Algorithmic Contingencies

 

 

 

Leads: Liz de Freitas & Sam Sellar

 

We use the term algorithmic contingency to underscore the indeterminacy of our current cultural investment in computational infrastructure. We emphasize both the gamble of such an adventure, and the fundamental role of chance (and hazard) in data science applications that present themselves as certain or absolute. This massive cultural experiment in digital AI plays with young people’s futures and fortunes, reshaping the very nature of learning, labour and life.

 

As part of our focus on Digital Life, and situated within our expertise in Science and Technology Studies, the Manifold Lab is organizing a series of events dedicated to opening up a forum for collaborative and applied philosophical inquiry. We are hosting events in June 2020 and throughout the year, that bring together people who share interests in: (a) the nature of computational cultures; (b) the specifics of machine learning processes; (c) the digital rendering of reason and learning; and (d) the impact of data science on education and social policy. We aim to share knowledge and ideas, to build a powerful network of people working on these issues, and to identify a set of actions that we can begin to pursue collectively.

Guiding Research questions: What new images of reason are produced with machine learning? To what extent are new epistemic paradigms influencing new ways of theorizing decision, judgment, understanding and human learning? What images and theories of learning inform current AI efforts? How do these approaches relate to other theories of learning? How do present concepts of algorithm differ from past concepts? Is there a general ecological paradigm that captures the current technicity of the digital planet? Can art offer ways to hack ‘smart’ environments and promote critical participation in computational publics? Do particular software applications have the potential to transform pedagogy in substantial ways? To what extent do new digital technologies simply fold into existing institutional structures and accelerate or amplify established pedagogies? How will automated production affect education and employment? How is social policy and practice changing in response to predictions about futures shaped by AI and digital labor? How do algorithmic logics shape governance practices (e.g. prediction, anticipation?) How are governments and private entities using machine learning and data science for education policy? How does the use of machine learning and data science affect public scrutiny and debate about decision making in education? Do current ethical and legal guidelines for AI intersect with the principles that underpin public education? Can machine learners ‘compensate for society’ by correcting biases in training data? How can ‘black boxed’ machine learning be adopted in education contexts where situated and culturally responsive practices are necessary?