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I recently had the privilege of participating in the AWS Case Competition, specifically the Cyber Trust Challenge. Our team, EHCAI (Ensuring Human-Compatible Artificial Intelligence), focused on digital trust and human judgment within the public-good track, with a special emphasis on government technology. We were drawn to this topic because digital trust depends not only on the accuracy of AI systems but also on how real people perceive, interpret and emotionally respond to the information these systems produce. With teammates from the United States, India, Nepal and South Korea, we each brought unique perspectives on global trust challenges — ranging from the fragmentation of AI governance in the U.S., to the European Union’s strict compliance standards and the cost-based barriers that lock emerging economies out of trustworthy AI ecosystems.

I still remember the moment we received the email announcing that our team had been selected. As an economics and psychology major, I have always been passionate about understanding how people think, decide and interpret information — and then translating those insights into meaningful strategies or solutions. My academic and internship experiences gave me strong foundations in consumer research, behavioral study design, data interpretation and communication. But the AWS Case Competition felt different. It was a direct invitation to step into an entirely new industry, learn about AI ethics and cybersecurity, and collaborate with a team with members whose backgrounds spanned computer science, math, sociology and psychology. I knew it would push me far outside my comfort zone, and that was exactly why I wanted to do it.

The competition unfolded over two fast-paced days. Our task was to design a solution that strengthened digital trust in the age of generative and agentic AI, using Amazon Web Services (AWS) technologies, such as Amazon Bedrock Guardrails. Our final presentation had to fit into a 12-minute pitch, followed by a Q&A session with a judging panel comprising AWS experts and MHC faculty. One of the most challenging parts came early on: learning to understand and then translate technical concepts that were completely new to me and eventually being able to explain them to a general audience without relying on technical jargon. Tools such as Automated Reasoning and AI Service Cards and foundational ideas, such as self-hosting and foundation models, felt intimidating at first. But our team set a clear principle: if we couldn’t explain a concept clearly to an everyday audience, we didn’t truly understand it.

So, we slowed down. We spent hours breaking down each tool, rewriting definitions in plain language and building analogies. I appreciated this process more than I thought I would. Not only did I learn from my brilliant teammates, but I also realized something important: when something feels overwhelmingly complex, it often means that you’re just one step away from clarity.

One of the highlights of the competition was working with our AWS coach, Jainaba Seckan, a senior program manager in Inclusive Experiences & Technology at Amazon. Throughout our preparation, she was patient, insightful and incredibly encouraging. When we met her in person the night before the competition, she gave us some of our most valuable feedback: she praised how thoroughly we had analyzed the problem and then pushed us to strengthen the delivery and to present our work not as a set of ideas but as a coherent, compelling story. That piece of advice changed the tone of our entire presentation.

On the day of the competition, our goal was simple: communicate our ideas with clarity, confidence and purpose. We aimed for a pitch that was punchy and cohesive, anchored in the theme of bridging the gap between AI capabilities and public trust. After the presentation, I asked each of my teammates how they felt. Everyone shared the same sentiment: that it was exciting to see industry experts value interdisciplinary thinking so deeply. AWS didn’t just bring a competition to campus; they brought an entire conversation about the future of AI ethics, digital responsibility and human-centered design.

While our team didn’t take home an award, the experience was invaluable. Participating in the AWS Cyber Trust Challenge pushed me to grow in ways I did not expect. I entered with strong foundations in behavioral science and decision-making but almost no background in cloud technologies or cybersecurity. The biggest lesson I learned is that technical understanding and human understanding are inseparable when designing trustworthy AI systems. You cannot talk about digital trust without talking about people: their emotions, their biases, their fears, their cultural contexts and their lived experiences.

If I could share one message with other students, it would be this: you do not need to be an AI expert to meaningfully contribute to conversations about the future of technology. Competitions like the AWS Cyber Trust Challenge are built for interdisciplinary voices, and the solutions are stronger because of it. In fact, during the Q&A portion of our presentation, most of the judges’ questions weren’t about algorithms or models; they were about human interpretation, ethical communication and public understanding.

Another insight I’d share is that the competition is, at its core, an exercise in storytelling. Whether you’re proposing a new digital trust framework, pitching a project to a potential employer or even interviewing for a job, the way you frame your message matters. Good content is necessary, but good storytelling is what makes people listen. This experience reminded me that persuasion, clarity and empathy are skills we carry into every part of our lives.