In this rapidly evolving AI landscape, what was the “aha moment” when you realized the opportunity and the necessity to train AI on low-resource languages to unlock and accelerate Africa’s AI potential?

My “aha” moment came while searching for a text-to-​s​peech api and finding out that Yoruba was still not supported in the ​​Google Cloud Text-to-Speech. With all of Google’s massive technology, it utterly failed in Yoruba. It became evident that language diversity in Africa was not just underrepresented but was essentially missing in mainstream AIs. I realized that we have to move quickly before we have a massive reservoir of cultures invisible to AI. The need became very evident. The opportunity for big tech to bridge this gap and support efforts to train AI on low-resource languages is both urgent and transformative. This is not just to save language heritage but to support inclusive innovation in healthcare, education, governance​,​ ​and beyond​. The future of Africa’s AI depends on language equity as a foundation, not an option. 

How does your work with low-resource languages move the needle for data and AI for social impact work? What are some of the biggest challenges you have faced in doing so?

My focus is on developing ethically sourced and linguistically valid speech data for low-resource languages​,​ specifically Yoruba and Hausa. This enables voice tools for different sectors (healthcare, education, citizenship engagement​,​ etc)​,​ particularly in underserved communities. One of our most significant challenges is infrastructure. Low-resource languages often have no digitized data, no standard orthographies, and variable speaker representation. ​There are​ institutional challenges​, ​​such as​​ ​under-resourced research and low levels of collaboration among technologists and linguists. However, by filling in the gaps, we empower the voices of the locals to shape AI as an instrument of inclusion. 

What are the diverse, interdisciplinary skills that are required to do this work effectively? Which one surprised you the most?

Effective work in AI for low-resource languages demands a fusion of skills​,​ which may include machine learning, computational linguistics, cultural anthropology, community organizing, trust building​,​ and ethics. What surprised me the most was realizing the importance of trust building during interactions with language speakers ​, ​as co-creators and not as merely data providers​ ,​ in ensuring quality data. It reminded me that the future of AI isn’t just about code and compute​;​​ ​it’s ​about ​the people. And unless we prioritize the people, our models will always remain incomplete. 

What key responsible practices should AI practitioners prioritize when developing and training AI systems in African—or other low-resource languages?

When developing and training responsible AI for African and other low-resource language communities, practitioners must give ​priority​ ​to​ community-centered data collection, transparent ​use​ of ​models​, and long-term benefit sharing. Practices such as participatory dataset design, multilingual documentation, and culturally sensitive model assessments must be adopted by practitioners. Some other guardrails include strict consent protocols and preventing models from perpetuating negative stereotypes. Trust from the community is a requirement. Without trust, communities will not cooperate, and the resulting data will ​be both​ ethically and technically imperfect. This trust is earned ​through​ respect, feedback loops, and respecting the rights of speakers as not merely data points but as rights-holders to the data. 

What is the importance of cross-sector collaborations in building inclusive AI? What advice would you offer to people interested in this work?

Inclusive AI cannot be built in silos. Governments offer policy frameworks, technologists bring tools, NGOs offer ​a ​ground-level perspective, and communities provide lived experience. Cross-sector collaboration ensures that the development of AI systems is linguistically fair, culturally relevant, and scalable. My advice to aspiring AI equity advocates is that they should start locally, stay humble, and collaborate widely. They should learn from linguists, community elders, and social scientists. They should also prioritize impact over novelty and remember that language is identity. Working in AI language equity is not just a technical challenge​,​ but a social justice mission. ​You must​​ build for​ and with​ the communities you aim to serve. 

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When did you realize that AI could be more than just a skill, but a way to solve real challenges and contribute meaningfully to Mississippi’s future?

I realized the potential of AI when I was an apprentice at first with the Mississippi AI Collaborative (MSAIC) AI Agency. As we began to work with entrepreneurs in the Jackson, Mississippi area, I saw that AI wasn’t just a tech tool; it helped us turn ideas into impact.

Tell us about your work at the AI Agency initiative. What inspired you to be a part of this initiative?

At the MSAIC AI Agency, we help small businesses and startups grow by integrating AI into their workflows. As an apprentice, I was trained on a range of AI tools and brought my background in graphic design to support clients in creative and strategic ways. We taught entrepreneurs how to use ChatGPT to develop business plans, then transformed those plans into investor-ready pitch decks using Gamma. We also introduced them to tools for building and embedding custom chatbots on their websites to improve customer experience. In branding sessions, we used Adobe Express’s generative AI features to help them create logos, define color palettes, and establish a cohesive brand identity. 

What initially drew me to the program was fear—fear of how AI might impact creative work. Social media often painted AI as a threat to artists and designers. But thanks to Dr. Brittany Myburgh’s encouragement, I joined the initiative and quickly saw a different side of AI. I realized it wasn’t replacing creativity; it was expanding it. That shift in perspective helped me overcome my fears and recognize AI’s potential to empower entrepreneurs, especially here in Mississippi. Now, I’m passionate about helping others see AI not as something to fear, but as a tool to amplify their ideas and impact.

Your work connects you directly with small businesses and nonprofits. How has applying AI in these real-world settings deepened your understanding of its social and economic potential?

Working directly with small businesses has completely reshaped how I view AI. It’s not just advanced technology—it’s a practical tool for leveling the playing field. Many of the entrepreneurs we serve at the MSAIC AI Agency have incredible ideas and strong foundations, but they often lack time, staffing, or access to resources. AI helps bridge that gap. We’ve seen firsthand how using tools like ChatGPT and Gamma have helped entrepreneurs polish their business ideas, and in some cases, win pitch competitions. That kind of momentum can be the spark that turns a vision into a fully operating business.

One example that stands out is a counseling professional we worked with. We helped her integrate a custom chatbot into her website, which now answers frequently asked questions about her services. This simple solution saved her hours of time each week and made her services more accessible to clients. Experiences like this deepened my belief that digital equity is essential. When people understand how to use AI, they gain the same creative power and operational efficiency as larger, better-resourced organizations. But access and education are essential, and training and trust-building are just as important as the technology itself. This work has shown me that AI, when used with intention, has the power to create real economic and social opportunity.

Through this journey, you have learned to use AI and helped small businesses understand its value. What’s one unexpected thing you’ve learned—and one thing you’ve taught others—that’s helped create a ripple effect beyond your own experience/ project?

One unexpected thing that I’ve learned throughout this journey is that mindset matters. I thought learning the tools would be hard, but my experience using the technology has enabled me to shift people’s minds or perspective about AI, letting them know AI isn’t necessarily here to replace your job. If used right, it’s here to help.

