The Responsibility Quantification (ResQu) Model of Human Interaction with Automation
This addresses the issue of human accountability in automation for policymakers and system designers, but it is an incremental step with acknowledged limitations.
The paper tackles the problem of ambiguous human responsibility in intelligent automated systems, particularly autonomous weapon systems, by developing a responsibility quantification (ResQu) model using Information Theory, and finds that human comparative responsibility is often low even with major functions allocated to humans, challenging policies like 'keeping humans in the loop'.
Intelligent systems and advanced automation are involved in information collection and evaluation, in decision-making and in the implementation of chosen actions. In such systems, human responsibility becomes equivocal. Understanding human casual responsibility is particularly important when intelligent autonomous systems can harm people, as with autonomous vehicles or, most notably, with autonomous weapon systems (AWS). Using Information Theory, we develop a responsibility quantification (ResQu) model of human involvement in intelligent automated systems and demonstrate its applications on decisions regarding AWS. The analysis reveals that human comparative responsibility to outcomes is often low, even when major functions are allocated to the human. Thus, broadly stated policies of keeping humans in the loop and having meaningful human control are misleading and cannot truly direct decisions on how to involve humans in intelligent systems and advanced automation. The current model is an initial step in the complex goal to create a comprehensive responsibility model, that will enable quantification of human causal responsibility. It assumes stationarity, full knowledge regarding the characteristic of the human and automation and ignores temporal aspects. Despite these limitations, it can aid in the analysis of systems designs alternatives and policy decisions regarding human responsibility in intelligent systems and advanced automation.