Autonomous human-machine teams: Data dependency and Artificial Intelligence (AI)

The reliance on concepts derived from observations in laboratories combined with the assumption that concepts and behavior are one-to-one (monism) have impeded the development of social science, machine learning (ML) and belief logics by restricting them to operate in controlled and stable contexts. Even in open contexts, using ideas developed in laboratories, despite using well-trained observers to make predictions about the likelihood of outcomes in open contexts, using the same concepts and assumptions, in 2016, Tetlock and Gardner's "superforecasters" failed to predict Brexit (Britain’s exit from the European Union) or Trump’s presidency. Similarly, in 2022, using traditional techniques, the CIA's expert observers and the Russian military planners both mis-judged the Ukranian people by claiming that Russia's army would easily defeat Ukraine. Providing support for overturning these concepts and assumptions, however, in 2021, the National Academy of Sciences made two claims with which we fully support. First, the Academy had warned that controlled contexts are insufficient to produce operational autonomous systems. We agree; by studying real-world contexts, we have concluded that the data derived from states of social interdependence not only create data dependency, but also that interdependence is the missing ingredient necessary for autonomy. Second, a team’s data dependency increases by reducing its internal degrees of freedom, thereby reducing its structural entropy production; this situation of heightened interdependence explains the Academy's second claim that the “performance of a team is not decomposable to, or an aggregation of, individual performances,” consequently providing corrobration for our new discipline of data dependency. We extend the Academy’s claims by asserting that the reduction of entropy production in a team’s structure (SEP), indicating the fittedness among team members, represents a tradeoff with a team’s performance, reflected by a team’s achievement of maximum entropy production (MEP).