Something I’ve taught others was the idea that AI doesn’t have to be hard to work with. When we showed entrepreneurs how to use ChatGPT for tasks like writing social media captions, drafting emails, or even basic prompt engineering, they were amazed! The “I can do this!” moments unlocked a new level of confidence and potential for the entrepreneurs.

How has learning about AI helped your career?

Learning about AI has expanded my career. As a graphic designer, I saw AI as a threat to my creativity at first because of what I had seen on social media, but once I started working with the MSAIC, my perspective changed. I saw AI as a tool to enhance my workflow and design process, even sometimes help me expand on ideas that probably would’ve taken me days or weeks to think of. The tools allowed me to work faster to create content and communications. Instead of replacing me, it became like a partner. It’s also helped me beyond my art career, as I’ve gained skills in consulting, digital marketing, and more. 

Through the AI Agency, we’ve helped entrepreneurs build a business while using AI. This opportunity has pushed me out of my comfort zone, helped me grow into a multidisciplinary creative, and bridged the gap between art and technology. AI isn’t here to replace my job and creativity; it’s here to help and empower them. 

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In this rapidly evolving AI landscape, what was the “aha moment” when you realized the opportunity and the necessity to train AI on low-resource languages to unlock and accelerate Africa’s AI potential?

I realized how often low-resource African languages are left out of global natural language processing (NLP) advancements, as most machine translation models underperform for these languages, not because they are less important, but because the data, infrastructure, and high computing are not readily available. The gap created by this divide doesn’t just limit innovation but marginalizes millions of people, hindering access to critical sectors like primary health care, education, and agriculture, where AI is needed to bridge the gap.

The “aha moment” for me is that if we are serious about AI being a force for inclusive growth, we can no longer overlook the languages our people in Africa speak daily as a developmental imperative. Imagine AI-driven conversational agents that can offer basic medical advice in the Hausa language for a rural village in Northern Nigeria, bridging the gap created by the shortage of health professionals. We can democratize access to technology by enabling localized solutions that empower communities across the continent.

Projections suggest AI can contribute over $1.2 trillion to Africa’s GDP by 2030, which shows that we have a massive opportunity and an urgent responsibility. The necessity becomes clear: without AI models trained on Africa’s linguistic diversity, the continent risks being left behind in the global revolution. Training AI on low-resource languages is not just about catching up but creating truly inclusive and scalable solutions. The vision of AI that genuinely reflects the continent’s contexts drives my work to help accelerate Africa’s AI future.

How does your work with low-resource languages move the needle for data and AI for social impact work? What are some of the biggest challenges you have faced in doing so?

Nigeria has over 500 languages, making it one of the most linguistically diverse countries in the world. However, over 90 percent of these languages are considered low-resource in Natural Language Processing (NLP), meaning they lack the digital resources, corpora, computational infrastructure, and datasets needed to build effective language models. And that’s a problem because, without language inclusion, we’re building technology that doesn’t serve everyone. My work focuses on closing that gap by training AI in local African languages and building localized AI solutions to unlock access to critical services in education, healthcare, agriculture, and finance for communities that have historically been left out. When a student in a rural area can learn in their mother tongue or a patient can describe symptoms to a chatbot that understands them, that’s impact.

But it has not been easy. One of the biggest challenges we faced was acquiring locally nuanced datasets. Community-driven data collection, such as crowdsourcing, is promising but slow and resource-intensive. Additionally, limited access to computational infrastructure hinders model training. These barriers slow progress and prevent low-resource communities from accessing effectively trained AI models in their local languages. Despite the hurdles, we’re seeing progress. Our homegrown Equalyz Crowd allows you to collect multi-modal datasets and be incentivized. Through our startup, equalyzAI, we have built a language-inclusive product that drives health, education, and financial inclusion. We move the needle by making inclusion the foundation, not an afterthought, fostering equitable development, preserving cultural heritage, and driving socioeconomic progress.

What are the diverse, interdisciplinary skills that are required to do this work effectively? Which one surprised you the most?

Developing effective low-resource language models that authentically reflect Indigenous communities’ natural conversational style, cultural nuances, and religious contexts requires an interdisciplinary blend of skills. Of course, you need strong technical skills in machine learning, speech recognition, and model optimization, especially for real-time applications like speech-to-text systems. But what often gets overlooked is just how crucial linguistic expertise is, particularly from native speakers who are also trained linguists. Their ability to capture subtle tonal shifts, idiomatic expressions, and grammatical structures is non-negotiable for accuracy in low-resource language processing.

Beyond linguistics and engineering, we also needed cultural and anthropological insight, with ethical data governance, because we’re representing people’s identities, histories, and worldviews. That’s why community engagement is at the center of the process. We’ve had to co-design data collection methods with local communities to build trust and ensure the outputs are validated in contexts (meaningful and respectful).

The identity element challenged me to think beyond the algorithm and focus on inclusive, ethical AI development that reflects the people it serves.

What key responsible practices should AI practitioners prioritize when developing and training AI systems in African—or other low-resource languages?

Developing AI for African and other low-resource languages demands responsible practices to ensure ethical and inclusive outcomes. Firstly, I strongly recommend Privacy-by-Design principles and robust consent protocols. Prioritizing participant sovereignty and culturally sensitive data is responsible AI development. Interdisciplinary teams, including data governance experts and legal compliance specialists, must enforce these guardrails to align with local regulations.

Secondly, it is important to address linguistic biases in training data. These biases can distort cultural representation and reduce model accuracy. Data Collectors should curate diverse datasets and account for dialectal variations to preserve meaning across contexts.

I attest that community trust is foundational. Engaging local communities fosters linguistic authenticity, improves data quality, and builds confidence in AI systems. Transparent collaboration, including co-designing data collection with indigenous stakeholders, ensures models reflect cultural nuances and meet community needs. Communities may resist participation without trust, undermining data integrity and model effectiveness. By prioritizing ethical stewardship and community trust, AI products can drive equitable impact that preserves cultural heritage and drives social progress in low-resource settings.

What is the importance of cross-sector collaborations in building inclusive AI? What advice would you offer to people interested in this work?

I advocate for partnerships among AI startups, tech companies, academic institutions, governments, and local communities. This pool of expertise, resources, and perspectives addresses linguistic and cultural gaps in AI systems.

These partnerships minimize challenges like scarce datasets and limited infrastructure by leveraging shared resources, such as community-driven data collection or government-funded computing facilities. They also promote ethical practices, balancing technological advancement and cultural preservation.

I advise those interested in AI language equity to prioritize interdisciplinary learning and community engagement. Gain NLP, linguistics, and ethics skills and develop cultural competence to collaborate effectively with diverse stakeholders. Seek mentorship from experts in low-resource language AI and contribute to open-source projects to build practical experience. Finally, it is important to engage communities actively; their insights are critical for creating relevant, trustworthy AI systems.

