that doesn't seem right for the documentation, please add it to the wiki page for this project!

5. 🔍 We believe the primary benefit of a checklist is ensuring that we don't overlook important work. Sometimes it is difficult with pressing deadlines and a demand to multitask to make sure we do the hard work to think about the big picture. This package is meant to help ensure that those discussions happen, even in fast-moving environments.

6. ❓ We can't define exhaustively every term that appears in the checklist. Some of these terms are open to interpretation or mean different things in different contexts. We recommend that when relevant, users create their own glossary for reference.

7. ✨ We want to avoid any items that strictly fall into the realm of statistical best practices. Instead, we want to highlight the areas where we need to pay particular attention above and beyond best practices.

8. ✅ We want all the checklist items to be as simple as possible (but no simpler), and to be actionable.

Sources

We built our initial list from a set of proposed items on multiple checklists that we referenced. This checklist was heavily inspired by an article written by Mike Loukides, Hilary Mason, and DJ Patil and published by O'Reilly: "Of Oaths and Checklists". We owe a great debt to the thinking that proceeded this, and we look forward to thoughtful engagement with the ongoing discussion about checklists for data science ethics.

Using this tool

<img src="https://s3.amazonaws.com/drivendata-public-assets/deon_demo.svg">

Prerequisites

• Python >3.8: Your project need not be Python 3, but you need Python 3 to execute this tool.

Installation

$ pip install deon

or

$ conda install deon -c conda-forge

Simple usage

We recommend adding a checklist as the first step in your data science project. After creating your project folder, you could run:

$ deon -o ETHICS.md

This will create a markdown file called ETHICS.md that you can add directly to your project.

For simple one-off analyses, you can append the checklist to a Jupyter notebook or RMarkdown file using the -o flag to indicate the output file. deon will automatically append if that file already exists.

$ jupyter notebook my-analysis.ipynb

...

$ deon -o my-analysis.ipynb  # append cells to existing output file

This checklist can be used by individuals or teams to ensure that reviewing the ethical implications of their work is part of every project. The checklist is meant as a jumping-off point, and it should spark deeper and more thourough discussions rather than replace those discussions.

Proudly display your Deon badge

You can add a Deon badge to your project documentation, such as the README, to encourage wider adoption of these ethical practices in the data science community.

HTML badge

<a href="http://deon.drivendata.org/">
    <img src="https://img.shields.io/badge/ethics%20checklist-deon-brightgreen.svg?style=popout-square" alt="Deon badge" />
</a>

Markdown badge

[![Deon badge](https://img.shields.io/badge/ethics%20checklist-deon-brightgreen.svg?style=popout-square)](http://deon.drivendata.org/)

Supported file types

Here are the currently supported file types. We will accept pull requests with new file types if there is a strong case for widespread use of that filetype.

• .txt: ascii
• .html: html
• .ipynb: jupyter
• .md: markdown
• .rmd: rmarkdown
• .rst: rst

Command line options

Usage: deon [OPTIONS]

  Easily create an ethics checklist for your data science project.

  The checklist will be printed to standard output by default. Use the --output
  option to write to a file instead.

Options:
  -l, --checklist PATH  Override default checklist file with a path to a custom
                        checklist.yml file.
  -f, --format TEXT     Output format. Default is "markdown". Can be one of
                        [ascii, html, jupyter, markdown, rmarkdown, rst].
                        Ignored and file extension used if --output is passed.
  -o, --output PATH     Output file path. Extension can be one of [.txt, .html,
                        .ipynb, .md, .rmd, .rst]. The checklist is appended if
                        the file exists.
  -w, --overwrite       Overwrite output file if it exists. Default is False,
                        which will append to existing file.
  -m, --multicell       For use with Jupyter format only. Write checklist with
                        multiple cells, one item per cell. Default is False,
                        which will write the checklist in a single cell.
  --help                Show this message and exit.

Default checklist

<hr class="checklist-buffer"/>

Data Science Ethics Checklist

A. Data Collection

• [ ] A.1 Informed consent: If there are human subjects, have they given informed consent, where subjects affirmatively opt-in and have a clear understanding of the data uses to which they consent?