African-Americans make up 2–3% of UC Berkeley’s undergraduates. Less than 1% graduate with a CS degree. I’m proud to be on track to join that fraction, but access to opportunities must be the standard, not the exception. No one explains what a zero EFC means. It’s just a number on your financial aid award. But as an out-of-state Pell Grant student, I’ve lived that reality for three years, making decisions by need, not opportunity, with no fallback. In a single-parent home, independence is expected. I work part-time during school and full-time during breaks. That’s not a backup plan; it’s the plan. I never expected college to be easy. I expected to work hard. I didn’t know I’d end up at Berkeley, but I knew coding was my calling since age ten after a Girls Who Code event. It was my first experience of intentional inclusion. I was seen, engaged, and inspired in a tech space. Those sessions didn’t just teach me to code. They showed me what it means to be welcomed, and how much that shifts what you believe is possible. Community isn’t separate from inclusion. Community IS inclusion. It’s not just who codes, but how they’re treated, who teaches, who gets access, whose perspectives are centered, and whether they feel they matter. When those pieces are in place, that’s community. Creating spaces in tech with those values became as important to me as coding itself. Community can invite and support or quietly alienate. I’ve experienced both, and learned it comes from intentional inclusion through mentorship, representation, shared values, support systems, and leadership. In later STEM classrooms, often as the only Black girl, my earlier Girls Who Code experience became my reference point. I knew what belonging felt like, and how quickly it could vanish in spaces not built with inclusion in mind. That contrast pushed me to act and build the community I’d experienced for others who look like me. At Berkeley, I found that opportunity in ANova, a student-run organization focused on making CS education more equitable and accessible. I joined as a freshman, elected Co-Chair of Curriculum sophomore year, then VP of Sites junior year. Now, as President, I lead strategy so marginalized students see coding as a tool they can master. My academic work adds another layer. As a CS and Political Science double major, I study how code and policy interact to either reinforce or challenge systemic inequality. In one course, we built an algorithm to predict recidivism. It flagged Black defendants as higher risk at disproportionately high rates. Same data, same crime, different outcomes. What looked like neutrality was bias in code form. That reconfirmed that without lived experience, ethical context, inclusion, and representation, technical systems reinforce the same patterns they claim to fix. This is why I study both CS and political science. It’s why I lead with inclusion in every space I’m in. And it’s why I focus on equity-driven STEM work that brings in underrepresented voices as users, builders, and decision-makers. My long-term goal is to design algorithms that inform public policy without reproducing harm and teach machines to reason with equity, not just efficiency. But none of that happens without building the kind of tech community where inclusion is the foundation. I’m building a career focused on equity-driven STEM initiatives. Inclusion isn’t a technical feature you can add later. It’s a cultural one. It starts with people, and with the communities that shape how we design, deploy, and evaluate systems. That’s why I mentor, teach, and lead. Community isn’t a side interest. It’s the infrastructure. That’s why I stay.

African-American students make up roughly 2–3% of UC Berkeley’s undergraduate population. Less than 1% graduate with a computer science degree. I’m proud to be among that fraction, but access to opportunities that lead to this space must be the standard, not the exception. I was ten when I attended a Girls Who Code event and fell in love with coding. It was at those coding events that I experienced intentional inclusion for the first time. I was seen, engaged, and inspired in a tech space. Those early sessions didn’t just teach me to code. They showed me what it means to be welcomed, and how much that can shift what you believe is possible. I began to understand that community isn’t separate from inclusion. Community IS inclusion. It’s not just about who gets to code, but how they’re treated while learning, who’s teaching, who gets access, whose perspectives are centered, and whether people feel like they matter in the space. When we do those things well, that’s community. Creating spaces in tech that reflect those values became just as important to me as coding itself. Community creates the environment that either invites people in and supports them or alienates them silently. I’ve felt both. And I’ve learned that community means intentional inclusion through mentorship, representation, shared values, support systems, and leadership. At Girls Who Code, I found not only my love for coding, but also my belief in what’s possible when people are actively welcomed. Later, in STEM classrooms where I was often the only Black girl, that early moment became a reference point. I knew what it felt like to belong, and how quickly that feeling could vanish in spaces not built with inclusion in mind. That contrast pushed me to do more than just persist. I wanted to create the kind of community I’d briefly experienced, for others who looked like