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In this rapidly evolving AI landscape, what was the “aha moment” when you realized the opportunity and the necessity to train AI on low-resource languages to unlock and accelerate Africa’s AI potential?

My “aha moment” came when I realised that my intensive cultural and creative sectors background actually qualified me for this opportunity.  For so long, many of us working in language and the arts believed that AI belonged solely to engineers and data scientists. We excluded ourselves from conversations that deeply affect the futures of our languages and cultures. But then I recognised that our absence was the gap and our inclusion is the opportunity. Working with indigenous African languages, I saw how AI systems often mistranslate, misrepresent, or ignore them entirely. Training AI on these languages isn’t just a technical task — it’s a cultural necessity. Without it, Africa’s digital future risks being shaped by systems trained on foreign values. Inclusive AI can empower communities to define themselves in digital spaces not as data points, but as agents of meaning.

How does your work with low-resource languages move the needle for data and AI for social impact work? What are some of the biggest challenges you have faced in doing so?

My work with low-resource African languages advances AI for social impact by centering people, not just data. I come from a literary and linguistic background, and I approach this work by asking: What’s the best way to engage with these languages meaningfully? That question continues to guide me. One of my biggest challenges is holding deep conversations with data scientists and asking hard questions like: Who is this for? My role is making sure African communities are not reduced to data sources, that our cultural nuances are respected, and that this work is not treated as a niche for profit. I see myself as a bridge helping to facilitate relationships between communities and AI practitioners. For me, social impact in AI means ensuring that African languages and the people who speak them are central to the design and purpose of these systems.

What are the diverse, interdisciplinary skills that are required to do this work effectively? Which one surprised you the most?

Linguistic expertise, community engagement, ethical research practices, technical literacy, machine translation, project management, advocacy, and policy awareness are diverse interdisciplinary skills required to do this work effectively. Linguistic and cultural knowledge is foundational, especially when working with indigenous languages that carry deep histories and nuanced meanings. At the same time, you need the technical ability to navigate the language of AI, machine translation, and data ethics — even if you’re not building the models yourself.

The skill that surprised me the most was community engagement. I had underestimated how central it would be to the success of AI projects involving low-resource languages. Building trust, working ethically with people, and communicating across power dynamics are not side tasks — they are the core of the work. Without community participation, even the most accurate models fall flat in impact and relevance. This work doesn’t sit neatly in one discipline. It thrives in the space between them, and that’s where I’ve found my purpose. Being able to connect the dots, sit at multiple tables, and bridge knowledge systems is what allows me to push for more inclusive, culturally grounded AI in Africa.

What key responsible practices should AI practitioners prioritize when developing and training AI systems in African—or other low-resource languages?

Key responsible practices include transparency about how data will be used, co-designing projects with language speakers, and ensuring that communities benefit from the tools being developed. AI practitioners must also avoid extractive data collection, where languages are sourced for model training with little regard for who owns, controls, or understands the outcomes. Community trust isn’t just important – it’s essential. Without it, you may get data, but not meaning. Communities need to see themselves reflected in the process, have access to the outputs, and feel respected in how their languages and stories are handled. This is especially true in African contexts where colonial histories have left deep scars around knowledge extraction. Guardrails should include ethical review processes tailored to cultural contexts, open dialogue between technologists and language practitioners, and mechanisms to track and respond to potential harm. Inclusion must be more than representation; it must be active collaboration. Ultimately, AI systems built for low-resource languages will only be sustainable if they are built with the people who speak them.

What is the importance of cross-sector collaborations in building inclusive AI? What advice would you offer to people interested in this work?

Cross-sector collaboration is essential to building inclusive AI because language equity cannot be solved by one field alone. Technologists bring the tools, but linguists, cultural workers, educators, and communities bring the context. Without that blend, we risk building systems that are technically impressive but socially disconnected. In my work, I have seen how the most meaningful AI projects emerge when people from different sectors come together to listen, challenge assumptions, and co-create new approaches. To those interested in AI language equity, my advice is simple: start where you are, and bring your full skillset. You don’t need to be a coder to matter. You need curiosity, humility, and a deep respect for the languages and people you’re working with. Learn to speak across disciplines. Ask hard questions about ethics, power, and access. And most importantly, remember that inclusion is not just about who’s in the room, but about who gets to shape the outcome.

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Tell us about your work with the Capacity Accelerator Network. What impact or outcome are you most excited or encouraged by? How do you measure your success?

I worked with the Research and Insights team at an organization called Janaagraha, a well-known foundation in India that works with governments and citizens to improve the delivery of infrastructure and services in urban areas.

The project I worked on focused on identifying pathways for cleaner energy transitions in household fuel usage among the urban poor in the state of Odisha. My primary responsibility was analyzing a survey dataset of over 5,000 respondents, which provided insights into household fuel usage and behaviour in low-income settlements. The goal was to identify potential cleaner energy sources, with a specific focus on cooking fuels.

Additionally, I worked on developing tools to:

1. Link cooking fuel usage to health outcomes, one of the key evaluation criteria for cleaner fuels.
2. Understand the costs associated with transitioning to cleaner fuels.

Given the substantial body of research linking adverse health outcomes to indoor air pollution caused by traditional cooking fuels, I was particularly excited about quantifying the costs of transitioning to cleaner cooking fuels. I was also excited to work with spatial data, and provide a visual map of fuel usage across Odisha. 

For me, success meant two things: first, completing the project deliverables to meet my team’s expectations and achieving the outcomes I had envisioned at the outset. Second, and more challenging in the short term, was creating outputs that could be used by relevant stakeholders to inform their decision-making processes.

How has your approach and work evolved based on what you have learned and observed from your colleagues across the CAN network?

As the point person for data analysis on this project, I relied on the CAN network to identify spatial datasets at the district and sub-district levels and to navigate reliable publicly available health datasets. It pushed me to seek help – reaching out to colleagues from other projects, clearly explaining the outputs I wanted to achieve, and leveraging their expertise and contacts to support my work.

From my colleagues at Janaagraha, I learned how to meaningfully integrate different research methods and analyses with inputs from stakeholders across community, government, and industry, creating a cohesive and comprehensive framework for the study. Specifically, I gained valuable experience in co-creating energy pathways with inputs from local community members, and in designing a representative sampling approach in the absence of administrative data.

There can be a disconnect between academia or government institutions and social impact organizations doing the work on the ground. How do you build trust and increase adoption?

