Leading Equitable Data Practices
Public workshops
This is the program for you if: Your work involves data. You are making data-informed decisions. And you are seeking ways to use data responsibly, counteract harmful data bias, and contribute to social change.
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Our January Data Equity Skills Workshop Series was a huge success!
❝ We welcomed our largest cohort of participants, who brought great energy and active participation and gave us our highest-ever recommendation rating.
Our team of seven facilitators allowed us to dedicate someone to each breakout group, and funded operational support meant that things ran smoothly. ❞
– Rachel Whaley, Data Equity Program Lead
Read our press release here.
Here’s what participants had to say
❝ I really enjoyed the workshop! Y'all pulled together a really thoughtful, well organized agenda that introduced me to new resources and concepts! I also really appreciated hearing about others' work and the intentionality in helping us figure out how to embed data equity principles into our current projects. Shout out to the facilitators too! ❞
–Danica Shipley, People Analytics Professional at Red Hat
❝ Inspired and equipped, I just finished the Data Equity Skills Workshop hosted by LA Tech4Good!
This incredible program connected practitioners from across the country to dive into topics like the impact of data collection on equity, power dynamics in organizations, and the ripple effects of AI on communities.
One key takeaway I can't wait to implement is datasheets, a game-changer for ethical data projects! This framework, Datasheets for Datasets by Gebru et al, will help me identify and address potential biases in future projects. It's perfect for documenting decisions and reasoning; knowledge sharing and replicability are the key factors! Thanks to the plethora of resources provided in the virtual workshops, I picked up Data Feminism by D'Ignazio and Klein and hope to further understand how power can be challenged and shifted.
Thanks to the LA Tech4Good team for teaching our cohort responsible data and my breakout room buddies for thought-provoking conversations.❞
–Cecilia Mejia, Community Navigator at One Degree, Inc.
❝ Privacy engineers need to begin prioritizing data ethics. I recently participated in LA Tech4Good's Data Ethics intensive [and] this experience underscored the necessity of centering equity and ethics in my privacy work.❞
–Erin Relford, Privacy Engineer at Google
Read more in Erin Relford’s blog post The intriguing edge cases of privacy in an AI-driven world
January workshop outcomes
Thank you to the Internet Society Foundation
We were able to offer the program at no cost to participants thanks to the San Francisco Bay Area Chapter of the Internet Society through a Beyond the Net Small Grant from the Internet Society Foundation.
About our public workshops
What we cover
Part 1: Equity in Data Projects. We will cover approaches for defining fairness within a specific project context, and how equity impacts the creation and use of data through collection and categorization.
Part 2: Data Ethics. We will discuss various types of bias and their impacts, how power dynamics are reflected in data and organizational practices, and practical methods to embed ethical practices into data projects.
Part 3: Impact of AI and Algorithms. Here we will discuss the assumptions and choices that shape the design and implementation of algorithms, applying our learnings about equity and ethics to understand the impact of machine learning and AI on stakeholders and communities.
What’s included
A curated curriculum of the latest and greatest resources to teach you the foundational concepts of equity and ethics in data and algorithms.
Three live virtual sessions with your cohort facilitated by data equity coaches to enable you to dig into these concepts, apply them to your work right away via hands-on exercises, and get support and feedback from your cohort in a small-group setting.
A one-on-one meeting with your data equity coach to help you create your customized action plan and navigate any challenges you’re facing in developing a personal or work-related data project.
Our only requirement is that each participant comes with a data project so that you can directly apply the principles learned to real-world practice.
For more details, check out our FAQ.
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