Contingency, Control, Creativity
As part of our focus on Digital Life, and situated within our expertise in both the Learning Sciences & Science and Technology Studies, MIXI is organizing a Spring conference (April 2023) dedicated to opening up a forum for collaborative and applied philosophical inquiry about our algorithmic conditions.
The conference explores three key facets of learning under algorithmic conditions (a) the play of contingency at the heart of speculative-mathematical models of chance and necessity (b) the practices of control in digital governance and bodily subjection, and (c) the force of creativity in resisting and artful repurposing of new media.
April 18-20, 2023. Manhattan, NY.
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?