Societal inequalities have become a plague that continues to affect marginalized communities in the era of advanced technological interventions. While technology was meant to be the “great equalizer”, most often it in fact perpetuates societal stereotypes, inequalities, and injustices. This has led to an increase in interest around understanding and supporting marginalized communities, especially for researchers and practitioners who personally identify with the societal and technological inequalities that have become commonplace. If we are to truly shift the tides in technological inequality and oppression, we need to consider a shift in how we design and develop technologies that affect marginalized populations. Following the recent (and familiar) racially charged murders of innocent Black men and women, there have been concentrated efforts aimed at supporting the Black community. However, many of these efforts attempt to make blanket solutions that do not take into consideration the diversity even within a marginalized population, such as the Black community. Furthermore, there is often a focus on the challenges they face rather than the assets and triumphs that help define their experience. In this presentation, I will talk about work that myself, and others, are doing on designing and developing technology that truly understands, considers, and supports the Black-lived experience.

I am Dr. Brittany Johnson-Matthews, an Assistant Professor in the Department of Computer Science at George Mason University. You may also know me as my pen name, Brittany Johnson. I direct the INSPIRED (INterdisciplinary Software Practice Improvement REsearch and Development) Lab. I received my Ph.D. in Computer Science from North Carolina State University (2017), after getting my B.A. in Computer Science from the College of Charleston (2011). I explore sociotechnical problems pertaining to developer productivity and software development/use, such as tool support, work environments, ethics, and software for social good. My research is interdisciplinary, cross-cutting with research in software engineering, human-computer interaction, and machine learning.