I work at the intersection of product innovation, strategy, and responsible data and AI, helping mission-driven organizations design, launch, and scale solutions that drive measurable community and economic impact. I integrate human-centered product design with ecosystem-aware go-to-market strategy to ensure innovation is not only built, but adopted, operationalized, and sustained.

My work bridges strategy and execution by aligning vision, product design, technical workflows, stakeholder incentives, and narrative clarity. I’m typically engaged when the opportunity is high, the path is ambiguous, and success requires both systems thinking and creative problem-solving. I move initiatives from concept to prototype to adoption through intentional workflow design, collaboration, and clear communication.

In short: I specialize in converting strategy into scalable, operational reality.

Sarah Merion

𝐒𝐡𝐨𝐫𝐭 𝐯𝐞𝐫𝐬𝐢𝐨𝐧 𝐚𝐛𝐨𝐮𝐭 𝐦𝐞? – A humble first-gen immigrant, data, research and analytics passionate consultant, author, certified life coach and mentor - with a knack for telling empathetic stories and a hint of humor.

𝐒𝐥𝐢𝐠𝐡𝐭𝐥𝐲 𝐥𝐨𝐧𝐠𝐞𝐫 𝐯𝐞𝐫𝐬𝐢𝐨𝐧? - I blend storytelling, conversions, and human-centric data tactics to support nonprofits. My key influencers of life include my family, starting with my mother. Her perseverance is the voice in my head, pushing me to be better.

Broadly speaking, there are four pillars of my work:

➡️ Consulting
● End-to-end data collection (e.g., donor survey)
● In-house Advisory on AI Adoption
● Data analysis (e.g., you have the raw data for last survey)
● Data Collection Assessment (e.g., are you collecting right identity data points?)
● Data Equity Coaching (9-hours of my time on any data related issue you have)

➡️ Workshop/Training
You can hire me to conduct nonprofit-wide/team-wide/board workshops on:
● Advancing Equity in Data Collection
● Advancing Equity in Visualizations
● Moving Towards Human-Centric AI
● AI Coaching for Nonprofits (called AI Advancement Lab)
● Data For Good

➡️ Speaking
You can hire me as a session facilitator/Keynote speaker. My topics include (but not limited to):
● Doing good with data
● Tactical ways for nonprofits to get started with data equity
● Designing inclusive data collection tools
● Designing dashboards for good
● Responsible AI and AI adoption in nonprofits
● AI Equity
● Centering humans in algorithms

➡️ Writing
● You can invite me as a guest writer, or,
● You can re-print any of my existing articles (please reach out to access 50+ article repository) for a fee.


Interested to work with me? Send me a message today!

Meenakshi Das

Over the last two years, I have heard some versions of this sentiment again and again:
“We know AI has risks… we just don’t have the words for what feels off.”

The conversation about “AI risk” in our sector often feels distant-enough, and yet, look close enough, harm is already showing up in very ordinary places: in how we write about communities, where we paste donor stories, how we measure staff, who we quietly deprioritize because a score told us to.

This is why, two years ago, when the AI Equity Project was launched, simultaneously, a “forever” open ‘Report Your Data and AI Harms’ form was launched as well.

To date, it has collected 24 responses.

This month, I am creating the first round of distilled AI Harms into an accessible resource.

It names 24 very real ways AI can cause harm in our context—across discrimination and representation, privacy and surveillance, misinformation and bad advice, labor and culture, and power and community.

Names or any identifiable information from these stories have been removed.

For each one, it asks:
● What is this harm?
● Who is harmed when this happens?
● How do I know if I/we might be causing this?
● How do I know if I might be experiencing this?

Mind you, this isn’t a technical checklist. It’s a reflection tool you can use in team meetings, retreats, and board conversations to look into examples of what we need to be careful about.

If you are a person in this sector who finds themselves being pulled into AI—by vendors, funders, or sheer necessity—and you want to stay grounded in equity, this resource is for you.


Link: https://lnkd.in/gUK-6M_Y

Here is to continuing to create safe spaces to record these harms, and then sharing them in accessible ways (with permission) to understand our actions...


#nonprofits #community

Meenakshi (Meena) Das

CEO at NamasteData.org | Advancing Human-Centric Data & Responsible AI | Founder of the AI Equity Project

Virtuous's Responsive Nonprofit Summit (RNS) is coming up, and it’s one of the few virtual events that’s actually built for real nonprofit constraints: limited time, messy systems, big fundraising goals.

It’s free, virtual, and packed with sessions on donor experience, retention, data, and what “AI-forward” can look like without losing the human parts of fundraising.
(This is why it's always on my list - to join as a speaker or attendee...)

My nonprofit friends, if you have been thinking, “We need to modernize… but we can’t afford chaos,” this is a good room to be in.

My dear friend T. Clay Buck, CFRE and I will try to replicate our nerdy and joyful 1-1s into a panel on Wednesday (data → AI-ready), but honestly — the value is the whole lineup.

If you haven't registered, here is the link: https://lnkd.in/gbZDWyDJ

#nonprofits #nonprofitleadership #continuouslearning

The 78th edition of the newsletter Data Uncollected is here!

