Episode 185: How can you approach AI with an equity lens?

Summary

The Smart Communications Podcast: Episode 185 – Approaching AI with an Equity Lens

Released on April 30, 2025 | Host: Farrah Trim Peter | Guest: Mina Das, CEO of Namaste Data

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Introduction

In Episode 185 of The Smart Communications Podcast, host Farrah Trim Peter delves into the critical intersection of artificial intelligence (AI) and equity. This episode, titled "How Can You Approach AI with an Equity Lens?", features Mina Das, the CEO of Namaste Data—a consulting agency dedicated to fostering ethical and human-centered AI and data practices within the nonprofit sector. With 17 years of experience bridging the tech and nonprofit worlds, Mina brings invaluable insights into how organizations can harness AI responsibly to advance their missions.

Understanding AI Equity and the AI Equity Project

Farrah Trim Peter opens the conversation by introducing the central theme: approaching AI through an equity lens. She highlights Mina Das as a leading voice in this domain, noting her contributions such as authoring the Community Centric Data principles and co-founding the AI Equity Project.

Mina Das elaborates on the origins of the AI Equity Project, revealing a collaborative effort with Michelle Flores Vrin initiated in 2024. The project emerged from a shared concern that discussions around AI within the nonprofit sector were predominantly technical, overshadowing crucial conversations about accountability, ethical data practices, and inclusive implementation. Mina articulates:

“The conversation about AI has to be more beyond just the tech conversations or the tech stack or login credentials.”
[01:58]

The AI Equity Project aimed to fill this gap by engaging over 700 nonprofit organizations, gathering data to assess the sector's readiness and identifying key areas where equity considerations were lacking. The project is slated for annual repetition to track progress and evolving challenges, emphasizing that AI is a lasting technological force that must be approached thoughtfully and proactively.

Key Findings from the AI Equity Project

When prompted by Farrah to summarize the report's findings, Mina Das presents three primary insights, supplemented by a fourth bonus learning:

1. Insufficient Funding for Experimentation

   The study revealed a significant funding gap hindering nonprofits from experimenting with and co-learning about AI and data equity. This lack of financial support stifles innovation and the ability to implement equitable AI solutions.

2. Lack of Common Language Around Data Equity

   There is a pressing need for a unified terminology within the sector. Terms like data equity and AI equity are not universally understood, leading to fragmented efforts and misunderstandings. Mina emphasizes the importance of building a shared vocabulary to facilitate meaningful dialogue and collaboration:

   “We are not just sharing that with each other enough to build that common Vocabulary, that common document.”
   [07:10]

3. Limited Partnerships Beyond Tech Companies

   Nonprofits tend to rely heavily on partnerships with major tech firms, overlooking opportunities for collaboration with other nonprofits or coalitions. This narrow focus restricts the diversity of perspectives and solutions that could enhance AI equity initiatives.

4. Transition from Task-Based to Mission-Based AI

   Currently, AI usage within nonprofits is predominantly task-oriented—such as drafting letters or planning events. Mina advocates for a shift towards mission-centric AI applications that align with organizational goals and foster deeper, more strategic use of technology.

   “Pretty much that's it with when we think of Gen AI. Where I want us to get to is a place of moving from task based AI to mission based AI.”
   [09:50]

These findings underscore the need for strategic investment, shared understanding, broader collaboration, and a mission-aligned approach to AI in the nonprofit sector.

Data Equity Practices for Nonprofits

Transitioning the discussion to data equity, Mina Das highlights several practices that nonprofits must adopt to ensure their data handling is fair, transparent, and inclusive:

1. Reevaluating Data Collection Methods

   Nonprofits must scrutinize the why and how of their data collection. It's not merely about amassing more data but understanding the purpose behind it and ensuring that data collection processes are transparent and contextually appropriate.

   “The second misconception around data equity is how we collect the data and why we collect the data.”
   [11:57]

2. Building Context, Transparency, and Accessibility

   Data should be collected with an emphasis on context and made accessible to relevant stakeholders. This approach fosters trust and ensures that data serves the organization's mission without perpetuating biases or exclusions.