me. At Berkeley, I found that opportunity in ANova, a student-run organization focused on making computer science education more equitable and accessible. I joined as a freshman, elected Co-Chair of Curriculum as a sophomore, then VP of Sites as a junior. Now, as President, I lead strategy to help marginalized students see coding as a tool they can master. My academic work adds another layer. As a CS and Political Science double major, I study how code and policy interact to either reinforce or challenge systemic inequality. In one course, we built an algorithm to predict recidivism. It flagged Black defendants as higher risk at disproportionately high rates. Same data, same crime, different outcomes. What looked like neutrality was bias in code form. That experience reaffirmed that without lived experience, ethical context, inclusion, and representation, technical systems will reinforce the same patterns they claim to fix. This is why I study both CS and political science. It’s why I lead with inclusion in every space I’m in. And it’s why I focus on equity-driven STEM work that brings in underrepresented voices as users, builders, and decision-makers. My long-term goal is to design algorithms that inform public policy without reproducing harm. I want to teach machines to reason with equity, not just efficiency. But none of that happens without building the kind of tech community where inclusion is the foundation. I’m building a career focused on equity-driven STEM initiatives. Inclusion isn’t a technical feature you can add later. It’s a cultural one. It starts with people, and with the communities that shape how we design, deploy, and evaluate systems. That’s why I mentor, teach, and lead. Community isn’t a side interest. It’s the infrastructure. That’s why I stay.

My love for coding began at age 10 at a Girls Who Code event. I knew then my journey would be about building with code. Often the only Black girl in STEM classrooms, I also saw the need to make these spaces more inclusive, especially for Black women. Access to STEM should not depend on privilege. What began as a passion for solving problems became a mission to challenge how technology reinforces inequality. A decade later, as a computer science and political science double-major at UC Berkeley, I’ve found the intersection of coding and policy and my purpose within it. In one course project, we built an algorithm to estimate the likelihood of a convicted individual reoffending. While testing different models, I found that our algorithm consistently predicted higher recidivism rates for Black defendants. Despite our intent to build a neutral system, we replicated racial bias found in human decision-making. The reasons were clear: systemic discrimination, disproportionate arrests for minor offenses, and over-policing in marginalized communities. That moment showed how critical diversity and awareness are to building AI that doesn’t replicate bias. It also made me more certain that I want to be part of that work. The integration of AI into public policy is inevitable. But instead of ushering in objectivity, algorithms risk automating and legitimizing longstanding inequalities. Machine Learning trained on data shaped by systemic discrimination will inevitably reproduce that bias. This affects policy decisions in healthcare, education, housing, and other critical areas. I’m exploring how to design transparent, rigorously tested models that account for the social and historical contexts in which they operate. Too often, developers and data scientists are disconnected from the real-world impact of the tools they build. I want to help change that. I also want to change what a STEM classroom looks like and make sure more people who look like me are in those classes, if they choose to. That is why I joined Berkeley ANova, a student-run organization focused on making computer science education more equitable, available, and achievable for students of all backgrounds. I joined as a freshman, was elected Co-Chair of Curriculum as a sophomore, then VP of Sites as a junior. Now, as President, I lead our strategy and bring our mission to life, helping students from marginalized communities see coding as a tool they can master. I’m also a senior instructor at iCode, teaching youth programming fundamentals and expanded my role to teaching the teachers through workshops built on my computer science experience. This scholarship would allow me to continue my studies and focus on equity-driven STEM initiatives that advance ethical and inclusive technologies. My goal is to drive change in the tech field through design, representation, and accessibility. I’m focused on building algorithms that guide public policy without replicating the systemic bias embedded in many existing models. I’m working to teach machines to think in ways informed by ethics, context, and equity. My love for coding runs deep, and so does my commitment to change. I’m building inclusive AI systems to reshape how technology affects people’s lives, especially in marginalized communities. I’m also deeply involved in volunteer work that helps students from underrepresented backgrounds access a field they have every right to shape. Today, I’m a STEM student, a coder building real-world applications, an instructor teaching youth, and an advocate using my technical and policy education to push for equity in the field I’m committed to transforming. With this support, I can continue developing the foundation I need to drive that impact.

