Like many previous technologies, artificial intelligence and machine learning have the potential to participate in progress toward increased democratization and social equity. However, there are persistent, widespread notions that the introduction of advanced technologies to society can constitute such progress in and of itself. Or, conversely, that technologies of automation have deleterious effects on society by their very existence. Our researchers believe these technologies can participate in both positive and negative social changes. Additionally, they magnify and standardize existing features of society, whether or not we as a society are attentive toward those features.
Therefore, without a grounding in intentional political policies, mythologies regarding the autonomy of technical systems contribute to an exacerbation and acceleration of pre-existing processes of socioeconomic stratification and political polarization. In the United States, regulations on artificial intelligence and machine learning that might steer their development along a democratizing path are inhibited by several sociopolitical phenomena and discursive threads.
Our research explores the following mythologies and prevalent discourses surrounding automation:
- Epistemological bordering & hierarchy: the sense that technology is too complete to complex to understand without intense technical training (i.e. computer science or coding).
- Technical naturalization: the mythology that the present form of technology was inevitable—that the way it exists today is a result of the only possible way it could have developed.
- Conflation of automation and autonomy: This is the mistaken idea that technologies simply “do things” or “make decisions” on their own without continuous investment, labor, monitoring and maintenance from human beings, which is largely a blurring of science fiction and political reality.
- Myth of “cloud computing”: Much of the politically damaging “magical thinking” that occurs with regard to technical systems is grounded in the so-called “immateriality” of software and data.
- Supposed value-neutrality of technology: how, where, why, by whom and about whom is data collected? Who owns data collected “about” them?
Researchers
Ayinwi Muma
Ayinwi Muma is currently a Ph.D. candidate at Stanford University. Her research examines the impact of regulatory policy changes on entrepreneurial activity across various socially-constructed identity groups, e.g., race and gender.
Her dissertation explores three central questions: First, in “normal times” how do young, relatively unknown entrepreneurial firms introduce novel technologies into established organizational contexts? Second, what happened to investments in early-stage entrepreneurship during the COVID-19 pandemic? Third, how did the June 2022 overturn of Roe v. Wade affect subsequent entrepreneurial activity across states in the U.S., and what are the implications for entrepreneurship and labor force participation among women?
Ayinwi holds a B.A. in English & Comparative Literature from Columbia University, an M.B.A. in Strategic Management from Georgia Institute of Technology, and (will soon hold!) a Ph.D. in Management Science & Engineering from Stanford University in Summer 2023.
Elizabeth Degefe
Quran Karriem
Quran is an experimental musician, media artist and theorist working primarily with electronic and algorithmic media. His research is concerned with human improvisation and automated decision, particularly insofar as they reproduce sovereign power and racial hierarchy through semi-autonomous knowledge systems. His work examines the power relations and ideologies that inhere in the design of digital systems, processes and interfaces, and is motivated by a concern with the operative and recursive nature of computational, racialized capital in postmodern sociotechnical assemblages.
A multiple award-winning software designer and former product executive, Quran has led development teams for a number of media and technology companies and applies a decade of direct experience with systems design, data management and organizational structure in the context of ‘start-up culture’ to social critique. His product initiatives have been recognized by such global research and trade bodies as Gartner Research, the Groupe Spéciale Mobile Association (GSMA), the Software and Information Industry Association (SIIA) and Frost & Sullivan.