From my experience, the following approaches have proven effective:

1. Involving relevant stakeholders in the design process. It is essential to take time to understand specific objectives and activities of the government and other implementation or policy partners, and try to engage with them at each stage of the project. This could be done by providing regular status updates, incorporating stakeholder feedback, and having a primary point of contact, among others. It is not possible to achieve this for every project, though, as it really depends on the scope of work and nature of your partnerships. 
2. Recognizing that communication and advocacy are integral to the research process. In many cases, research efforts end with the publication of a paper or presentation. However, building trust and fostering adoption requires actively promoting your research and tailoring communication to meet the needs of each stakeholder. This process demands significant time, resources, and persistent effort but is crucial to ensuring that your work is meaningfully utilized.

It is important to note that despite identifying the best approaches to minimize disconnect, you can still be constrained by the initial theory of change design, funding, organizational capacity, and your own connections with the government and other stakeholders. Transparency about those kinds of limitations can help maintain trust and confidence.

How is your data-driven work driving impact at the intersection of climate and health? What is the importance of an interdisciplinary approach to data training?

Using the National Family and Health Survey, I explored correlations between household cooking fuel choice and related health impacts, such as heart disease and respiratory issues on adult women, caused by indoor air pollution. I also spatially mapped this data at the sub-district and cluster level in Odisha to identify areas where this correlation was strong. 

In the case of my project, it became clear that we needed more robust data to measure these linkages, and that the sample size at smaller geographic units was not representative enough to draw localized insights. 

An interdisciplinary approach to data training is very important as it helps you ask the right questions you want from your data. If you are a generalist in the data space, you are usually connected with domain experts to advise you. In the absence of such experts, it is essential to have training in reading policy reports and research papers to help you understand a particular sector or linkages between two or more sectors. For example, such an approach can enrich your insights by contextualizing your analyses based on various demographic cuts, such as geography, caste, gender etc., that you may apply having had such training.

What advice do you have for data practitioners as they begin purpose-driven careers? Why should they apply their skills in the social impact sector?

Having worked previously in the corporate sector, I think there is a difference in rigor applied to designing and achieving goals using data between the private and public sector. While results and impact-driven work is normalized in the private sector, it is often less mature in the government and social impact sector. This is why we need skilled data personnel in the social impact sector who can improve service delivery through monitoring and reporting, and who can help develop quantifiable goals and measure the impact of programs so that funds are channeled to the most effective and efficient policies. 

My advice to data practitioners is that there is a trade off to working in the social impact sector, and to not be discouraged by the bureaucracy and limited resource capacity that is more common in this space. It is important to remember that you are working towards social and economic good in a sector that is meaningful to you, and that your skills are helping to improve livelihoods and address these structural challenges. 

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Tell us about your work with the Capacity Accelerator Network. What impact or outcome are you most excited or encouraged by? How do you measure your success?

My work with the Capacity Accelerator Network (CAN) focuses on leveraging data science to drive climate and health research through interdisciplinary, data-driven approaches. A significant outcome of this work is translating research findings into actionable insights. One of my key initiatives involved linking climate variability with malnutrition rates and birth outcomes across Indian districts. Using two rounds of nationally representative National Family Health Survey datasets, the project establishes a robust baseline assessment of climate change’s impacts on children’s nutritional outcomes in India.

This research provides a spatial baseline of the health infrastructure’s capacity to deliver essential care for women and children in drought-prone districts. Identifying hotspot areas where health systems need strengthening helps address the projected impacts of climate change effectively.

I measure success by how well my research translates into actionable insights and how these learnings contribute to future projects. Moving forward, I aim to further my career in data-driven policymaking, focusing on sustainable and impactful solutions.

How has your approach and work evolved based on what you have learned and observed from your colleagues across the CAN network?

Collaboration within the CAN network has profoundly influenced my approach and broadened my perspective. Engaging with colleagues from diverse disciplines has highlighted the importance of adapting global frameworks to regional contexts. 

For instance, insights from the network encouraged me to incorporate additional indicators into climate vulnerability assessments, creating a more comprehensive understanding of how climate change affects health. Initially, my work took a single-lens approach, but collaboration exposed me to innovative datasets and methods, helping me analyze climate and health pathways through multiple lenses. This interdisciplinary mindset has significantly enhanced my ability to generate actionable insights.

The collaborative environment has also enriched my technical expertise in data science, equipping me with innovative methods and practical strategies to tackle real-world challenges. This ongoing exchange of knowledge and capacity-building training sessions have allowed me to continuously improve the impact of my work.

There can be a disconnect between academia or government institutions and social impact organizations doing the work on the ground. How do you build trust and increase adoption?

To bridge the disconnect between academia, government institutions, and social impact organizations, it is crucial to establish a robust evidence base that serves as a shared foundation. Involving all stakeholders—government institutions, academia, and social impact organizations—at every stage of the process is essential, from evidence generation to decision-making and implementation.

Building trust requires transparency and consistent communication. A participatory approach ensures that stakeholders feel valued and are more likely to adopt and sustain proposed solutions. This collaboration not only aligns goals across groups but also enhances the relevance and scalability of interventions, fostering long-term trust and impact.

How is your data-driven work driving impact at the intersection of climate and health? What is the importance of an interdisciplinary approach to data training?

My data-driven work identifies and addresses vulnerabilities at the intersection of climate and health, focusing on the needs of vulnerable communities. By integrating climate data such as rainfall variability and droughts with health indicators like malnutrition rates and maternal health, I identify hotspots and prioritize interventions for regions most at risk.

I believe an interdisciplinary approach is critical to understanding the complexity of climate-health linkages. These interconnected issues require perspectives from various fields to develop nuanced insights and effective solutions. This holistic understanding is pivotal for sustainable interventions.

Interdisciplinary training plays a vital role by equipping practitioners with the ability to analyze complex datasets while understanding their broader societal implications. For instance, training in tools like Geographic Information Systems (GIS) empowers professionals to visualize and act on the intricate connections between climate and health, fostering both technical competence and impactful decision-making.

What advice do you have for data practitioners as they begin purpose-driven careers? Why should they apply their skills in the social impact sector?

For data practitioners starting their careers, my advice is to align your technical expertise with a clear social purpose. Understand the needs of underserved communities and design solutions that incorporate their priorities and feedback.

The social impact sector provides unique opportunities to witness the tangible benefits of your work, whether improving public health systems or addressing climate risks. Applying data for social good allows practitioners to address systemic inequities and contribute to solving urgent societal challenges. The work is deeply fulfilling, offering both a sense of purpose and a chance to make lasting contributions to the public good.

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Tell us about your work with the Capacity Accelerator Network. What impact or outcome are you most excited or encouraged by?

I am thrilled to have the opportunity to work with students from the Capacity Accelerator Network for the third consecutive year. As part of the Data Science for Social Impact Summer Experience, I teach a data science “crash course” and mentor groups of CAN students on real-world projects. In 2022, I worked with a group of students to analyze traffic stop data for Business Professionals for the Public Interest who work to improve legislation around biased police practices. Last summer, my students and I analyzed geospatial data capturing where different pesticides are applied for Californians for Pesticide Reform. Through our work, students see how data science can be applied to do good in their community.

