AI in society: A theory - Quandt, Ryan Phillip (Frontiers in Physics, 2022)
Human-machine teams or systems are integral parts of society. Many grant that such developments alter society, yet healthily debate the effects, how to measure them, and their mechanisms. This debate spans questions about the event(s) of human-machine interaction. How is it distinct from human interaction? What does it require and entail? How does it ramify across society? In this article, I propose a central concept for understanding human-machine interaction: convergent cause. This concept marks a requirement of interaction on which other requirements hinge. To defend my proposal, I repurpose Donald Davidson's triangulation argument as a model for studying human-machine teams and systems. AI does not fit squarely in his model as agent or object. Rather than detract from the model, this blindspot promises insights for how AI contributes to the conception of objects within society. From causal convergence, triangulation explains how human-machine teams and systems effect an ontological shift.