I was ten when I attended a Girls Who Code event and fell in love with coding. But something else happened. I experienced intentional inclusion for the first time. I was seen, engaged, and inspired in a tech space. Those early sessions didn’t just teach me to code. They showed me what it means to be welcomed, and how much that can shift what you think is possible. I started to see that community isn’t separate from inclusion. Community IS inclusion. It’s not just about who gets to code, but how they’re treated while learning, who’s teaching, who gets access, whose perspectives are centered, and whether people feel like they matter in the space. When we do those things well, that’s community. Creating spaces in tech that reflect those values became just as important to me as coding itself. Community creates the environment that either invites people in and supports them or alienates them silently. I’ve felt both. And I’ve learned that community means intentional inclusion through mentorship, representation, shared values, support systems, and leadership. At Girls Who Code, I experienced intentional inclusion. I found not only my love for coding, but also my belief in what’s possible when people are actively welcomed. Later, in STEM classrooms where I was often the only Black girl, that early moment became a reference point. I knew what it felt like to belong, and how quickly that feeling could vanish in spaces not built with inclusion in mind. That contrast pushed me to do more than just persist. I wanted to create the kind of community I had briefly tasted, for others who looked like me. At Berkeley, I found that opportunity in ANova, a student-run organization focused on making computer science education more equitable and accessible. I joined as a freshman volunteer and steadily took on more responsibility, becoming Co-Chair of Curriculum, then VP of Sites, and now President. My academic work adds another layer. As a CS and Political Science double major, I study how code and policy interact to either reinforce or challenge systemic inequality. In one course, we built an algorithm to predict recidivism. It flagged Black defendants as higher risk at disproportionately high rates. Same data, same crime, different outcomes. What looked like neutrality was bias in code form. That experience reaffirmed that without lived experience, ethical context, inclusion, and representation, technical systems will reinforce the same patterns they claim to fix. This is why I study both computer science and political science. It’s why I lead with inclusion in every space I’m in. And it’s why I focus on equity-driven STEM work that brings in underrepresented voices as users, builders, and decision-makers. My long-term goal is to design algorithms that inform public policy without reproducing harm. I want to teach machines to reason with equity, not just efficiency. But none of that happens without building the kind of tech community where inclusion is the foundation. I’m building a career focused on equity-driven STEM initiatives. Inclusion isn’t a technical feature you can add later. It’s a cultural one. It starts with people, and with the communities that shape how we design, deploy, and evaluate systems. That’s why I mentor, teach, and lead. Community is not a side interest. It’s the infrastructure. That’s why I stay.  Thank you for your consideration. Camille Edwards UC Berkeley, Class of 2026 Hometown: Pearland, Texas

My love for coding began at age 10 at a Girls Who Code event. I knew then my journey would be about building with code. Often the only Black girl in STEM classrooms, I also saw the need to make these spaces more inclusive, especially for Black women. What began as a passion for solving problems became a mission to challenge how technology reinforces inequality. Now, as a computer science and political science double major at UC Berkeley, I’ve found purpose at the intersection of coding and policy. In a course on decision-making algorithms for public policy, we used large datasets and built regression models to guide complex decisions. These included directing healthcare distribution and identifying optimal locations for refugee resettlement. The most impactful part was confronting that without accounting for systemic bias and discrimination, even the smartest systems can reinforce harm. In one project, we built an algorithm to estimate the likelihood of a convicted individual reoffending. While testing different models, I found that our algorithm consistently predicted higher recidivism rates for Black defendants. Despite our intent to build a neutral system, I had unintentionally replicated the same racial bias found in human decision-making. Our professor asked us to reflect on why the algorithm produced those results. The causes were clear to me: systemic discrimination, over-policing in marginalized communities, disproportionate arrests for minor offenses, and harsher sentencing all create biased data. But when I discussed it with classmates, many only knew fragments of that reality. That moment reinforced how essential diversity and awareness are in building AI systems that don’t replicate bias. It also made me more certain that I want to be part of that work. The integration of AI into public policy is inevitable. But instead of ushering in objectivity, algorithms risk automating and legitimizing longstanding inequalities. Machine Learning trained on data shaped by