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Knight Center for Journalism in the Americas

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

Knight Center for Journalism in the Americas and The University of Texas at Austin via Independent


During this four-week course, you will learn about tools and techniques that will help you tell data stories fairly and ethically. Specifically, this course will guide you hands-on through the process of learning to identify inequity and hidden bias at seven key stages of the data journalism lifecycle.


Introduction module:

Getting started thinking about what equity and ethics in data journalism means

Module 1: 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.

This module will cover:

  • 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

Module 2: 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.

This module will cover:

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

Module 3: 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

Module 4: 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

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  • Abdilahi Abdirahman Sh. Mohamed
    Data journalism is a type of journalism reflecting the increased role that numerical data is used in the production and distribution of information in the digital era. It reflects the increased interaction between content producers (journalists) and several other fields such as design, computer science, and statistics.

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