3. Moving Beyond Identity Data

   While much attention is given to demographic data (e.g., race, gender), data equity encompasses all types of data. Organizations must consider how every piece of data is handled, stored, and utilized through an equity lens.

4. Challenging the "Need More Data" Paradigm

   Mina urges nonprofits to move away from the belief that more data inherently leads to better outcomes. Instead, organizations should focus on the quality and relevance of the data they hold, ensuring it's leveraged effectively to support their missions.

Approaching AI and Emerging Technologies

Farrah Trim Peter draws parallels between the evolution of past technologies and the current emergence of AI, prompting Mina to share her perspective on how nonprofits should navigate these changes. Mina emphasizes the normalization of fear and resistance to new technologies, likening the apprehensions surrounding AI to those experienced during the advent of floppy disks or VCRs.

“I want us all to feel encouraged and welcome to approach it differently. Look at it not in a way that yes, it's going to solve their breeding, but look at it from a point of view of curiosity.”
[15:25]

Key takeaways from Mina's approach include:

• Embracing Curiosity Over Fear

  Instead of viewing AI as a threat, organizations should foster a sense of curiosity and explore how AI can augment their capabilities.

• Distinguishing Between What AI Can Do and Who You Are

  AI can replicate tasks but cannot replace the unique human qualities—such as compassion and creativity—that drive nonprofit missions.

  “AI can replace what you do. It can't replace who you are.”
  [17:10]

• Focusing on Intentions and Values

  The ethical use of AI hinges on the intentions behind its deployment and the values guiding its implementation. Nonprofits must ensure that their use of AI aligns with their core mission and ethical standards.

Hopes for the Future of AI in the Nonprofit Sector

When asked about her aspirations for AI's role in nonprofits five decades down the line, Mina Das shares a visionary outlook grounded in continuous equity and solid foundational practices:

1. Sustained Commitment to Inclusivity

   Mina hopes that in 50 years, nonprofits will persistently evaluate whether their AI initiatives are inclusive and justice-oriented. This ongoing self-assessment will ensure that AI serves as a tool for equity rather than perpetuating existing disparities.

   “Is this whatever we are doing with AI, is this inclusive enough or not?”
   [20:10]

2. Establishing Robust Data Foundations

   She envisions a future where nonprofits have sophisticated data systems in place, eliminating the need to repeatedly grapple with basic data management issues. This foundational strength will enable more advanced and strategic use of AI without backtracking on essential data practices.

   “I don't want someone to pick up this kind of research again and find out that we don't have the right data systems.”
   [21:20]

3. Continued Collaboration and Collective Action

   Mina emphasizes the importance of collective efforts in maintaining ethical AI practices. By working together, nonprofits can ensure that AI technologies are leveraged to democratize power and promote equity across the sector.

Final Advice and Closing Thoughts

As the conversation draws to a close, Mina Das imparts heartfelt advice to nonprofits navigating the complexities of AI:

“Approach it with a lot of love, a lot of joy. I know it sounds so fundamental, but that's the thing. We just need to go back to fundamentals.”
[23:18]

She underscores the importance of maintaining a positive and values-driven approach to technology adoption, encouraging organizations to harness AI in ways that amplify their strengths and serve their communities effectively.

Farrah Trim Peter echoes Mina's sentiments, advocating for love, joy, and curiosity as guiding principles for handling emerging technologies. She also directs listeners to additional resources, including the AI Equity Project report and Mina's LinkedIn profile for further engagement.