systemic discrimination will inevitably reproduce that bias. This affects policy decisions in healthcare, education, housing, and other critical areas. I’m exploring how to design transparent, rigorously tested models that account for the social and historical contexts in which they operate. Too often, developers and data scientists are disconnected from the real-world impact of the tools they build. I want to help change that. I also want to change what a STEM classroom looks like and make sure more people who look like me are in those classes, if they choose to. That is why I joined Berkeley ANova, a student-run organization focused on making computer science education more equitable, available, and achievable for students of all backgrounds. I joined as a freshman, was elected Co-Chair of Curriculum as a sophomore, then VP of Sites as a junior. Now, as President, I lead our strategy and bring our mission to life, helping students from marginalized communities see coding as a tool they can master. I’m also a senior instructor at iCode, teaching youth programming fundamentals and expanded my role to teaching the teachers through workshops built on my computer science experience. This scholarship would allow me to continue my studies and focus on equity-driven STEM initiatives advancing ethical and inclusive technologies. My goal is to design algorithms that guide public policy decisions without replicating the systemic bias found in many existing models. I’m working to teach machines how to think in ways that are informed by ethics, context, and equity. By building AI systems that are inclusive and representative, I plan to change how technology is used to shape people’s lives, especially for marginalized communities. With this support, I can keep developing the technical and policy foundation I need to make that impact.

No one tells you what a zero EFC really means. You just see a number on your financial aid award. But as an out-of-state Pell Grant student at UC Berkeley, I’ve lived what that number means for the past three years. It means budgeting with no fallback, making decisions based on need instead of opportunity, and learning how to do hard things on your own. It’s shaped how I move, how I think, and how I lead. Being admitted to UC Berkeley was a dream. I grew up in a single-parent household. My mom is a military veteran who joined the Army to build a life with more possibility. Even with her sacrifice, like many families, we lived paycheck to paycheck. Sometimes the paycheck didn’t come. Still, when I got into Berkeley, I said yes. We knew it would be expensive. Saying yes meant committing to the financial fight. As I enter senior year, I can say I haven’t stopped yet. Technically, I’m not a first-generation college student, but I didn’t grow up with the resources or insight that usually come with having a college-educated parent. My mom earned her degree while serving, so she wasn’t exposed to the college prep, selection, admission, or financial aid process. I navigated all of that on my own. My love for coding began at age 10 at a Girls Who Code event. I knew then my journey would be about building with code. Often the only Black girl in STEM classrooms, I also saw the need to make these spaces more inclusive, especially for Black women. Access to STEM should not depend on privilege. Both my educational pursuits and volunteerism are focused on making tech more representative and assessable. That is why I joined ANova, a student-run group focused on equitable access to computer science. I joined as a freshman, was elected Co-Chair of Curriculum as a sophomore, then VP of Sites as a junior, and now serve as President. I design and lead strategy for outreach programs that help marginalized students see coding as a tool they can master. I’ve seen firsthand how early, equitable exposure to tech shifts what kids believe is possible. Outside of campus, I’m a senior instructor at iCode, where I teach youth programming fundamentals and expanded my role to teaching the teachers using my computer science experience. In a course on decision-making algorithms for public policy, we used large datasets and built regression models to guide complex decisions. One project involved estimating the likelihood of a convicted individual reoffending. Despite our intent to build a neutral system, our algorithm consistently predicted higher recidivism rates for Black defendants. The causes were clear to me: systemic discrimination, over-policing in marginalized communities, disproportionate arrests, and harsher sentencing all shape the data. That moment reconfirmed how essential diversity and awareness are in building AI systems that don’t replicate bias. It made me more certain that I want to be part of that work. I’m double majoring in computer science and political science, focused on STEM initiatives that advance ethical, inclusive technologies. My goal is to design algorithms that guide public policy without replicating systemic bias. I’m working to teach machines to think in ways informed by ethics, context, and equity. This scholarship would help me continue my studies and stay focused on this work. As I enter my final year, the financial pressure is mounting. With your support, I can continue building inclusive, representative AI systems, strengthen my technical and policy expertise, and help ensure that people like me have a voice in shaping what technology does and who it serves.

