This paper addresses theoretical, empirical, and analytical studies pertaining to human use, misuse, disuse, and abuse of automation technology. Use refers to the voluntary activation or disengagement of automation by human operators. Trust, mental workload, and risk can influence automation use, but interactions between factors and large individual differences make prediction of automation use difficult. Misuse refers to overreliance on automation, which can result in failures of monitoring or decision biases. Factors affecting the monitoring of automation include workload, automation reliability and consistency, and the saliency of automation state indicators. Disuse, or the neglect or underutilization of automation, is commonly caused by alarms that activate falsely. This often occurs because the base rate of the condition to be detected is not considered in setting the trade-off between false alarms and omissions. Automation abuse, or the automation of functions by designers and implementation by managers without due regard for the consequences for human performance, tends to define the operator's roles as by-products of the automation. Automation abuse can also promote misuse and disuse of automation by human operators. Understanding the factors associated with each of these aspects of human use of automation can lead to improved system design, effective training methods, and judicious policies and procedures involving automation use.

The increasing role of automation in human-machine systems requires modelling approaches which are flexible enough to systematically express a large range of automation levels and assist the exploration of a large range of automation issues. A General Model of Mixed-Initiative Human-Machine Systems is described, along with a corresponding automation taxonomy, which: provides a framework for representing human-machine systems over a wide range of complexity; forms the basis of a dynamic, pseudomathematical simulation of complex interrelationships between situational and cognitive factors operating in dynamic function allocation decisions; and can guide methodical investigations into the implications of decisions regarding system automation levels.

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