Working with CAN students has been incredibly fulfilling for me. Witnessing their growth as aspiring data scientists throughout the program is truly rewarding. They learn a lot of material at a very quick pace, not only through formal lessons but through the experiential learning involved in their research project. The program is meant to jumpstart interested students’ data science careers. This is really important since we need more data scientists with diverse backgrounds and experiences. I have personally seen and experienced how crucial it is to feel represented and heard in your field. That’s why I am most excited when I see an increase in their confidence in their abilities and their sense of belonging in this field as the program progresses.

What are some of the challenges of doing this work? Which were anticipated, and which unexpected?

Developing a curriculum that takes into consideration the differing levels of prior knowledge among students and aims to foster equal opportunities for learning has been one of the major challenges of this work. Data science is an interdisciplinary field, incorporating elements of statistics, computer science, and research methodology, and students often have varying levels of experience with these different aspects. For instance, some students are highly skilled in mathematics but have no experience in coding, while others are proficient coders but have never taken a statistics course. And some students may not have had the opportunity to be exposed to much of either. This challenge is also present in my work at both the University of Chicago and the City Colleges of Chicago. The introductory data science material must meet a variety of needs to ensure that students are well-prepared for the next stage, whether that be a research project or a course in advanced machine learning. 

How has your approach and work evolved based on what you have learned and observed from your colleagues across the CAN network?

My teaching approach has certainly developed through my involvement with CAN. I’ve learned a great deal from conversations with faculty members at Truman College, especially about how to effectively teach students from different educational backgrounds. Talking with educators like Kate Connor has been really valuable—it’s given me a sense of support and guidance as I navigate my role as a young professor.

There can be a disconnect between academia or government institutions and social impact organizations doing the work on the ground. How do you build trust and increase adoption?

This is a very important question, and it’s been a topic of intense discussion among members of the data science community. Drawing from my education and personal experiences, I believe it’s crucial to involve social impact organizations and the people they work with in every step of our projects. By keeping these organizations meaningfully informed and involved, we not only enhance transparency but also foster a sense of ownership, which is fundamental for building trust and increasing adoption. After all, these tools are being designed for use by these organizations, so it really makes sense to solicit their input for the design process. This collaborative approach also ensures that we are tackling the most pressing issues as identified by those directly impacted, leveraging their domain expertise to keep our work both relevant and enduring. 

During the CAN DSSI Summer Experience, we emphasize the importance of this collaboration, and students regularly engage with organization members through Zoom calls to present their progress. This model mirrors our approach at the University of Chicago in the DSI Clinic (an experiential learning opportunity for data science majors), where students routinely cite their interactions with clients as one of the most impactful parts of the experience.

The US CAN playbook Data Science for Social Impact in Higher Education: First Steps, provides educators with a range of ways to bring data for social impact to students. The playbook also includes ways to embrace social impact and ethics, elevate support and reduce barriers, and engage partners. How are these topics useful as you bring the UChicago DSI course to City Colleges of Chicago?

These topics are a key component of my curriculum planning for the data science sequence at City Colleges of Chicago. Part of the motivation for bringing these courses to the City Colleges is to make data science education more accessible to students. During the courses, we have built up a strong support network for students including lab sessions, office hours, and discussion boards. I also encourage peer support and community building by promoting an environment where students can collaborate, answer each other’s questions, and talk about common concerns or sources of confusion. In the classroom, I work to build a community where students feel comfortable asking questions, commenting on material, and connecting what they learn to their own experiences. We discuss ethics and social impact using case studies and topical data sets. For example, we analyze data on bias in policing and trends in college admissions. We also take time to explore datasets that are more lighthearted yet still relevant to students, such as Spotify listenership. Through these discussions, we aim to maintain an atmosphere of curiosity as well as mutual respect and support. This approach helps students engage with and learn from difficult social impact and ethical questions. I have found that this approach motivates students to question and explore data beyond what may be asked in an assignment.

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Tell us about the role of data collection, analysis, and action in your work. What significant findings have emerged from it and what impact or outcomes have you observed? 

At Legal Dignity, we use data collection and analysis to address gender-based violence and support sexual and gender minorities in Malaysia. By gathering and studying relevant data, we identify areas of concern, monitor progress, and create evidence-based interventions. 

These insights guide the development of policies and laws promoting gender equality, protecting LGBTIQ+ individuals, improving access to justice, and tackling systemic discrimination. 

Data also helps prioritize awareness-raising, public dialogue, and advocacy efforts for positive change. With data-driven knowledge, we can target specific intervention areas and implement focused programming and projects to improve outcomes for the LGBTIQ+ community.

Our data revealed significant patterns of gender-based violence, discrimination, and harassment against LGBTIQ+ people in Malaysia. A 2022 survey showed disparities in their access to justice, influenced by other social identities like race, religion, and socioeconomic level. These systemic barriers must be addressed to improve justice system accessibility and equity.

When did you first recognize a gender challenge that could be addressed through data? Was there a personal motivation to delve into this area of research? 

Data is the lifeblood of the work I do at Legal Dignity, and through my experience, I’ve become aware of a significant and worrying issue: the acute lack of gender-disaggregated data in government datasets. 

This not only means we can’t precisely identify the number of LGBTIQ+ individuals harmed by the inaccessibility to justice, but it also makes it difficult for our organization to analyze the effectiveness of existing programs. 

To bridge the gap in gender-disaggregated datasets, we are taking on the responsibility of developing and utilizing our own data. We launched a nationwide survey to determine the scope of injustice in relation to access to justice among Malaysian LGBTIQ+ people. 

Our data collection provides crucial insights into the LGBTIQ+ community’s experiences and needs and allows us to develop and tailor targeted programs, addressing the specific challenges that our data reveals in accessing justice and navigating the legal system. 

What are some of the challenges of doing this work? Which were anticipated, and which unexpected? 

LGBTIQ+ individuals often face significant stigma and discrimination in various countries, including Malaysia, where same-sex sexual behavior is criminalized and freedom of expression and association for LGBTIQ+ individuals is restricted.

For these reasons and more, people hesitate to openly share their experiences, and the hidden nature of this population makes gathering comprehensive and inclusive data challenging. Additionally, the lack of sufficient government support or funding for data-gathering activities targeting this population further compounds the difficulties.

As a result, we approach data collection with the utmost care, recognizing that asking individuals about sensitive topics like sexual orientation and gender identity can put them at real risk. Researchers must navigate intricate ethical considerations, emphasizing confidentiality, privacy, and participant safety, which ultimately impacts the accuracy and reliability of the data collected.

