Aviation Automation: General Perspectives and Specific Guidance for the Design of Modes and Alerts

Theoretical, Measured, and Subjective Responsibility in Aided Decision Making

When humans interact with intelligent systems, their causal responsibility for outcomes becomes equivocal. We analyze the descriptive abilities of a newly developed responsibility quantification model (ResQu) to predict actual human responsibility and perceptions of responsibility in the interaction with intelligent systems. In two laboratory experiments, participants performed a classification task. They were aided by classification systems with different capabilities. We compared the predicted theoretical responsibility values to the actual measured responsibility participants took on and to their subjective rankings of responsibility. The model predictions were strongly correlated with both measured and subjective responsibility. Participants’ behavior with each system was influenced by the system and human capabilities, but also by the subjective perceptions of these capabilities and the perception of the participant's own contribution. A bias existed only when participants with poor classification capabilities relied less than optimally on a system that had superior classification capabilities and assumed higher-than-optimal responsibility. The study implies that when humans interact with advanced intelligent systems, with capabilities that greatly exceed their own, their comparative causal responsibility will be small, even if formally the human is assigned major roles. Simply putting a human into the loop does not ensure that the human will meaningfully contribute to the outcomes. The results demonstrate the descriptive value of the ResQu model to predict behavior and perceptions of responsibility by considering the characteristics of the human, the intelligent system, the environment, and some systematic behavioral biases. The ResQu model is a new quantitative method that can be used in system design and can guide policy and legal decisions regarding human responsibility in events involving intelligent systems.

Where We Come from and Where We Are Going: A Systematic Review of Human Factors Research in Driving Automation

During the last decade, research has brought forth a large amount of studies that investigated driving automation from a human factor perspective. Due to the multitude of possibilities for the study design with regard to the investigated constructs, data collection methods, and evaluated parameters, at present, the pool of findings is heterogeneous and nontransparent. This literature review applied a structured approach, where five reviewers investigated n = 161 scientific papers of relevant journals and conferences focusing on driving automation between 2010 and 2018. The aim was to present an overview of the status quo of existing methodological approaches and investigated constructs to help scientists in conducting research with established methods and advanced study setups. Results show that most studies focused on safety aspects, followed by trust and acceptance, which were mainly collected through self-report measures. Driving/Take-Over performance also marked a significant portion of the published papers; however, a wide range of different parameters were investigated by researchers. Based on our insights, we propose a set of recommendations for future studies. Amongst others, this includes validation of existing results on real roads, studying long-term effects on trust and acceptance (and of course other constructs), or triangulation of self-reported and behavioral data. We furthermore emphasize the need to establish a standardized set of parameters for recurring use cases to increase comparability. To assure a holistic contemplation of automated driving, we moreover encourage researchers to investigate other constructs that go beyond safety.

Comparing Trust in Auto-GCAS Between Experienced and Novice Air Force Pilots

We examined F-16 pilots’ trust of the Automatic Ground Collision Avoidance System (Auto-GCAS), an automated system fielded on the F-16 to reduce the occurrence of controlled flight into terrain. We looked at the impact of experience (i.e., number of flight hours) as a predictor of trust perceptions and complacency potential among pilots. We expected that novice pilots would report higher trust and greater potential for complacency in relation to Auto-GCAS, which was shown to be partly true. Although novice pilots, compared with experienced pilots, reported equivalent trust perceptions, they also reported greater complacency potential.

Implementing Lumberjacks and Black Swans Into Model-Based Tools to Support Human-Automation Interaction

OBJECTIVE

The objectives were to (a) implement theoretical perspectives regarding human-automation interaction (HAI) into model-based tools to assist designers in developing systems that support effective performance and (b) conduct validations to assess the ability of the models to predict operator performance.

BACKGROUND

Two key concepts in HAI, the lumberjack analogy and black swan events, have been studied extensively. The lumberjack analogy describes the effects of imperfect automation on operator performance. In routine operations, an increased degree of automation supports performance, but in failure conditions, increased automation results in more significantly impaired performance. Black swans are the rare and unexpected failures of imperfect automation.

METHOD

The lumberjack analogy and black swan concepts have been implemented into three model-based tools that predict operator performance in different systems. These tools include a flight management system, a remotely controlled robotic arm, and an environmental process control system.

RESULTS

Each modeling effort included a corresponding validation. In one validation, the software tool was used to compare three flight management system designs, which were ranked in the same order as predicted by subject matter experts. The second validation compared model-predicted operator complacency with empirical performance in the same conditions. The third validation compared model-predicted and empirically determined time to detect and repair faults in four automation conditions.

CONCLUSION

The three model-based tools offer useful ways to predict operator performance in complex systems.

APPLICATION

The three tools offer ways to predict the effects of different automation designs on operator performance.

Judgment and Decision Making by Individuals and Teams: Issues, Models, and Applications

Consistent with technological advances, the role of the operator in many human factors domains has evolved from one characterized primarily by sensory and motor skills to one characterized primarily by cognitive skills and decision making. Decision making is a primary component in problem solving, human-automation interaction, response to alarms and warnings, and error mitigation. In this chapter we discuss decision making in terms of both front-end judgment processes (e.g., attending to and evaluating the significance of cues and information, formulating a diagnosis, or assessing the situation) and back-end decision processes (e.g., retrieving a course of action, weighing one's options, or mentally simulating a possible response). Two important metatheories—correspondence (empirical accuracy) and coherence (rationality and consistency)—provide ways to assess the goodness of each phase (e.g., Hammond, 1996, 2000; Mosier, 2009). We present several models of decision making, including Brunswik's lens model, naturalistic decision making, and decision ladders, and discuss them in terms of their point of focus and their primary strategies and goals. Next, we turn the discussion to layers in the decision context: individual variables, team decision making, technology, and organizational influences. Last, we focus on applications and lessons learned: investigating, enhancing, designing, and training for decision making. Drawing heavily on sources such as the Human Factors journal, we present recent human factors research exploring these issues, models, and applications.