Key Quotes with Timestamps

• Mina Das on Expanding AI Conversations Beyond Tech:

  “The conversation about AI has to be more beyond just the tech conversations or the tech stack or login credentials.”
  [01:58]

• Mina Das on Common Language in Data Equity:

  “We are not just sharing that with each other enough to build that common Vocabulary, that common document.”
  [07:10]

• Mina Das on Transitioning to Mission-Based AI:

  “Pretty much that's it with when we think of Gen AI. Where I want us to get to is a place of moving from task based AI to mission based AI.”
  [09:50]

• Mina Das on AI Replacing Actions, Not Identity:

  “AI can replace what you do. It can't replace who you are.”
  [17:10]

• Mina Das on Inclusivity in AI Applications:

  “Is this whatever we are doing with AI, is this inclusive enough or not?”
  [20:10]

• Mina Das on Foundational Data Practices:

  “I don't want someone to pick up this kind of research again and find out that we don't have the right data systems.”
  [21:20]

• Mina Das on Approaching AI with Positivity:

  “Approach it with a lot of love, a lot of joy. I know it sounds so fundamental, but that's the thing.”
  [23:18]

Conclusion

Episode 185 of The Smart Communications Podcast offers a profound exploration of how nonprofits can engage with AI responsibly and equitably. Mina Das provides a roadmap for integrating AI in ways that uphold ethical standards, promote inclusivity, and align with organizational missions. By emphasizing the importance of shared language, strategic funding, and collaborative efforts, this conversation serves as a crucial guide for nonprofits aiming to leverage AI as a force for good. Listeners are encouraged to engage with the full report of the AI Equity Project and connect with Mina Das through Namaste Data and LinkedIn for ongoing insights and support.

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Connect with Mina Das and Explore Further:

• Website: NamasteData.org
• LinkedIn: Mina Das
• Podcast Contact: [e-mail]

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Transcript

[Mina Das]

This is the Smart Communications Smart Communications.

[Farrah Trim Peter]

Smart Communications Podcast Developing the Voices Developing.

[Big Duck]

The voices of Determined Nonprofits brought to.

[Mina Das]

You by Big Duck.

[Farrah Trim Peter]

Welcome to the Smart Communications Podcast. This is Farrah Trim Peter, co director and worker owner at Big Duck. In today's conversation, we're going to ask the question, how can you approach AI with an equity lens? And I am delighted to be joined by a new favorite, Mina Das. Mina uses she her pronouns and is the CEO of Namaste Data, a data and AI equity consulting agency helping nonprofits design ethical human centered AI and data practices. With 17 years of experience in the tech and nonprofit space, she she has authored principles like Community Centric Data and co founded the AI Equity Project. She's passionate about making complex tech accessible for social impact. Mina, welcome to the show.

[Mina Das]

Thank you so much for having me here, Farah. And I'm going to ignore the word new favorite. I'm going to focus on the word favorite.

[Farrah Trim Peter]

That's right, just favorite. Eternal favorite. New to my life, but clearly a favorite.

[Mina Das]

Love it. Okay, I'm going to say thank you so much for having me here and having this conversation with me.

[Farrah Trim Peter]

Well, I want to start with the AI Equity Project, which you co founded with Michelle Flores Vrin, who's been on the podcast twice discussing audience prioritization and radical honesty with Marissa Desalls. We'll link to both of those conversations if people are interested, but it is great to have a connection there. And I know we have lots of other friends in common and folks who do work in the nonprofit space. So again, welcome. Excited to know you. But let's start with the big question, which is what is AI Equity and what is the AI Equity Project? What led you and Michelle to actually conduct the research that is part of the project as well?

[Mina Das]

