Online Training

Data Ethics: Designing for Fairness in the Age of Algorithms

Learn how to build fair and ethical data projects from the ground up

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Questions surrounding the ethical use of data are especially urgent today, as public awareness of biased software and algorithms continues to grow. Understanding the emerging landscape of data ethics and the procedures and institutions you can implement to handle ethical quandaries can help you and your company better confront these issues early on in the research or product design process.

This online seminar examines the critical and ethical issues surrounding data, focusing on three areas most relevant to understanding data and its social impact today: 1) research ethics, 2) privacy and security, and 3) fairness and equity.

In just two mornings, you’ll learn how to recognize and proactively respond to possible “red flags” in data projects, avoid unconscious bias, and build robust ethical data projects.

What you’ll learn—and how you can apply it

By the end of this live, online course, you’ll understand:

  • Recognize possible sources of bias or unfairness in data
  • Recognize patterns that relate to cultural bias
  • Think critically about issues of data in the world
  • Connect both data analysis and the development of algorithms to ethical issues
  • Be more effective and efficient in recognizing and responding to possible “red flags” in data-intensive and related projects

This course is for you if:

  • Your organization collects, stores, or analyzes personal data, and you need to recognize and proactively address ethical issues regarding the use of data
  • You are working to use data science for social good and you want to ensure your means are as ethical as your ends
  • You are a public or organizational policymaker and need to ensure your words and practices build toward positive outcomes

About your instructor

Anna Lauren Hoffmann

Anna Lauren Hoffmann is a trans woman and scholar working at the intersections of data, technology, culture, and ethics. She is currently a postdoctoral scholar with the School of Information at the University of California, Berkeley. She teaches both academic and practicing data scientists how to detect and predict the effects of their work, and to ensure that the ways we design, use, and speak about technology promotes the pursuit of important human values like respect and justice.

She also ties the history and development of research ethics and professional codes of ethics to current practice. She has written about these things in scholarly contexts as well as popular outlets.

“Anna Lauren Hoffmann gave the best overview of ethics in relation to technology and big data I have ever seen!”

“The tremendous @annaeveryday is incisive on Facebook’s new ethical review standards


  • Day One
    • Introduction: What problems or dilemmas are we facing today? (15 minutes)
    • Data are people are data! Connecting data and algorithms to humans and human values (75 minutes)
    • Break (15 minutes)
    • Automating exclusion: Why fairness, justice, and respect matter for data science and technology design (65 minutes)
    • Overnight assignment: Review case examples (10 minutes)
  • Day Two
    • Day one recap and discussion (15 minutes)
    • Thinking and doing in context: Social and political considerations for data and decision-making (45 minutes)
    • Break (15 minutes)
    • Assignment review and discussion: Interrogating case studies in fairness, data, and algorithms (75 minutes)
    • Final Q&A (30 minutes)

Register now

Individual ticket: $299

Participate in this workshop from the convenience of your home, your office… whatever environment you find most comfortable and conducive to an intensive educational experience.

Group tickets

Working as a team? Learn as a team.

Taking this course as a team ensures that everyone is on the same page and understands both the immediate and long-term and immediate goals of your project. Exploring new ideas and collaborating on exercises together is a great team-building experience; everyone on your team will have the opportunity to ask questions, discuss use cases, and learn from other participants.

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