SODAS Lecture: The Philosophy of the Predicted Human
SODAS lecture "The Philosophy of the Predicted Human: The Implications of Machine Prediction for the Theory of Action, and Vice-Versa"
We are delighted to host Prof. John Levi Martin for the first lecture this Fall, please join us for this event.
Abstract
Before the parallelization revolution that spelled the death of “AI 1.0,” the Newell-Simon attempt to mimic human decision making via serial algorithms, it was inevitable that theories used to guide the development of computer software would leak back into our theories of human being. Given that the dominant family of theories of human action was entrenched in the first-approximation model of economic decision making, bound up with the core ideological self-understanding of capitalist societies (“choice”), this was an easy slippage to make. However, the rise of deep-learning algorithms is forcing researchers to grapple with the limits of what Haugeland called “Good Old Fashioned AI” (GOFAI), and to develop new understandings of how future machines will operate, ones that treat the machines less like formal flow-charts and more like organic brains. Sadly, our theory of human cognition has not budged, and we still attempt to apply to ourselves theories that we recognize are not even appropriate for current computer technology. I consider how the puzzles generated by predictive engines have the potential to radically change our understanding of human action, and the criteria by which we judge a potential explanation. Indeed, I will propose that the predictive success of future AI will force us to recognize the limited significance of prediction (as it is currently understood) in the assessment of theories.
About
John Levi Martin received a Ph.D. in Sociology from the University of California at Berkeley, where he was recently a professor, after being a professor at the University of Wisconsin at Madison and an assistant professor at Rutgers—The State University of New Jersey at New Brunswick. He is now a professor at the University of Chicago at Chicago. He has researched the formal properties of belief systems and social structures, and has written books on how to think through theory, methods, and statistics.
This fall, the theme of the SODAS lecture series is "Philosophy of the Predicted Human".
The Predicted Human
Being human in 2021 implies being the target of a vast number of predictive infrastructures. In healthcare algorithms predict not only potential pharmacological cures to disease but also their possible future incidence of those diseases. In governance, citizens are exposed to algorithms that predict - not only their day-to-day behaviors to craft better policy - but also to algorithms that attempt to predict, shape and manipulate their political attitudes and behaviors. In education, children’s emotional and intellectual development is increasingly the product of at-home and at-school interventions shaped around personalized algorithms. And humans worldwide are increasingly subject to advertising and marketing algorithms whose goal is to target them with specific products and ideas they will find palatable. Algorithms are everywhere – as are their intended as well as unintended consequences.
Predicting and manipulating the future behavior of human beings is nothing new. Most of the quantitative social sciences focus on this topic in a general sense. There are entire subfields of statistics dedicated to understanding what can be predicted and what cannot. Yet the current situation is different. Computers’ ability to analyze text and images has been revolutionized by the availability of vast datasets and new machine learning techniques. We are currently experiencing a similar shift in terms of how algorithms can predict (and manipulate) human behavior. Human beings can be algorithmically shaped, we can be hacked.
The ambition with this semester’s SODAS Lectures is to present and discuss different perspectives on human prediction. Inviting a list of distinguished scholars and speakers whose expertise ranges from traditional social sciences, over machine learning and data science to philosophy and STS, we hope to delve into some of the principles and dynamics which govern our ability to predict and control both individual and collective human behaviors.