Great question to start with and so glad it's not Introduce yourself Nina Question. I never know what to say on that one. AI Equity Project well, Michelle and I have been in conversations for almost year. Before we started this project in 2024. We had been in conversations understanding where are we asking each other where are we as a sector when we speak of the word AI, most of the conversations when it happens around AI, we see a bunch of reactions which is overwhelmed. I don't know how to use it. Where do we start? The basic fundamental questions and then the responses pretty much always came from the big tech companies saying here are the four tech things you need to know and here are the cloud products you need to know and here's the subscriptions you need to get but there were a variety of conversations that were not happening in that spectrum, like who should be held accountable when we are working with AI? Is it our legislature? Is it our board members? Is it us individually? Are we even having the right data practices and data values when we work with artificial intelligence? I mean, the conversation about AI has to be more beyond just the tech conversations or the tech stack or login credentials. And we couldn't see any space like that. So we wanted to figure out how to capture some data on that, how to capture some stories on that, how to talk to the sector in a way where we know where we are on this conversation holistically. So we established this three years from now project. I mean, I started it and then I said, michelle, you have to be part of it. And then Michelle was, is amazing. And she said, yes, I will be. And I never gave her enough choice to say no. And I'm so grateful for her. But she and I designed a bunch of questions. It was pretty straightforward. From there we designed a bunch of questions, reached out to nonprofits. The sector responded very kindly to our request and they came back with several 700 plus nonprofit responses to what and where they are and what we need as a sector. Not all of it was surprising, and I know you and I are going to talk about it, but it did give some very interesting and obvious and messy outcomes that we found in the project. And we are going to repeat the same study this year and the next to be able to compare. Where are we heading? What is the story here? This is a technology that is going to stay with us for a very long time. And we need an approach which is not fear based, which comes from our joy, which comes from our strengths, and is not constantly used as a weapon to threaten our existence. That is AI every project.

[Farrah Trim Peter]

Yeah, that's great. Well, actually, you know, I had folded another question in there that I realized I probably shouldn't have combined. So I'm going to pull it out, which is just for folks who may not, who may be having a hard time understanding AI and equity together. What do we mean by AI equity? And then we'll come back to the project in the report.

[Mina Das]

AI and equity. Well, first of all, I want to acknowledge that we do not use these two words together in the world enough, as much as we should. We use the words AI generally loosely, as much as we can in this time. And then we sometimes happen to use the word responsible AI almost as if that is another flavor of AI that you can purchase or buy. We have a tendency to not put the importance of these words together. So what AI equity, When I say I really mean AI, first of all, I mean AI, using it, purchasing it, selling it, doing any kind of work, any kind of work around AI with the lens of equity, inclusion and justice. AI equity doesn't pertain just to the data scientists who are designing algorithms. This word doesn't belong to the CEOs and C level executives who are responsible to sign on purchases regarding softwares and products like these. So when I say AI equity, I am talking to almost everybody who is interested to use, design, produce, sell, purchase something, to do something with artificial intelligence. And I am trying to tell them that any action we take around in with AI, it has to happen with a lens of equity. It has to happen with a lens of inclusion. That is what I mean when I see the words AI equity.

[Farrah Trim Peter]

Thank you. Well, like I said, we're going to.

[Roundtable Technology]

Come back to the report and we'll.

[Farrah Trim Peter]

Link to the report and the transcript of this [e-mail] insights I really hope folks download it and read it. But for now, since folks hopefully are just hearing this conversation, I would love for you to share with everyone the three big summary findings from the report so they can just wrap their heads a little bit more around what you found.

[Mina Das]

