RESUMEN
Many approaches in biomedical informatics (BMI) rely on the ability to define, gather, and manipulate biomedical data to support health through a cyclical research-practice lifecycle. Researchers within this field are often fortunate to work closely with healthcare and public health systems to influence data generation and capture and have access to a vast amount of biomedical data. Many informaticists also have the expertise to engage with stakeholders, develop new methods and applications, and influence policy. However, research and policy that explicitly seeks to address the systemic drivers of health would more effectively support health. Intersectionality is a theoretical framework that can facilitate such research. It holds that individual human experiences reflect larger socio-structural level systems of privilege and oppression, and cannot be truly understood if these systems are examined in isolation. Intersectionality explicitly accounts for the interrelated nature of systems of privilege and oppression, providing a lens through which to examine and challenge inequities. In this paper, we propose intersectionality as an intervention into how we conduct BMI research. We begin by discussing intersectionality's history and core principles as they apply to BMI. We then elaborate on the potential for intersectionality to stimulate BMI research. Specifically, we posit that our efforts in BMI to improve health should address intersectionality's five key considerations: (1) systems of privilege and oppression that shape health; (2) the interrelated nature of upstream health drivers; (3) the nuances of health outcomes within groups; (4) the problematic and power-laden nature of categories that we assign to people in research and in society; and (5) research to inform and support social change.
Asunto(s)
Informática Médica , Humanos , Informática Médica/métodos , Investigación BiomédicaRESUMEN
In this work, we survey a breadth of literature that has revealed the limitations of predominant practices for dataset collection and use in the field of machine learning. We cover studies that critically review the design and development of datasets with a focus on negative societal impacts and poor outcomes for system performance. We also cover approaches to filtering and augmenting data and modeling techniques aimed at mitigating the impact of bias in datasets. Finally, we discuss works that have studied data practices, cultures, and disciplinary norms and discuss implications for the legal, ethical, and functional challenges the field continues to face. Based on these findings, we advocate for the use of both qualitative and quantitative approaches to more carefully document and analyze datasets during the creation and usage phases.
RESUMEN
Racial discrimination has been a central driver of residential segregation for many decades, in the Seattle area as well as in the United States as a whole. In addition to redlining and restrictive housing covenants, housing advertisements included explicit racial language until 1968. Since then, housing patterns have remained racialized, despite overt forms of racial language and discrimination becoming less prevalent. In this paper, we use Structural Topic Models (STM) and qualitative analysis to investigate how contemporary rental listings from the Seattle-Tacoma Craigslist page differ in their description based on neighborhood racial composition. Results show that listings from White neighborhoods emphasize trust and connections to neighborhood history and culture, while listings from non-White neighborhoods offer more incentives and focus on transportation and development features, sundering these units from their surroundings. Without explicitly mentioning race, these listings display racialized neighborhood discourse that might impact neighborhood decision-making in ways that contribute to the perpetuation of housing segregation.