Data is an essential part of modern nonprofit life. Equally critical is equity in our collection, management, and use of data. We use data to tell stories and make decisions; to be successful we must know the strengths and weaknesses of our data. You'll leave this session with a foundational understanding of data equity concepts, the latest frameworks available to promote equity in your data, and actionable steps you can take to make your data practices more equitable. KAI WILLIAMS Kai Williams is fascinated with the junction of technology, organizational theory, and the ability to do good for our world. Kai volunteers with LA Tech4Good and is a data equity and ethics workshop facilitator. She is also executive director of The International Wildlife Rehabilitation Council (IWRC), attended Lawrence University, and has a Masters degree in Public Policy and Administration from Northwestern University.

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Internalizing Data Equity in
Nonprofit Operations
October 2022
TechSoup Future of Work Conference
Kai Williams @malkahkai

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Agenda
Intro + Why
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Data Equity
•
Data Ethics
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Algorithms + AI
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Making the case + implementation
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Wrap-up
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Computer science + consulting Startup + LA Tech4Good Nonprofit
Hello, nice to meet you!

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Group Norms
Active presence
Be present, ask questions, and "step up"
Peer learning
Share what you know (using “I” statements) and
actively listen to your peers. "Step back"
Growth mindset
Bring an open mind and practice humility. If you
don’t understand something, look it up or ask.
Feedback + iteration
Help us iterate! Send me your feedback after this
session.

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Learning Objectives
1. Understand how equity impacts the creation and use of data through collection and
categorization
2. Consider biases and their impacts, how power dynamics are reflected in data and
organizational practices, and how these assumptions and choices shape the design
and implementation, and impact of algorithms.
3. Practical methods to embed ethical practices into data projects

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Warm Up Poll
How familiar are you with the concepts of data equity
and data ethics?
(1 = totally new concepts; 5 = you can confidently define
both terms)

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WHY
“...maximize positive impact and minimize unintended
consequences” - Rachel Whaley & Jen Holmes

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Data is power
“What gets counted counts.”
Joni Seager

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Data
is
Information Made Tractable

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Equity in Data Projects
"Equity is both a process and an
outcome."
Data Feminism

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“What story is told?
How is the problem framed?
Who decides the scope?
What values are built into the designed objects and
processes?
Who benefits?
Who loses?”
Sasha Costanza-Chock, Design Justice

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Documentation is a way to
interrogate data

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Counting
Consent
Context
Graphic from
Data Feminism, D'Ignazio + Klein

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Metrics addressing structural
problems
“What gets counted counts.”
Joni Seager

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Designing metrics
https://data-
feminism.mitpress.mit.edu/pub/3hxh4l8o/release/
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Documentation as a tool for equity.

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Data Ethics
"We often focus too narrowly on whether we have
complied with ethical guidelines, and we forget
that ethical issues concerning technology don’t
just go away once we have diligently performed
our own particular tasks. It is essential to think
about what happens to ... data after it leaves our
hands."
Shannon Vallor + Brian Green,
Markkula Center for Applied Ethics
Image from Coded Bias film

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Counting
Consent
Context
Graphic from
Data Feminism, D'Ignazio + Klein

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Why documentation?
Accountability
Backstop
Transparency
Assumptions

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Datasheets for datasets
“In the electronics industry, every
component, no matter how simple or
complex, is accompanied with a datasheet
that describes its operating characteristics,
test results, recommended uses, and other
information.”
Timnit Gebru, et al.

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Documentation for
equity example
Image from d4bl.org

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Frameworks for data documentation
Data Nutrition Project: https://datanutrition.org/labels/taxbills-nyc/
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Data Ethics Canvas: https://theodi.org/wp-content/uploads/2021/07/Data-Ethics-
Canvas-English-Colour.pdf
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Datasheets for Datasets: https://github.com/fau-masters-collected-works-
cgarbin/datasheet-for-dataset-template
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The first step when using one of these frameworks is to
decide which questions you would answer if you were
to create a datasheet for the dataset.

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“Data is a thing, a process, and a
relationship we make and put to
use. We can make it and use it
differently.”
The Feminist Data Manifest-No
https://www.manifestno.com/home

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AI + algorithms
“[Predictive] models are opinions embedded in mathematics.”
Cathy O’Neil, author, Weapons of Math Destruction

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“Data is an amplifier, an accelerant”
Ganesh Padmanalehan

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Questions to consider
Where is algorithmic processing employed to make
decisions? When are human decision makers used? For
whom?
How do you think accountability for algorithms
could/should be upheld?

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Mitigating Bias in AI
"The algorithm did it" is not an acceptable excuse if algorithmic systems make mistakes
or have undesired consequences, including from machine-learning processes.
– Principles for Accountable Algorithms

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Making the case + implementation
Asking tough questions
Stakeholder buy-in
Leadership + alignment
Implementation + measurement
Governance + sustainability

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Prioritizing better data practices is
key.
"Data is a thing, a process, and a relationship we make and put to use. We can
make it and use it differently." – The Feminist Data Manifest-No

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TAKE ACTION
Awareness
Documentation
Be a voice

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3-week workshop to learn
more, coming up in
November and December!
latech4good.org/dataworkshop-public
Thank you!
Kai Williams
@malkahkai
director@theiwrc.org
LATech4Good
The International Wildlife Rehabilitation Council