Absolutely. So my first finding in the report was that we do not have enough funding for organizations to experiment in these conversations. So most of the funding that is available, at least that was the case in 2024 when we were looking at this study. And the cycle of the study, just for context, was May 2024 to October 2024. So that's when we wrapped it up. We didn't have enough funding in the sector for nonprofits to experiment and co learn and produce something together on data equity and AI. Data equity for starters, was a word which I'm coming to. One of the findings was a word not all of us are familiar with or were familiar with. And we were getting to. So that was the first one. Lack of fund, no surprises. Number two was this data equity piece that we might not have common language. That was one of the things we might not have common language around the word data equity, around the word AI equity. Like even asking the question that you asked, what is data equity? What is AI equity? We are doing that the trust. The reality is we are doing some good things with data from our tables, individually, in our small teams, in our day to day. We are not just, we are just not sharing that with each other enough to build that common Vocabulary, that common document. And so that was one of the second findings of that research that we need common language. We don't have that yet. The third one was we don't look for partnerships beyond the tech companies, any kind of partnership. When it comes to AI, we are our natural tendency and when I say our, I mean the sector. Our natural tendency is to look at the tech companies to, to tell them, help us, guide us, what products do we need. But there is a step of dreaming, visioning when it comes to this technology, this incredibly powerful technology that doesn't happen. We miss that step. And the moment we think that our organization needs AI, we go straight to the tech companies. And that's the third thing we need. Our recommendation is we need collaborations between two nonprofits or nonprofit and a coalition or all forms of different collaborations. That goes even beyond the tech companies. And let's not restrict ourselves to the tech products. And the fourth bonus learning that I and Michelle had through this project is a lot of the work that we do around AI. And it's true even for this like we are in the second or second year of the project and fourth year of having ChatGPT now in the world. A lot of the work that we are doing with AI in the sector is still individual, task based. Someone is writing a letter, someone is drafting an outreach plan plan for an event. Pretty much that's it with when we think of Gen AI. Where I want us to get to is a place of moving from task based AI to mission based AI. And for that to happen, we need to have more conversations. We need to have more talking to each other so we get to know, okay, it's not just enough to write one outreach letter using Gen AI. Now let's talk to each other. What is the purpose of this letter and how is it actually leading up to donors supporting us for the X campaign or something so that we are tying it this conversation with evaluation with the why is this important? So that is the learning of the project that we are still in the task based AI, not yet in the mission centered outlook.

[Farrah Trim Peter]

Great, I appreciate that and thank you for giving us the bonus.

[Roundtable Technology]

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[Farrah Trim Peter]

No worries.

[Roundtable Technology]

Roundtable Technology is here with the ultimate umbrella. Their free three part webinar series Weathering the storm. Protecting your nonprofit and yourself in uncertain times has you covered their privacy. Experts real practical steps will protect your team, your data and your mission in the current US Political climate, think data governance, risk ownership, staff protection and more. To explore this content, go to roundtabletechnology.com, look for the Visit Free Stuff section and view the webinar library. Here's to staying dry, secure and prepared, no matter what the clouds bring.

[Farrah Trim Peter]

You know coming back to data equity, right? We start talk about AI equity, now we're getting into data equity. What are some of the data equity practices you uncovered that nonprofits actually need to do more and engage with?

[Mina Das]

I think the biggest one that kind of became my work was how we do data collection, because that's one of the primary areas where my work is most focused and comes up often. I'm going to give some misconceptions, some things that we can be doing differently. One is we need more data. That is not always true. We do have data. We need to understand why are we collecting, what are we collecting. Number two is the data that we collect. We collect it from multiple different places. We don't build in context, transparency and accessibility around that data that we collect. So the second misconception around data equity is how we collect the data and why we collect the data. And the misconception around data equity is we think data equity is a foreign concept almost. And for that to use and build and do something with data equity, we need to learn a lot. I actually got some notes when we started with the AI Equity project asking am I even eligible, Mina to take this survey? Because I don't know what is data equity? Should I just remove myself from this ask? I want to support in this project, but I don't know if my story is good enough for this survey or not. And that's where I want to change the narrative. The biggest misconception is that we are doing things with data. Data equity means how we are handling the data, how we are collecting it, how we are storing it, how we are taking decisions with data. With this equity and inclusion lens. It doesn't just talk about the identity data. Data equity doesn't mean just about ethnic data or racial identity data or gender data. Data equity means any data, regardless of what it is about, how are we operating around it? Are there values with which we are operating around that? And that is the missing piece when you're asking what is missing in this conversation, that level of understanding that yes, you have a space in this conversation that is missing how we are collecting that data. I want to bring us back to that. And then the third one being that we don't always need more data. We have Enough data. Can we step away from that idea that to do anything with data equity and AI, we just need more and more. I want to take that off of our vocabulary.

