Computational Biology to Identify Coronary Artery Related Disease in Smooth Muscle Cells, Persistent Bias in Facial Recognition: A Social and Ethical Examination of The Persistence of Algorithmic Discrimination

Sociotechnical Synthesis
(Executive Summary)
Integrating Genomic Insights and Ethical AI: Synergizing Health and Fairness in Engineering
Technology is a very human activity. Technology is neither good nor bad; nor is it neutral. Although technology might be a prime element in many public issues, nontechnical factors take precedence in technology-policy decisions.
Melvin Kranzberg

My identity is not just a demographic data point—it is the driving force behind my technological innovation. As a woman of color who has repeatedly experienced the stark limitations of facial recognition technologies, I transformed personal frustration into a rigorous scholarly pursuit. Each additional security screening, each moment of algorithmic misrecognition, became a catalyst for understanding how technology can simultaneously advance and marginalize human experiences. My academic journey this fall semester encompassed two pivotal projects: identifying genetic markers for coronary artery disease (CAD) through computational biology and examining algorithmic bias in facial recognition technologies. Engineering advancements today demand not only technical excellence but also a profound commitment to societal well-being and ethical responsibility and the core of both my research was centered around that.

In my technical project, I concentrated on uncovering the genetic underpinnings of coronary artery disease, the leading cause of mortality worldwide. Leveraging advanced computational techniques and artificial intelligence, my team and I analyzed over 200,000 RNA isoforms from individuals representing diverse genetic backgrounds and environmental conditions. This inclusive approach was essential in ensuring that our findings were unbiased and representative of various populations, thereby enhancing the generalizability and applicability of our results. Utilizing tools such as LeafCutter, Ensembl, and PacBio sequencing, we successfully identified six high-confidence genes strongly associated with CAD. These discoveries not only deepen our genetic understanding of CAD but also pave the way for personalized therapeutic strategies for patients, potentially revolutionizing treatment paradigms and significantly reducing mortality rates. The precision and innovation demonstrated in this research highlight the transformative potential of computational biology in addressing critical global health challenges

Complementing this computational biology research, my STS investigation delved into the critical ethical challenges within facial recognition technologies. Utilizing Frank Geels' Multi-Level Perspective (MLP) framework, I dissected the systemic biases embedded in these technologies, revealing a complex ecosystem of corporate resistance, regulatory gaps, and fragmented social pressures. By meticulously analyzing performance data across diverse demographic groups, the study exposed how algorithmic systems disproportionately impact marginalized communities. My personal experiences as a woman of color repeatedly misidentified by facial recognition systems transformed this research from an academic exercise into a deeply personal mission to understand and challenge technological inequities.

The profound synergy between these projects lies in their shared commitment to diversity and ethical innovation. The computational biology research champions genetic inclusivity, while the facial recognition study advocates for algorithmic fairness. Both projects demonstrate that technological excellence is meaningless without a corresponding commitment to social responsibility. This synthesis is more than an academic exercise for me —it is a blueprint for reimagining technological development. By intertwining genetic diversity with ethical AI practices, we can create a future where innovation serves humanity in all its beautiful complexity. Engineering is not just about solving technical challenges; it is about expanding the boundaries of human potential, ensuring that every individual—regardless of their background—is seen, understood, and valued. The profound synergy between these projects lies in their shared commitment to diversity and ethical innovation. The computational biology research champions genetic inclusivity, while the facial recognition study advocates for algorithmic fairness. Both projects demonstrate that technological excellence is meaningless without a corresponding commitment to social responsibility.

The sociotechnical perspective reveals how technological systems are not merely technical artifacts, but complex socially embedded networks of human and non-human actors. Through the lens of Frank Geels' Multi-Level Perspective, my research exposed the intricate interactions between micro-level innovations, meso-level socio-technical regimes, and macro-level landscape pressures that shape technological development. This approach illuminates how individual technological choices are constrained and enabled by broader social, economic, and political structures. By highlighting the agency of both human actors—such as corporate decision-makers, regulators, and marginalized communities—and non-human actors like algorithms and genetic sequences, the sociotechnical perspective transforms our understanding of technological innovation from a linear, deterministic process to a dynamic, negotiated interaction. Ultimately, this framework provides engineers with a critical toolkit for recognizing and addressing the ethical implications of technological design, moving beyond narrow technical considerations to a more holistic understanding of technology's societal impact.
At the core of this work lies a profound reimagining of technological development. These projects are not just academic investigations, but strategic interventions that challenge existing paradigms. Engineering excellence transcends mere technical proficiency. The collaboration between technical experts, social scientists, and policymakers becomes essential in developing solutions that don't just solve problems, but fundamentally transform how we understand technological potential. By exposing the interconnections between genetic research and algorithmic fairness, my research provides a provocative blueprint for technological innovation that prioritizes human complexity over computational efficiency. The future of engineering lies not in technological sophistication alone, but in its capacity to create meaningful, equitable outcomes across diverse human experiences.

School of Engineering and Applied Science

Bachelor of Science in Computer Science

Technical Advisor: Briana Morrison

STS Advisor: Kathryn Neeley