To overcome these challenges, building trust and cooperation with the LGBTIQ+ community is crucial for successful data collection, and establishing partnerships with community leaders can help facilitate data collection efforts so that, ultimately, we can get larger, more representative datasets.

How did data.org’s Gender Data 101 course influence your perspective on your work, and what were the most significant insights you gained from the course?

The Gender Data 101 course begins with the basics — how does gender intersect with race, class, and sexuality? The training helped me fully comprehend the scale of gender complexity, which is essential for data analysis and interpretation.

Moreover, the course provides step-by-step processes and best practices for conducting gender-responsive data collection, ensuring data is appropriately disaggregated by gender and other relevant variables. By following this process, I have been able to identify and address gender biases in datasets effectively. The course also includes real-world case studies that have greatly aided my understanding of theory-to-practice implementation. 

Armed with this comprehensive framework, I am better equipped to recognize and rectify gender biases and disparities within datasets. As a result, my work fosters a more accurate and nuanced understanding of the issues at hand.

How do you plan to apply the knowledge and insights gained from the course to your specific work involving gender and data?

By incorporating the data lifecycle methodology that the Gender Data 101 course taught, we have been able to ensure that gender analysis is integrated into every stage of our research processes — from the planning and design phase to data collection, analysis, and interpretation. This methodology also helps us identify potential biases, gaps, and limitations in our research, allowing us to generate more reliable findings.

The gender analysis frameworks in the course also offer us a structured way to examine the differential impacts of our research on different gender groups and, in turn, will inform our advocacy as we work to better understand how gender intersects with other social identities and influences access to justice.

To ensure that these valuable insights and approaches are consistently applied across our organization, we are in the process of translating and summarizing the course content into a policy for research work. This policy will serve as a reference and guide for all researchers at Legal Dignity, ensuring that gender analysis is a fundamental component of their work.

What are your hopes for the future? What will more, better, and better-applied data change? 

Improved Decision-Making: With access to more comprehensive and accurate data, decision-makers across various fields can make more informed and evidence-based decisions leading to more effective strategies and policies to support marginalized groups.

Evidence-Based Social Interventions: Better data and its effective application can contribute to addressing pressing societal challenges, such as poverty, inequality, and climate change through evidence-based policies, targeted interventions, and sustainable development initiatives.

Enhanced Efficiency and Productivity: Better data and its application can streamline processes, automate repetitive tasks, and optimize resource allocation leading to increased efficiency, reduced costs, and improved productivity in organizations working to make social change. 

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Tell us about the role of data collection, analysis, and action in your work. What significant findings have emerged from it and what impact or outcomes have you observed? 

I work with both public and private organizations on how to use gender data to inform their work. Collecting and analyzing data is key from the beginning of any engagement — not only to provide facts to those who are still skeptical, but also to identify an organization’s specific needs and measure the results of any intervention. 

Just asking the question “Do you know how many women are part of your clientele or workforce or recent promotions?” moves the needle because more often than not, the answer is “no” or “yes” but only in absolute numbers without real analysis.

Recently, I worked with a financial institution that wanted to strengthen the gender perspective of its client portfolio, particularly in rural areas where they suggested that the local culture made it difficult to include women. Understanding the local dynamics was critical, as many of the potential clients were actually partners of the current male clients. 

The problem was that the institution had a credit limitation per household. In other words, the credit provided to the man limited the amount left to be offered to the women of that household. Data was key, but going beyond sex-disaggregated data and understanding social dynamics was also essential to identifying a solution for women´s financial inclusion.

When did you first recognize a gender challenge that could be addressed through data? Was there a personal motivation to delve into this area of research? 

It became clear to me that gender was a critical component of solving complex challenges when I kept encountering the “whys” every time I would assert a gender lens. I’d be asked: Why are we talking about gender in water and sanitation? What does gender have to do with this technical issue? The more technical the person was, the more I realized I needed to show facts and numbers to get the point across. 

But not just any number. I came to realize just how important it is to consider biases and understand the context around numbers to provide accurate and insightful information. How we present, collect, and interrelate data can create entirely different stories.

Realizing the power of data analysis motivated me to learn as much as possible. This understanding gave me the tools to convince others that applying a gender lens makes our economy and community stronger. 

What are some of the challenges of doing this work? Which were anticipated, and which unexpected? 

Anticipated: Official data is insufficient and changing these systems is slow and expensive. Governments and corporations are just starting to collect some key gender indicators and understand how to analyze them. Collection methodologies differ from one country to the other, or even between organizations, so comparison or additionality is difficult, if not impossible.

In smaller interventions, it is expected for participants to answer surveys to get data, and it is hard to get a high response rate. Usually, participants (for instance, women entrepreneurs) have little time for additional activities or do not check emails. There is often a lack of trust or a misunderstanding of what is being asked. The design of surveys is not as straightforward as it may seem.

Unexpected: There is, at times, a disconnection between who designs the indicator, who collects it, who has the information, and who is going to use the data. So, often we find forms incomplete or systems that don’t understand how to process the information received. 

How did data.org’s Gender 101 course influence your perspective on your work, and what were the most significant insights you gained from the course? 

I decided to take the Gender 101 course because I believe in continual learning and I wanted to obtain technical insights to improve my work. One of the learnings I have applied is the understanding that the people who provide the data are the owners of the data and we are asking them — for free — to provide their valuable time and share their information. It is important to make clear why we are collecting data. And it is important for data collectors to pause and ask themselves why. Is it just because I find it interesting, or is that data going to benefit those who provided it? I think it is very important to reflect on the use and need of data before designing and requesting nice-to-have indicators.

Additionally, I came to recognize that what we measure and what we publish is a political decision. There is a reason behind what we collect and the story we expect to tell with that data. Gender 101 helped me to become conscious of this — both when I make decisions and when I read research papers on gender statistics.

Finally, biases. In my job, I talk a lot about biases, but I had not related them to data and analysis before. Or at least not so clearly. This has been very eye-opening, and I have taken bias into account in the design of a survey just recently. 

How do you plan to apply the knowledge and insights gained from the course to your specific work involving gender and data?

I design surveys and questionnaires as part of the projects I manage and I am constantly reading research on gender topics in different sectors. After taking Gender 101 I now know how to identify and avoid potential bias in data collection and analysis

I am also eager to learn more about visualization techniques after the course as it was clarifying to understand that the way we present data can actually misrepresent the truth if we are not careful. 

I will also make sure that no private data is shared without the permission of the owners of that data. This part looks obvious, but it is so important. I intend to take a course on cybersecurity to continue learning about this issue.

What are your hopes for the future? What will more, better, and better-applied data change? 