[Farrah Trim Peter]

Yeah, definitely. Well, when we were preparing for this conversation, we were both reminiscing about how much technology has changed in our lifetimes. And you shared that regardless of whatever the new form of tech that is emerging, whether it's AI or something else, our approach should be the same. And I would love you to talk a little more about that. What should our approach be to AI and emerging tech? You know, whether it was matching when we first started using smartphones or. I remember I got my first email address when I was in college. You know, obviously that was brand new. I actually recently had to find my VCR in my closet because I wanted to see something. I still have it, thank God. But, you know, I remember when all I did was watch movies on vcrs now. Like, who needs those VCR tapes? So let's talk about, you know, again, tech is constantly changing. How it shows up in our life is changing. But how we think about it, we often, when these new things come and as you said, like the story about the person who got the invitation to your survey who felt, you know, that, that imposter syndrome, that I don't know enough about this, I'm not the right person. Let's talk about that. What do you think our approach should be to AI and emerging tech?

[Mina Das]

I want to first normalize the reaction that we are having right now in this moment, which is fear and rejecting this idea's ideas, feeling the imposter syndrome. I, I mean, I'm gonna say as a tech person, these are exactly the same things I saw when floppy disk first came and then seed came and then, you know, the, the USB ghost game and you suddenly, we were not even close to cloud storage. And now you can. You no longer have to use the floppy disk. What. And how can you. Do you not lose your projects? And there were like these, all these feelings and ideas and attitudes and approach when these new things came. I am seeing nothing different when we are talking about AI. I have been doing these conversations on AI for at least the last four years. I've been working in with the algorithms for many years. But to do these so much publicly has been my life for the last three years, I would say. And every single one of them has had this question, I don't want to use it. What would you suggest? Do I have to? Will I lose my job? Will this AI take over who I am they are some of the most basic questions. It's almost like it is threatening us in an all new way. But the threatening part is not necessarily new. We do know what fear is. We do know how fear works. And here we are looking at this technology, approaching it the same way. But I do want to offer number one, that this technology is incredibly powerful. This technology is going to stay with us for a very, very long time. We are no longer talking about floppy disks that we can insert and store something and pull out. We are talking about a technology that can learn on its own, its own what mistakes it made. And it can teach other AI products through itself how those products need to be updated. So we are speaking of an magic like incredible technology here. And yes, the approach that we are taking right now is fear based, understandably so. But I want us all to feel encouraged and welcome to approach it differently. Look at it not in a way that yes, it's going to solve their breeding, but look at it from a point of view of curiosity. Yes, knife is equally dangerous, right? We use it to cut our vegetables for salads as well as it can hurt and harm people, it doesn't mean we are no longer going to use knife. Same goes for fire. Fire exists. I mean the way it created it can cause harm and hurt as well as it can and keep us warm. So there, there are things in the world that exist which has brought out the same feelings. The question truly is about our intentions. The question truly is about our values. And the question truly is what are we trying to protect? When people ask me is my job going to be replaceable, I tell them probably yes. Right now, AI can do a lot of those same things that I do in my business. What it doesn't threaten me or the way I see it is that what am I trying to protect? Am I trying to protect Mina's job description that only Mina can do it? Or am I trying to protect who Mina is? The caring, the compassion, the kindness that are important to me. How can I make sure that the things the AI produces it has the same things, the kindness, the compassion, the humanity that I value. So don't think so much for in your fear as you're moving out of your fear as to what parts of you can AI do? AI can replace what you do. It can't replace who you are. Distinguish between the who you are and what you do. The better we know ourselves, the better we know our values, the better we understand where we are coming from, the more passionately can be protect the who part of us in this AI conversation. And that's how I feel the approach can be different when we are talking about this technology.

[Farrah Trim Peter]

I love the way you see it all. So thank you for bringing that together. And one of the things that I appreciated, there's a lot I appreciated about the report from the AI Equity Project was the opening letters from you and Michelle. And in those, you articulate your hope for the future of AI and nonprofits. And again, some of that may be many of these themes. And I'm just curious today, here we are, we're recording this in March 2025. What is your hope right now in this moment for the future of AI and nonprofit organizations or the nonprofit sector, let's say.

