Artificial Intelligence

Artificial Intelligence

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?