On the one hand, I wish for a future in which more gender data is collected and taken into consideration intersectionally to create equity across policies, products, services, solutions, and more. The use of this data should benefit those who have provided it, and they should have ownership of the data. But for this to happen, there must be a real understanding of what the data is saying because the same data can be used to tell different stories. 

There is also already a lot of interesting data available, but it is not shared or known. Before starting whole new research, asking the same people, again and again, let’s search what data is already available and could be useful for the objectives we have. And let´s share lessons learned to make the process more efficient. 

The post 5 Minutes with Cristina López Mayher appeared first on data.org.

Tell us about your work on gender and the role of data collection, analysis, and action. What significant findings have emerged from your work and what impact or outcomes have you observed? 

My work at the Aspen Network of Development Entrepreneurs is focused on building a stronger ecosystem for entrepreneurs in India and the broader South Asian region through strategic collaborations with the members of the organization as well as convenings, research, and advocacy. Over the past few years in our work, the urgency and importance of incorporating a gender lens to understand what challenges women entrepreneurs face has become very apparent and we are working to help bridge these gaps at a systemic level. But to make real change, access to gender-disaggregated data (both academic and practitioner-friendly) becomes pivotal to ensure any interventions are evidence-driven to the greatest extent possible. 

The issue briefs that I’ve co-authored highlight the status quo of women entrepreneurship in India and the significant challenges that women entrepreneurs face, most notably the financial gap. According to the IFC, this gap is nearly $320 billion in developing countries, and what’s more, they estimate that 70 percent of women-owned small and medium enterprises have inadequate or no access to financial services. It is also widely documented that the female labor force participation rate in India is among the lowest in the world. 

As we grapple with these numbers and qualitative evidence that women entrepreneurs continue to face disproportionate obstacles to success, the role of data collection and analysis becomes all the more critical to paving the way for the evidence-based change we are seeking.

When did you first recognize a gender challenge that could be addressed through data? Was there a personal motivation to delve into this area of research? 

Data plays a strong role in building a narrative toward driving change. More often than not, it is not just the mere numbers but how it is presented to the stakeholders involved in decision-making that makes the real difference. For example,  when the statistics on the female labor force participation rates were released, the numbers shocked me since in my silo as an educated, urban-dwelling woman in India, my friends and I were the exceptions and not the norm. Seeing this data was a major shift and inspired me to get my hands on as much sex-disaggregated data related to entrepreneurship as possible to begin understanding the extent of the gaps in the ecosystem.

What are some of the challenges of doing this work? Which were anticipated, and which unexpected? 

There is not enough (and in some cases, none at all) data! To date, there is a lack of extensive detailed survey data on women’s entrepreneurship (covering different stages of entrepreneurs across all geographies) in the country to help provide a baseline for stakeholders to build relevant support programs and initiatives.

How did data.org’s Gender 101 course influence your perspective on your work, and what were the most significant insights you gained from the course? 

The Gender 101 course helped build a framework for how to understand gender and data, along with concrete action steps that one could incorporate into existing program interventions at their organizations.

Plus, the access to valuable resources — both through the course as well as those shared by the cohort participants — was truly helpful. 

One of the main insights for me was how to contextualize the data. More often than not we fall in the trap of presenting too much data in the belief that it helps in making our case stronger. Based on the frameworks from the Gender 101 course, I now know it’s more important to keep the stakeholder in mind and analyze and present data accordingly. I also better understand that data is not necessarily always objective — it depends on how it’s collected, who analyzes it, who presents it, and who it is presented to.

How do you plan to apply the knowledge and insights gained from the course to your specific work involving gender and data? 

My initial steps will be to simply engage with as much data as is available to comprehend the status quo of the ecosystem. What are existing support systems for women-led businesses looking like? What are the major systemic gaps? What are the opportunities that could catalyze the growth of existing businesses? Next, I would help bring to the forefront the role strategic collaboration can play in supporting women entrepreneurs, as no single organization can take this mandate on alone. 

What are your hopes for the future? What will more, better, and better-applied data change? 

More women joining the workforce. More women retained in the workforce. More women-led businesses being built and scaled towards greater impact and significantly more capital flowing into this space. 

The post 5 Minutes with Saipriya Salla appeared first on data.org.

Tell us about your work on gender and the role of data collection, analysis, and action. What significant findings have emerged from your work and what impact or outcomes have you observed? 

At CCHUB’s Tech & Society Practice, we firmly believe that safe and unrestricted access to the Internet is a fundamental right for everyone. My work involves helping people grasp the basic concepts of digital security and emphasizing the importance of online privacy.  

To accomplish this, I collaborate with local partners who assist us in implementing various training, intervention, and technical support projects for organizations across Africa. What’s been interesting is that as we’ve assessed the impact of our initiatives, we’ve been able to identify a correlation between women’s online security and their experiences within broader societal circumstances. 

For instance, Nigeria is experiencing extreme inflation right now, and a substantial number of women sell groceries in the markets — they’re the ones doing most of the trading, and they’re also the ones financially supporting their families.

But the current economic situation is creating new financial constraints, which can cause or exacerbate domestic abuse within these women’s households. At the same time, they are confronted with the need to open bank accounts or secure online loans — something they’ve never done before — which puts them (and their data) at great risk for cyber targeting.  

That’s where our cybersecurity training can help. We focus on empowering women to become technically savvy with the ultimate goal of becoming financially independent. Because honestly, that’s the easiest way to get them out of the situations they’re in at home. And that’s what I’m really passionate about — making sure women can improve their personal lives.

When did you first recognize a gender challenge that could be addressed through data? 

When I started this job, my primary goal was to ensure compliance with security policies. But, during the pandemic, I realized there was a deeper need for people on the ground to learn about online safety — and that this was something that I could do to really contribute to the broader culture.

The number of people using the internet or smartphones grew rapidly during the pandemic when face-to-face interaction was sometimes impossible. And with so many new, and often naive, technology users comes an increase in online security threats — particularly our most vulnerable populations, including women, children, the elderly, and the LGBTQ community.

For instance, organizations and individuals working with human rights defenders often seek our assistance. These people are tackling issues that are considered outside of the mainstream, such as writing articles or posting social media about LGBTQ people in Nigeria, despite the laws that criminalize unions or same-sex marriages. Not only are these individuals at risk, but the people working to amplify their voices are also a target of online attacks — whether on their assets or even on their physical safety. 

What are some of the challenges of doing this work? 

One of the challenges I face is the perception among some cybersecurity and technology professionals that people-facing roles are not as serious or valuable. As someone who transitioned from a law-related career to technology, I have obtained cybersecurity certifications and developed expertise in both technology and communication. Bridging the gap between understanding cyber security and people is crucial because they are often the weakest link in information security. I strive to break down advanced online security concepts in a way that non-technical individuals can understand to keep themselves and their data safe. 