[Mina Das]

Lovely question and probably I should start thinking about it because the new report should be coming out in two months. I am going to write this. You know, this is the beautiful question that I always carry in my mind. What do I want out of this sector for this technology? Not to build a business out of it, but to understand where can I make the most impact? Where can my presence make the most help? And to answer your question, I want to answer two things. One, 50 years from now, a question I want always to be there. Is whatever we are doing with AI, is this work inclusive enough or not? That inclusion, that lens of equity that is never complete, it's an ongoing journey. We can never get to a point where we say, okay, we've done enough and we need to stop thinking about inclusion. Well, that's a check mark done. We no longer need to talk about that. That's a metric we have achieved and done and dusted. We can't be that community. We can't be that group of humans or beings on this planet. So 50 years from now, my hope is we still ask that question every day. Is this whatever we are doing with AI, is this inclusive enough or not? Is this justice oriented, equity led or not? My second hope kind of intention to that piece is we move away. We set up enough basic foundation so that we don't circle back the same things over and over again. So one of the conversations I don't want to circle back is a lot of our nonprofits are not ready yet to even have the right database systems, how to store the data points before they even talk about AI and Einstein analytics and other big, really nice products out there. We need good database systems 15 years from now. I don't want someone to pick up this kind of research again and find out that we don't have the right data systems. So my second hope is we have the right foundations. We don't circle back the same things over and over again. What we do circle back is the same question. Is it inclusive enough or not? Are the right voices included or not? Is the power democratized enough or not? Is what we have done with this product of AI, Is it sharing equity and power or not? Those kind of questions, but not if we have the right database systems or not. Where are we storing data? We need more data. Are we still on Excel spreadsheets? So my hope is we have the right foundations. My hope is we are asking over and over again the important questions that we cannot miss. And my hope is that we do it collectively, together.

[Farrah Trim Peter]

Love it. Well, if you would like to engage more with topics related to Data Equity and AI, be sure to visit Namaste Data.org you can also connect with Mina on LinkedIn. And I have to say I really enjoy your posts on LinkedIn. You always reference good recommendations and tips and thought provoking questions. So highly recommend you follow and connect with Mina on LinkedIn. So Mina, before we sign off, any other advice or thoughts you'd like to share? You just dropped a lot of knowledge, gave us a lot of things to think about. But anything else you want to say on this topic?

[Mina Das]

Probably just approach it with a lot of love, a lot of joy. I know it sounds so fundamental, but that's the thing. We just need to go back to fundamentals. There is a lot of comfort if we go back to the basics. So if you're approaching this technology, if you're new in it, or you have been dealing with this technology for a while now, make sure that you are bringing a lot of love, a lot of joy and you are not approaching it from a place of competition or threat or fear. But just how can you do it in a way which allows you to be the best version of yourself for the communities around us?

[Farrah Trim Peter]

Amazing. Well, love, joy and curiosity, we can all take that for the our entire worldview. So thank you so much Mina and really appreciate you being here today.

[Mina Das]

Thank you so much for having me here.

[Farrah Trim Peter]

All right everyone be sure to go download that report words and keep those minds open.

Are you a fan of this podcast or Big Duck's other resources on non profit communications? If you are, we'd love to hear from you. Please drop us a line by writing to helloigduck.com to tell us what you're working on and what topics you need help with. We also welcome getting your feedback via reviews. You can review this podcast in itunes or wherever you listen. We'd love to hear from this is.

[Mina Das]

The Smart Communications Podcast, Developing the Voices of Determined Nonprofits, brought to you by Big Duck.

[Big Duck]

Big Duck is an agency that puts smart communications in the hands of nonprofits. We help our nonprofit clients develop strong brands, strong campaigns, and strong teams that advance their missions and achieve their goals.

[Mina Das]

Connect with [e-mail].