Automation reliability, human-machine system performance, and operator compliance: A study with airport security screeners supported by automated explosives detection systems for cabin baggage screening

Judgments of Difficulty (JODs) While Observing an Automated System Support the Media Equation and Unique Agent Hypotheses

OBJECTIVE

We investigated how people used cues to make Judgments of Difficulty (JODs) while observing automation perform a task and when performing this task themselves.

BACKGROUND

Task difficulty is a factor affecting trust in automation; however, no research has explored how individuals make JODs when watching automation or whether these judgments are similar to or different from those made while watching humans. Lastly, it is unclear how cue use when observing automation differs as a function of experience.

METHOD

The study involved a visual search task. Some participants performed the task first, then watched automation complete it. Others watched and then performed, and a third group alternated between performing and watching. After each trial, participants made a JOD by indicating if the task was easier or harder than before. Task difficulty randomly changed every five trials.

RESULTS

A Bayesian regression suggested that cue use is similar to and different from cue use while observing humans. For central cues, support for the UAH was bounded by experience: those who performed the task first underweighted central cues when making JODs, relative to their counterparts in a previous study involving humans. For peripheral cues, support for the MEH was unequivocal and participants weighted cues similarly across observation sources.

CONCLUSION

People weighted cues similar to and different from when they watched automation perform a task relative to when they watched humans, supporting the Media Equation and Unique Agent Hypotheses.

APPLICATION

This study adds to a growing understanding of judgments in human-human and human-automation interactions.

Trust with increasing and decreasing reliability

OBJECTIVE

The primary purpose was to determine how trust changes over time when automation reliability increases or decreases. A secondary purpose was to determine how task-specific self-confidence is associated with trust and reliability level.

BACKGROUND

Both overtrust and undertrust can be detrimental to system performance; therefore, the temporal dynamics of trust with changing reliability level need to be explored.

METHOD

Two experiments used a dominant-color identification task, where automation provided a recommendation to users, with the reliability of the recommendation changing over 300 trials. In Experiment 1, two groups of participants interacted with the system: one group started with a 50% reliable system which increased to 100%, while the other used a system that decreased from 100% to 50%. Experiment 2 included a group where automation reliability increased from 70% to 100%.

RESULTS

Trust was initially high in the decreasing group and then declined as reliability level decreased; however, trust also declined in the 50% increasing reliability group. Furthermore, when user self-confidence increased, automation reliability had a greater influence on trust. In Experiment 2, the 70% increasing reliability group showed increased trust in the system.

CONCLUSION

Trust does not always track the reliability of automated systems; in particular, it is difficult for trust to recover once the user has interacted with a low reliability system.

APPLICATIONS

This study provides initial evidence into the dynamics of trust for automation that gets better over time suggesting that users should only start interacting with automation when it is sufficiently reliable.

Understanding the Impact of Time Pressure and Automation Support in a Visual Search Task

OBJECTIVE

To understand the impact of time pressure and automated decision support systems (DSS) in a simulated medical visual search task.

BACKGROUND

Time pressure usually impairs manual performance in visual search tasks, but DSS support might neutralize this negative effect. Moreover, understanding the impact of time pressure and DSS support seems relevant for many real-world applications of visual search.

METHOD

We used a visual search paradigm where participants had to search for target letters in a simulated medical image. Participants performed the task either manually or with support of a highly reliable DSS. Time pressure was varied within-subjects by either a trialwise time-pressure manipulation (Experiment 1) or a blockwise manipulation (Experiment 2). Performance was assessed based on signal detection measures. To further analyze visual search behavior, a mouse-over approach was used.

RESULTS

In both experiments, results showed impaired sensitivity under high compared to low time pressure in the manual condition, but no negative effect of time pressure when working with a highly reliable DSS. Moreover, participants searched less under time pressure and when receiving DSS support, indicating participants followed the automation without thoroughly checking recommendations. However, the human-DSS team's sensitivity was always worse than that of the DSS alone, independent of the strength of time pressure.

CONCLUSION

Negative effects of time pressure can be ameliorated when receiving support by a DSS, but joint overall performance remains below DSS-alone performance.

APPLICATION

Highly reliable DSS seem capable of ameliorating the negative impact of time pressure in complex detection tasks.