One of my first experiences building a machine learning model was realizing I had unintentionally created a system that mirrored the same discriminatory decisions made against my own community. I was taking a course on developing decision-making algorithms for public policy. The course focused on working with large datasets and building regression models to develop algorithms capable of making complex decisions. I worked on projects like estimating recidivism rates, guiding healthcare policy distribution, and identifying optimal locations for refugee resettlement. However, the most impactful aspect of the course wasn’t the technical skills I gained, but the ethical challenges I explored. One of our earliest projects involved designing an algorithm to estimate the likelihood of a convicted individual committing another crime. As I tested various models, I discovered that our algorithm consistently predicted higher reoffense rates for African-American defendants compared to other racial groups. Despite our intention to build an unbiased system, I had unintentionally replicated the same racial bias present in human decision-making. Our professor asked us to reflect on why the algorithm produced these results, despite being designed to be neutral. The causes felt immediately clear to me: systemic discrimination, over-policing in marginalized communities, disproportionate arrests for minor offenses, and harsher sentencing practices all contribute to biased data. Yet, in discussing the issue with my classmates I realized they were only familiar with some of these realities. That moment made me understand how essential diversity and awareness are in developing unbiased AI systems. The integration of AI into public policy decision-making is inevitable. But instead of ushering in a new era of objectivity, AI risks automating and legitimizing longstanding inequalities. When algorithms are trained on data shaped by systemic discrimination, they inevitably reflect that discrimination within their equations. And this issue isn’t limited to public policy. It extends to healthcare, education, housing, and other critical domains. This is why I am working to make a meaningful impact in the field of AI and machine learning by ensuring that its applications in public policy promote equity rather than exacerbate disparities. I’m exploring approaches to develop transparent, rigorously tested models and designing algorithms that account for the unique social and historical contexts in which they operate. Beyond model design, I’m also engaging with the AI/ML research community by raising awareness about the biases embedded in the data we often take for granted. Too often, developers and data scientists are disconnected from the real-world implications of the models they build and the data they use. We cannot hope to design fairer systems if we don’t fully understand the problems we’re trying to solve and how these problems manifest throughout the entire pipeline of algorithm development.

When I was 10, I went to a Girls Who Code event. I got to try coding for the first time, and from there, I was hooked. Now I am 17; I have the opportunity to create my own coding project, and the passion is still there. I enjoy the struggle I face solving my compiler issues and the feeling of accomplishment once I solve them. I want to study computer science because of how limitless it is. I grew up with a family that taught me to love learning, and Computer Science builds on my love for learning because there are so many things you can learn about the field. Each task you wish to accomplish has an endless number of solutions of varying complexity, and it's up to you to figure out the best way to complete that task in a code that is your own. There is always something new when coding, whether it's a new programming language, method, or variable. Sometimes it can be daunting when faced with a new subject, but it's a challenge that I am always excited to try to take on. I look forward to using what I learn in computer science to work on projects that I believe would help other people. Projects such as developing software that could scan music and color coordinate each note to make it not only easier but more enjoyable for dyslexic musicians to learn music. Or I would want to create something that lets kids relate to the various struggles I know I faced and allow them to feel seen and heard in the tech field. Beyond my passion projects, I also want to be an advocate within the STEM field, especially in computer science. I am passionate about advocacy for minorities in STEM, particularly black women in STEM, and I plan to help increase diversity. Throughout my experience in computer science, I have often felt pretty isolated. Whether it was in clubs, classes, or volunteering, I often found myself one of the only African American students, if not the only African American student, in every activity I participated in. Beyond my frustrations of feeling left out, the lack of African American women in the STEM field is a problem for society. The lack of diversity means that less is technology targeted specifically or even considers African-American people in its design. And the lack of support within the STEM field makes it less likely for black women to pursue it as a career. Making African American women feel more welcome in the stem field will be a slow process, and because we are still at the beginning of it, my job is to help build the foundation. During my time in high school, I was frustrated with the lack of space for African Americans in the STEM program. However, now I feel guilty for not creating that space myself. I plan to support current Black coders by being there and being a familiar face, introducing young black girls to the concept of computer science, and being able to continue supporting women in stem clubs and helping to create a space for black women as well. I would want to advocate for all the various things that make me who I am and teach other younger kids that they can be African-American and can be a woman and still do the things they love. I want to make STEM more welcoming as a whole, and I will begin with my community.