# Data Feminism Book Club 2024

__What are we reading?__ [Data Feminism by Catherine D'Ignazio and Lauren F. Klein](https://mitpress.mit.edu/9780262547185/data-feminism/)

We will be holding weekly meetings over the summer to help us all stay accountable and to discuss
our thoughts on _Data Feminism_ - a classic data ethics text that many of us have probably had on our
`to read` lists for too long!  

**Update - we had a brilliant time reading this book! We've included the questions we used for our discussions below in case they are useful to anyone else organising a similar group.**

## Discussion Questions
### Introduction

First we did an activity to introduce ourselves called
'The Story of My Name'. 
[Find the instructions on how to run this activity here](https://onehe.org/eu-activity/introductions-story-of-your-name/). 
We went into breakout rooms of 5-ish people to do this, but on reflection could have done it with up to 20 people in the main room. 

Then we discussed: 
- What brings you to the reading group?  
- What does feminism mean to you?  

### Chapter 1 - The Power Chapter

- Have you seen the Matrix of Domination before? What did you think - do you feel you understand it?  
- The chapter outlines issues with under-representation in data, and over-representation. What are your thought on the tensions between these?  
- In your current role, or examples from your personal life, how does data play in? Who does it benefit - and does it align with the science, surveillance, selling groups?  

### Chapter 2 - Collect, Analyze, Imagine, Teach

- Have you heard of 'counterdata' before - how might it apply in your areas of lived experience or expertise?  
- Imagine - what would it look like to be a Data Justice Club, rather than a Data Ethics Club? (See Table 2.1)
- What would need to change for projects like Local Lotto to be used more in teaching data skills?

### Chapter 3 - On Rational, Scientific, Objective Viewpoints from Mythical, Imaginary, Impossible Standpoints

- What does it mean to you to 'Elevant Emotion and Embodiment' in data visualisation and/or data generally?  
- What did you think of the examples of how positionality is expressed in data visualisations (intended or not) using different graph styles, colour or annotations - do you have any reflections from your personal context?
- What did you think of the reactions of NYT readers to the election gauge? How should we represent uncertainty?

### Chapter 4 - "What Gets Counted Counts"

- How can we make sure we are using classification when its needed, but not enforcing unnecessary distinctions when its not?  
- Have you come across the paradox of exposure before? Do you have examples of it being addressed well, or badly?
- Do you have any good examples of quantitative data supporting qualitative data or vice versa?

### Chapter 5 - Unicorns, Janitors, Ninjas, Wizards, and Rock Stars

- In your career until now, have you come across stereotypes re: data analysts or working with data? To what degree do you think these hinder or support the work being done? How would you reframe the public image of the data analyst within your workplace to support the processes you would like to model?  
- The distance between analyst/researcher and the data itself can introduce significant bias in the process of analysis. Sometimes in our jobs we do not have the scope to aim for co-liberation. Do you have any examples of what can be done to bridge this distance in our regular work processes?  
- When thinking of co-liberation as the end goal of data analysis, which parts of the process do you find least intuitive to model for this outcome? Do you have examples of processes where you have strived for data for good or co-liberation?  

### Chapter 6 - The Numbers Don’t Speak for Themselves

- What problems does big data solve versus create?
- What are your thoughts on how we label graphs with context?
- Where does responsibility lie for proving data context in your area?  How does funding play in?  

### Chapter 7 - Show Your Work

- What other products' hidden labor would you like to see mapped like the Amazon Echo in Anatomy of an AI system?
- Do you have any examples of invisible labor that you do in your work?  
- How do you think invisible labor should be communicated? Could we do anything differently?  

### Conclusion - Now Let's Multiply

- What is your favourite new definition/thing you've learned about from the book?
- Is there anything you plan to do differently on the basis of what we've read?  

## Meeting Dates and Times 

### Dates

The best dates for most people were Mondays and Thursday so we will be having two weeks on each.  
The week beginning Monday 6th August there will not be a meeting. 
All meetings will be at 15:00 UTC - see below for timezone info!  

| Chapter             | Date                   |
|---------------------|------------------------|
| Introduction and introductions! | Monday 8th July        |
| Chapter 1           | Monday 15th July       |
| Chapter 2           | Thursday 25th July     |
| Chapter 3           | Thursday 1st August    |
| Chapter 4           | Monday 12th August     |
| Chapter 5           | Monday 19th August     |
| Chapter 6           | Thursday 29th August   |
| Chapter 7           | Thursday 5th September |
| Conclusion          | Monday 9th September   |

### Time

The meetings will be held at 15:00 UTC across all the dates. 
This is to try and get the best coverage across requested timezones which were: UK, Europe, Canada, USA and South America.

| Timezone                    | Local Meeting Time |
|-----------------------------|--------------------|
| Central European Time UTC+2 | 17:00              |
| British Summer Time UTC+1   | 16:00              |
| Eastern Daylight Time UTC-4 | 11:00              |
| Peru Time UTC-5             | 10:00              |
| Pacific Daylight Time UTC-7 | 07:00              |

Psst - Please raise an issue if you spot that any of these time conversions are incorrect!