CDP Admin Tool: A Full-Stack, Centralized Web Application; Examining AI in Restructuring the Credit Scoring System

Both my technical project and STS research focus on the impact of technological innovation but in distinct contexts. The primary connection is exploring how emerging technologies transform established systems and empower diverse stakeholders. The technical project aimed to create a practical tool for non-technical users to manage complex digital infrastructures efficiently, while the STS research investigated how advanced artificial intelligence (AI) technologies can restructure credit scoring systems, emphasizing transparency and reducing systemic bias. Despite the differing motivations behind these projects, one being an industry application and the other an academic inquiry, they both reflect the broader theme of democratizing access and decision-making through technology.

During my summer internship at CarMax, I developed the Car Details Page (CDP) Admin Tool to address challenges faced by the Digital Merchandising Experience (DMX) team. The tool simplifies critical maintenance tasks such as stock-specific data regeneration, full stock regeneration, Akamai cache clearing, and performance metric visualization. This full-stack application empowers non-developers by providing an intuitive interface to perform tasks that previously required technical expertise. Alongside reducing manual workload and errors, the CDP Admin Tool supports post-outage diagnostics, contributing to the reliability of CarMax's digital platform. By streamlining workflows and providing scalable solutions, the project demonstrates how technological tools can address operational inefficiencies and enhance user accessibility.

The STS research examines the evolution of credit scoring models, focusing on integrating advanced AI technologies such as explainable AI and generative AI. The guiding question is: How can we employ AI to restructure the credit scoring system to enhance transparency, promote equality, and remove biases? The STS frameworks utilized to answer this question are Actor-Network Theory (ANT) and sociotechnical systems (TOC model), helping dissect the interplay between technological, organizational, and cultural pillars in implementing these AI innovations. This research expects to uncover how AI technologies can address the limitations of traditional credit scoring models while highlighting the necessary organizational and cultural changes required for effective implementation. Hence, the discovery process involves examining case studies of AI implementation in financial institutions and evaluating their impact on credit scoring processes. By comprehensively analyzing the current system, this study aims to promote equitable and transparent financial practices, rebuilding the trust between consumers and the credit scoring system. Additionally, this research evinces how an STS approach to an engineering system can unveil its shortcomings and outline a solution that tackles issues beyond the technical aspect.

Although I worked on these projects independently, each provided valuable insights into the interconnected nature of technological and sociotechnical challenges. Developing the CDP Admin Tool reinforced the importance of designing practical, user-friendly solutions tailored to the needs of stakeholders, particularly non-technical users. This project also demonstrated how modular and scalable designs could adapt to unforeseen challenges, providing value beyond the initial scope. Meanwhile, the STS research added a critical dimension by prompting me to think about the societal impact of technology. By applying multiple STS frameworks, I learned how to analyze systems comprehensively, balancing technical capabilities with organizational and cultural dynamics. This perspective helped me appreciate that effective solutions require more than technical excellence; they must align with user needs, institutional practices, and ethical considerations. Together, these projects highlighted the multifaceted role of engineers in developing technical solutions with a human-centered approach. Applying both practical and theoretical perspectives gave me a holistic understanding of how innovation, guided by thoughtful design and critical analysis, can drive meaningful change. This integrated mindset will guide me as I tackle future challenges, ensuring my work contributes meaningfully to both technical innovation and societal advancement.

School of Engineering and Applied Science

Bachelor of Science in Computer Science

Technical Advisors: Briana Morrison, Rosanne Vrugtman

STS Advisors: Bryn E. Seabrook, Kathryn A. Neeley