You and I both know the sector is moving fast (with AI) right now. It (AI) is showing up in our inboxes, our grant workflows, our donor communications, our program notes—sometimes quietly, sometimes loudly, sometimes directed as “efficiency” and “innovation.”

Yes, AI can draft a message. It can summarize a meeting. It can sort information. It can suggest what comes next.
 
But it cannot feel the consequences of being misunderstood by a system. It cannot hold the complexity of a human life without our help. It cannot repair trust when harm happens.

So, I keep coming back to one stubborn truth: trust is not automatable.

Especially because the work we do in this sector isn’t just a list of efficient tasks. It’s relationship, and it’s dignity, and it’s keeping hope, even when the most complex struggles surround us. 

Three years ago, I wrote Tenets of Human-Centric AI because I needed something to hold onto—something that wasn’t hype and wasn’t fear. 

Today, you and I are updating that language.

In this edition, I am sharing seven updated tenets for human-centric AI and the rights I don’t want us to forget as this technology becomes normal.

If you have been feeling that tension too—this (AI) could help us, but it could also flatten us—I need you with this piece. I need your wisdom, thoughts, and questions, so we can build a collective language here.

Read this edition as a reminder that we don’t need to be technical to lead this conversation. We just need to stay very, very human.

#nonprofits #community

Welcome to data uncollected, a newsletter designed to enable nonprofits to listen, think, reflect, and talk about data we missed and are yet to collec

Open article: Tenets and Rights of Human-Centric AI. by Meenakshi (Meena) Das • 10 min read

Let’s talk about checkboxes in our surveys.

Sometimes the most inequitable part of a survey is not the question.
It is the response options we choose to offer.

Because categories are not neutral. Categories are someone’s, ours, our team’s understanding of the world—frozen into a dropdown.

And when people can’t find themselves in our response options, they don’t just feel “miscategorized.” They feel invisible. Or worse, forced to choose something that feels untrue.

A practical practice I love and follow:
● Make identity questions optional.
● Offer multiple selections where relevant.
● Include prefer not to say without making it feel suspicious.
● Use “self-describe” with a text box when you can
● And please, don’t treat “Other” as a miscellaneous ground without dignity.

Data equity is not perfect categories. 

It is our humility to operate with the understanding that our community will always be bigger than our boxes.

#nonprofits #community

We have to understand - our (algorithmic) models are not neutral.

● Tech is built inside power.
● Data is collected inside power.
● Definitions of “good” and “professional” are shaped inside power.

So the job of leadership is not to pretend tools are neutral.

The job is to ask better questions before tools reach, collect, and engage people.

Here are some questions leaders should ask before approving any AI use:
● Who benefits if this works? Who pays if it fails?
● Who gets misread by this system?
● What kinds of people does this tool assume are "common" or “normal” or fall under "generally speaking"?
● What happens when someone wants to opt out?
● Who is accountable when harm happens?

If our AI plan has no answers here, it’s not a plan.

And it's leading to broken governance, guaranteed.

There is a particular kind of annoying loop I am frequently seeing:
We will spend money on AI tools to analyze community feedback faster…
while skipping the one thing that actually builds trust: the share-back.

If you want to be human-centric in a data world, this is important:
If we ask people for their time, we owe them a closing loop. Full-stop.

It doesn’t need to be a 40-page report or polished deck.

It just needs to be update - any form or shape - that speaks to
● Here is what we heard.
● Here is what surprised us.
● Here is what we are changing.
● Here is what we can’t change yet (and why).
● Here is when we’ll check back.

AI can speed up analysis.

But it cannot replace accountability.

And accountability is what people remember.

#nonprofits #nonprofitleadership #community

You might already be doing data equity—without calling it that.

Because data equity isn’t always a big framework, a new strategy, or a 40-page policy document. It’s often small, practical choices that quietly say: people are not data points, and trust matters.

Here are some examples of what data equity can look like inside organizations—and I am sharing them because I want you to know even if one of these is already true in your organization, you are not “behind”- you are practicing data equity.

Big or small, well-resourced or not, known or not… your mechanism of choosing how to practice data equity is not about perfection. 

It is and will remain about care, consent, and accountability—one action at a time.

#nonprofits #community

Working in this sector, I have seen this more than I can count now - not all “messy data” is bad data.

Sometimes “messy” is what real humans look like.

Multiple identities. Contradictions. Open-text answers that don’t fit neatly into a chart. People selecting “prefer not to say” because your categories were too small for their truths, and there was still no context for why it was asked.

That’s called nuance in the data.

And regardless of how our data looks, the goal of it is to help us understand our communities without flattening people.

So, a few practical reminders:
● “Prefer not to say” is not a weakness. It’s respect.
● Optional demographic questions are not “extra.” They are care.
● Disaggregation is accuracy, not over-analysis.
● Averages can hide harm. Always check who is left behind.

Collecting data doesn’t automatically mean good things will happen.

We collect data to be accountable, so our joy can create the good we can.

#nonprofits #community

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Meenakshi Das

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Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Meenakshi Das's LinkedIn Performance

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