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Self-Directed Course

Equity & ethics in data journalism: Hands-on approaches to getting your data right

Instructor(s):   Heather Krause
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This resource page features course content from the Knight Center for Journalism in the America‘s massive open online course (MOOC) titled “Equity & ethics in data journalism: Hands-on approaches to getting your data right.” The four-week course took place from June 22 - July 19, 2020. We are now making the content free and available to students who took the course and anyone else who’s interested in practical and conceptual foundations of embedding equity and ethics in data journalism stories.

with support from:

kflogo

The course was taught by Heather Krause. She created and curated the content for the course, which includes video classes, readings, exercises, and more.

 The course materials are broken up into five modules:

  • Intro Module: Introduction to the course and the outline of topics
  • Module 1: Essential concepts in equity and ethics for data journalism
  • Module 2: Gathering and collecting data for your data story
  • Module 3: Analyzing data for your data story
  • Module 4: Visualizing and communicating data for your data story

We encourage you to watch the videos, review the readings, and complete the exercises as time allows. The course materials build off each other, but the videos and readings also act as standalone resources that you can return to overtime.

We hope you enjoy the materials. If you have any questions, please contact us at journalismcourses@austin.utexas.edu.

Meet the Instructor

Heather Krause HeadshotHeather Krause, PStat, is a data scientist and storyteller with over a decade of experience building tools that improve our ability to access, use and make sense of data. She has a strong love of finding data, analyzing it in creative ways, finding impactful stories, and using cutting edge visualization methods to show the results. Her emphasis is on combining strong statistical analysis with clear and meaningful communication. She is currently working on implementing tools for equity and ethics in data. As the founder of two successful data science companies, she attacks the largest questions facing societies today, working with both civic and corporate organizations to improve outcomes and lives. Her relentless pursuit of clarity and realism in these projects pushed her beyond pure analysis to mastering the entire data ecosystem including award-winning work in data sourcing, modelling, and data storytelling, each incorporating bleeding edge theory and technologies.

Her work proves that data narratives can be meaningful to any audience from a boardroom to the front page. Heather is the founder of We All Count, a project for equity in data working with teams across the globe to embed a lens of ethics into their data products from funding to data collection to statistical analysis and algorithmic accountability. Her unique set of tools and contributions have been sought across a range of clients from MasterCard and Wells Fargo to the United Nations, the Canadian Government, and the Bill and Melinda Gates Foundation. She is on the Data Advisory Board of the UNHCR. She is also the Chief Data Scientist at Orb Media.

Essential concepts in equity and ethics for data journalism

 Introduction

1. Welcome video

Watch Video   

2. Course syllabus

Syllabus 

 Materials

1. Not Your Average Average [YouTube]

2. Why I’m not making COVID19 visualizations, and why you (probably) shouldn’t either By Deborah Stone William Chase

3. The Ethics of Counting By  [Cambridge University Press]

4. How to prevent confirmation bias affecting your journalism [OnlineJournalismBlog]

Essential concepts in equity and ethics for data journalism

In this module, students will get familiar with the basic ideas, language, and applications of ethics and equity in data journalism. We will look at some examples, learn some definitions, and discuss key guidelines.

 In this module you will learn:

  • Key concepts in equity and ethics, such as privacy, consent, power, error, and bias
  • The seven steps of the data equity lifecycle
  • Libraries of guidelines

 Video Classes

1. Is Data Objective?

Watch Video  Transcript 

2. The Seven Steps in the Data Equity Framework

Watch Video  Transcript 

3. Not Your Average Average

Watch Video

 Readings

 Optional Resources

1. Transparency, Interactivity, Diversity, and Information Provenance in Everyday Data Journalism By Rodrigo Zamith [Digital Journalism Journal]

2. To Post or Not to Post: Online Discussion of Gun Permit Mapping and the Development of Ethical Standards in Data Journalism By David Craig, Stan Ketterer & Mohammad Yousuf [Journalism & Mass Communication Quarterly]

3. International Codes of Ethics [Accountable Journalism]

4. Overcoming Bias: A Journalist’s Guide to Culture and Context By Sue Ellen Chrisian

Gathering and collecting data for your data story

In this module, we’ll explore what you need to know and think about in acquiring data for your journalism. We’ll learn ways to vet data that you get from other people as well as ways to collect your own data with an equity and ethics focus. 

 In this module you will learn:

  • Data biographies
  • Samples and populations
  • Weighting data
  • Public vs private vs open data
  • Checklist for ethical data collection and acquisition

 Video Classes

1. How to Understand the Data You’re Working With

Watch Video  Transcript 

2. What is a Good Enough Sample?

Watch Video  Transcript 

 Readings

 Optional Resources

1. Public Info Doesn’t Always Want to Be Free By Matt Waite [Source]

2. How to Verify Data Quality By Giannina Segnini [GIJN]

3. Times Magazine Editor on ‘Creative Apocalypse’ Article By Margaret Sullivan [New York Times]

Analyzing data for your data story

Despite its name, “data science” is not an objective science. All methods of analysis embed a set of world views and value systems. We’ll look at how to avoid common errors in analysis and what questions to ask when assessing other people’s analysis for your data journalism pieces. 

 This module will cover:

  • The four most common data fallacies
  • Denominators
  • Part of a statistical model
  • Algorithmic accountability

 Video Classes

1. Common Mistakes in Data Analysis

Watch Video  Transcript 

2. Causal Mistakes in Data

Watch Video  Transcript 

3. Simpson’s Paradox in Data Journalism

Watch Video  Transcript 

4. Prosector’s Fallacy in Data Journalism

Watch Video  Transcript 

5. Ethics and Equity in Algorithms

Watch Video  Transcript 

 Readings

1. Science Isn’t Broken It’s Just a Hell of A Lot Harder than We Give It Credit For By Christie Aschwanden [FiveThirtyEight]

2. How Statistics can be Misleading [YouTube]

3. How to Report Numbers in the News By Sarah Marshall [journalism.co.uk]

4. The Algorithms Beat [DataJournalism]

 Optional Resources

1. Statistically Sound Data Journalism By Jonathan Stray [Source]

2. The Dark Arts of Statistical Deception [The New York Times]

3. Data Journalism, Impartiality And Statistical Claims By Stephen Cushion, Justin Lewis & Robert Callaghan  [Journalism Practice Journal]

Visualizing and communicating data for your data story 

Data visualization “best practices” are not cross-culturally universal. It is extremely easy to send unintentional, accidentally dishonest or misleading messages when visualizing data. We’ll be looking at ways to avoid these pitfalls and checklists and tools to help embed a sense of equity in the way you communicate and visualize your data journalism story.

 This module will cover:

  • Learning to spot how data viz misleads
  • Understanding how to use a legend to embed equity in data viz
  • Do’s and Don’t of ethical and equitable narrative and word choices

 Video Classes

1. Best Practices in Ethical Data Viz

Watch Video  Transcript 

2. Interpretation in Data Journalism

Watch Video  Transcript 

3. Equity in Icons and Symbols

Watch Video  Transcript 

4. Course Wrap up

Watch Video  Transcript 

 Readings

 Optional Resources

1. A quiz game on causal words

2. List of causal verbs, nouns, and other phrases

3. Being honest with causal language in writing for publication By Deependra K. Thapa, Denis C. Visentin, Glenn E. Hunt, Roger Watson, and Michelle Cleary [Journal of Advanced Nursing]