In addition to these challenges, the technology industry remains predominantly male. Even when women enter the field, they are often confined to non-technical roles or face biases that question their competence. Overcoming these biases and encouraging more women to pursue technical positions in cybersecurity is essential for fostering diversity and expertise in the industry.

How do you see your work with gender and data evolving in the future? 

I want to expand my work with organizations that focus on individuals, particularly women, who may have limited access to technology or lack knowledge about online safety. In the long run, I aspire to help people use the internet,technology, and data to improve their lives while also educating them about basic security practices that can keep them safe online.

What are your hopes for the future? What will more, better, and better-applied data change?

I’d like to see the development of a culture where digital security is ingrained at the organizational and personal levels. Data security should be an easy and natural habit for people so they don’t have to exert extra effort to protect themselves online — whether it’s simply enabling two-factor authentication or using strong and unique passwords across different social media platforms — small steps will make a genuine impact to safeguard those most digitally vulnerable, particularly women.

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As the Director of Research, Evaluation, and Learning at Equimundo: Center for Masculinities and Social Justice, Dr. Taveeshi Gupta is leading groundbreaking research on the role of gender norms in perpetuating violence against women and children, creating gender-unequal environments at home and work, and reinforcing harmful gender identities for both boys and girls. 

Tell us about your work on gender and the role of data collection, analysis, and action. What significant findings have emerged from your work and what impact or outcomes have you observed?

At Equimundo I lead the global portfolio of research and support the collection, analysis and reporting of data focused on three interconnected pillars: violence against women, equity of care, and gender socialization. Alongside stand-alone research products, we also use this evidence and data to drive our programs and advocacy efforts.

One of our flagship research projects is the International Men and Gender Equality Survey (IMAGES), which began over 15 years ago and measures not just violence perpetration, but explores men’s and women’s gender attitudes. Through quantitative and qualitative data, we dive into various aspects of participants’ development of gender attitudes, such as their childhood experiences of gender roles at home, their health and health-related practices, household division of labor, men’s participation in caregiving and as fathers, and men’s and women’s attitudes about gender and gender-related policies.

What we’ve uncovered are intriguing connections and important linkages between gender attitudes and behaviors. For instance, in nearly 32 countries where a recent global analysis was done, men who held more restrictive gender norms also had more negative health outcomes. Conversely, we found that not surprisingly one of the strongest factors associated with men’s use of intimate partner violence was witnessing their own fathers or another man use violence against their mothers. And as adults, these men were more likely to abuse alcohol, to be depressed, to have suicidal thoughts and to be generally unhappy. 

When did you first recognize there was a gender challenge that could be addressed through data? Was there a personal motivation to delve into this area of research?

My research journey started with a genuine curiosity about how immigrant parents in the US teach their kids what it means to be an immigrant and, specifically, what it means to be an American. But as I dug deeper, I noticed something fascinating: these parents weren’t just passing on lessons about being immigrants; they were also shaping their children’s identity as immigrant boys or girls. It’s like this blend of fitting in with a new culture while also adhering to gender norms dominant in American culture. For instance, the universal message that “boys don’t cry” is complicated in the immigrant context because it’s coupled with the message of fitting in in a new culture. It hit me that gender is learned — and parents, media, teachers all over the world unintentionally play a part in gendering their children. 

Then, around the same time two significant events happened that affected me personally. In 2008, ten Pakistani men associated with the terror group Lashkar-e-Tayyiba stormed buildings in Mumbai, killing 164 people. My father had been in one of the hotels ambushed by the group but luckily remained safe. A few days later, a group of young boys attacked a close friend of mine on the subway in New York City as part of a gang initiation. He was hurt but luckily he escaped. 

These events — where essentially a group of boys and men were behaving in ways that are not core to who human beings are — were a turning point for me. I began questioning everything. What causes boys and men to be willing to inflict violence? How do they learn to become these people? How is masculinity weaponized to create people capable of causing this kind of harm?

I had a deep core recognition that if we’re not talking about masculinities and collecting data on the experiences of boys and men, then we’re just not having a holistic conversation.

What are some of the challenges doing this work? Which were anticipated, and which unexpected? 

Data continues to be the best way for us to understand what’s actually going on when digging into systemic problems. And yes, having more data is helpful. But often research stops short at just quantifying care. That is so important but it’s hard to understand what is happening in people’s day-to-day lived realities. 

For instance, global data shows that women unequivocally shoulder the largest responsibilities as caregivers. So the default assumption is that men don’t want to be caregivers. But when you start collecting qualitative data at even a very basic level, this assumption doesn’t play out.

Through our qualitative research as part of Equimundo’s Equity of Care initiative, it was discovered that many men do want to be involved caregivers, but often are entrenched in structural barriers that constrain their ability to do so, such as socialization and gender norms, workplace norms, the gender pay gap, economic vulnerability, and the lack of paternity leave. So the solution needs to be both at individual level as well as at the larger, system and structural level. 

Once you start uncovering the daily lived experiences that reveal why this problem exists, you can begin to work with governments and communities to create conditions that allow people of all genders to be good and present and equitable and involved caregivers.

How do you see your work with gender and data evolving in the future? 

When it comes to the future of my work, I see a lot of exciting possibilities. The field of boys and men is continuing to expand and becoming more mainstream than ever before — but it’s also facing tremendous backlash. So at a high level, I want to find ways to talk about our work from a feminist space so that we are not inadvertently causing more harm while also finding ways to talk about how to break down the gender binary and talk about our shared humanity. 

In terms of data, I want to continue to apply an even more comprehensive and nuanced approach to both the types of data we collect and the methods we use to collect that data. This more personal insight will allow us to tailor our interventions and policies to better address the specific needs and challenges faced by different communities.

What are your hopes for the future? What will more, better, and better-applied data change?

I am hopeful that there will be an increased global focus on supporting and calling in men when it comes to challenging damaging gender norms and expectations. Gender equality is about creating equitable worlds for women and it’s about recognizing that men are also affected by societal pressures related to masculinity.

I also hope that in the future, we can put a stronger emphasis on qualitative data collection given the current donor trends. I value both quantitative and qualitative work but rarely do we get a chance to do truly mixed methods projects. Intentionally trying to inject qualitative data that gives us a more personal and in-depth understanding of what’s really happening on the ground is something I want to move the field towards. When we have the stories, struggles, and aspirations of people impacted by gender norms, it adds a whole new dimension to our understanding.

By gathering better-applied data, we can make a real difference. It can help us challenge stereotypes and misconceptions, shape targeted interventions, and drive conversations around gender equity.

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