Data Ethics Club

Welcome! Data Ethics Club is a discussion group/"journal" club about doing data science ethically. "Journal" because we will also read blog posts, (parts of) books, or watch videos. The organisers are based in Bristol, but the club is open to all. To stay in the loop, please join our mailing list. You can see the dates of our upcoming meetings here.

While you're here, you may also want to:

• enjoy our reading list.
• suggest reading materials.
• get involved in organising.

Stay in the loop

The main way to keep informed about upcoming meetings is to join our ✨ ➡️ Data Ethics Club Mailing List ⬅️ ✨ !

We also advertise meetings:

• Through the Jean Golding Institute mailing list - where you can also hear about other Data Science Events.
• By putting the dates right here in this repository.
• I'll tweet about it at #DataEthicsClub.

Get Involved

If you're interested in joining us in organising, please email us or ping us a message on our favourite data science Discord- you don't need to be in Bristol.

Organisers

You can also contact us separately:

• Nina Di Cara - twitter, email
• Natalie Thurlby - twitter, email

How to make suggestions

If you'd like to make a suggestion for what to read, please do whatever you'd prefer:

• make an issue - use the reading suggestion(s) template.
• email us
• edit this README and make a Pull Request (PR)

Reading list

Suggestions for reading:

• Review into bias in algorithmic decision making - suggested by @nataliethurlby¹
  • Suggested excerpt: executive summary - reading material for the 20th January 2021 meeting
  • Related articles:
    • Accountability for algorithms: a response to the CDEI review into bias in algorithmic decision-making - suggested by Tom Whittaker
    • Artificial intelligence and transparency in the public sector - suggested by Tom Whittaker
• Towards Decolonising Computional Science - suggested by @nataliethurlby
• Google's AI principles - suggested by @milliams
• Ethical principles in machine learning and artificial intelligence: cases from the field and possible ways forward
• Introduction to Research Ethics - suggested by @leriomaggio
• The Tyranny of Structurelessness
• Design Justice: Towards an Intersectional Feminist Framework for Design Theory and Practice
• A toolkit for centering racial equity throughout data integration¹
• Measuring the predictability of life outcomes with a scientific mass collaboration - #3 - suggested by @ninadicara
• Hacking the cis-tem
• A feature not a bug
• What is Data Ethics?¹
• Algorithmic Injustices: Towards a relational ethics - suggested by @RobertArbon
• The Financial Modelers Manifesto - #2 - suggested by @ninadicara
• The Missing Datasets Project - #1 - suggested by @ninadicara
• Social Biases in NLP Models as Barriers for Persons with Disabilities
• Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings - suggested by @leriomaggio
• Gender Bias and Sexism in Language - suggested by @leriomaggio
• Collectors, Allies, and Nightlights, Oh My
• Why Open Source misses the point of Free Software
• Which programming languages use the least electricity?
• Machine decisions and human consequences
• Privacy is power
• Bropenscience is Broken Science - suggested by @nataliethurlby
• Paths to Social License for Tracking-data Analytics
• Ethics can't be a side hustle
• A manifesto for team science
• Algorithms Of Oppression¹ - suggested by @mtwest2718
  • Suggested excerpt: Social Inequality Will Not Be Solved By An App
• Reflections on trusting trust
• Why data is never raw
• Ethical data science¹
• Fairness and utilization in allocating resources with uncertain demand - suggested by @edwinrobots
• What does it mean to 'solve' the problem of discrimination in hiring? - suggested by @edwinrobots
• Automating Inequality¹ - suggested by Valentina Ragni and @mtwest2718
  • A hippocratic oath for data science
  • We created poverty, algorithms won't make that go away
  • Algorithms designed to fight poverty can actually make it worse
• Explainable machine learning: how can you determine what a party knew or intended when a decision was made by machine-learning? - suggested by Tom Whittaker
• On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? - suggested by @ninadicara

Suggestions for viewing/listening/experiencing

• Critical perspectives on computer vision
• Data Feminism Book Club¹
  • Chapter 7: "Show your work"
  • The description of "co-liberatory" projects in chapter 5: "Unicorns, janitors, ninjas, wizards and rock stars"
• AI, Ain’t I a Woman? - poem²
• How normal am I? - interactive

• [1]: A longer piece of work, we'd need to choose a chapter or section.
• [2]: A shorter piece of work, perhaps to combine with something else

Contributors ✨

Thanks goes to these wonderful people (emoji key):

NatalieThurlby 📋 🤔 🎨 🖋	Nina 📋 🤔 🖋 🚇	WestByNoreaster 🖋	robertarbon 🎨 🖋	Valentina Ragni 🖋	Matt Williams 🖋	Miranda Mowbray 🖋
Edwin Simpson 🖋	Valerio Maggio 🖋

Emoji Key

This project follows the all-contributors specification, with some small tweaks to fit our purposes! We use...

• 🖋 for submitting suggestions to the "reading" list (suggestions can be any medium!)
• 💬 for leading the discussion in a meeting
• 📋 for organising
• 🤔 for ideas and planning of the group
• 🎨 for visual design (e.g. logo)
• 🚇 for infrastructure (e.g. bots, CI)