Fluorescent Carbon Dots Derived From Soy Sauce for Picric Acid Detection and Cell Imaging

Picric acid (PA) is a powerful nitro-aromatic explosive that harms the environment and human health. Developing non-toxic and low-cost sensors for the rapid detection of PA is essential. An environment-friendly fluorescent probe for PA detection is designed based on carbon dots (CDs) directly separated from edible soy sauce by silica gel column chromatography. Neither organic reagents nor heating process was needed to prepare CDs. The obtained CDs exhibit bright blue fluorescence, good water solubility, and photostability. The fluorescent probe for PA was developed according to the CD's fluorescence can be significantly quenched via the inner filter effect between CDs and PA. The linear range was 0.2-24 µM with a limit of detection of 70 nM. This proposed method was successfully employed to detect PA in the real water samples with satisfactory recoveries between 98.0-104.0%. Moreover, the CDs were suitable for fluorescence imaging of HeLa cells owing to their low toxicity and good biocompatibility.

The Effect of Informing Participants of the Response Bias of an Automated Target Recognition System on Trust and Reliance Behavior

OBJECTIVE

To determine how changing and informing a user of the false alarm (FA) rate of an automated target recognition (ATR) system affects the user's trust in and reliance on the system and their performance during an underwater mine detection task.

BACKGROUND

ATR systems are designed to operate using a high sensitivity and a liberal decision criterion to reduce the risk of the ATR system missing a target. A high number of FAs in general may lead to a decrease in operator trust and reliance.

METHODS

Participants viewed sonar images and were asked to identify mines in the images. They performed the task without ATR and with ATR at a lower and higher FA rate. The participants were split into two groups-one informed and one uninformed of the changed FA rate. Trust and/or confidence in detecting mines was measured after each block.

RESULTS

When not informed of the FA rate, the FA rate had a significant effect on the participants' response bias. Participants had greater trust in the system and a more consistent response bias when informed of the FA rate. Sensitivity and confidence were not influenced by disclosure of the FA rate but were significantly worse for the high FA rate condition compared with performance without the ATR.

CONCLUSION AND APPLICATION

Informing a user of the FA rate of automation may positively influence the level of trust in and reliance on the aid.

Human behavioral response to fluctuating automation reliability

Human perception of automation reliability and automation acceptance behaviours are key to effective human-automation teaming. This study examined factors that impact perceptions of automation reliability over time and the acceptance of automated advice. Participants completed a maritime vessel classification task in which they classified vessels (contacts) with the assistance of automation. In Experiment 1 automation reliability successively switched from high to low (or vice versa). In Experiment 2 automation reliability decreased by varying magnitudes before returning to high. Participants did not initially calibrate to true reliability and experiencing low automation reliability reduced future reliability estimates when experiencing subsequent high reliability. Automation acceptance was predicted by positive differences between participant perception of automation reliability and confidence in their own manual classification reliability. Experiencing low automation reliability caused perceptions of reliability and automation acceptance rates to diverge. These findings have important implications for training and adaptive human-automation teaming in complex work environments.

Measuring the Efficiency of Automation-Aided Performance in a Simulated Baggage Screening Task

OBJECTIVE

The present study replicated and extended prior findings of suboptimal automation use in a signal detection task, benchmarking automation-aided performance to the predictions of several statistical models of collaborative decision making.

BACKGROUND

Though automated decision aids can assist human operators to perform complex tasks, operators often use the aids suboptimally, achieving performance lower than statistically ideal.

METHOD

Participants performed a simulated security screening task requiring them to judge whether a target (a knife) was present or absent in a series of colored X-ray images of passenger baggage. They completed the task both with and without assistance from a 93%-reliable automated decision aid that provided a binary text diagnosis. A series of three experiments varied task characteristics including the timing of the aid's judgment relative to the raw stimuli, target certainty, and target prevalence.

RESULTS AND CONCLUSION

Automation-aided performance fell closest to the predictions of the most suboptimal model under consideration, one which assumes the participant defers to the aid's diagnosis with a probability of 50%. Performance was similar across experiments.

APPLICATION

Results suggest that human operators' performance when undertaking a naturalistic search task falls far short of optimal and far lower than prior findings using an abstract signal detection task.

When experience does not promote expertise: security professionals fail to detect low prevalence fake IDs

Professional screeners frequently verify photograph IDs in such industries as professional security, bar tending, and sales of age-restricted materials. Moreover, security screening is a vital tool for law enforcement in the search for missing or wanted persons. Nevertheless, previous research demonstrates that novice participants fail to spot fake IDs when they are rare (i.e., the low prevalence effect; LPE). To address whether this phenomenon also occurs with professional screeners, we conducted three experiments. Experiment 1 compared security professional and non-professionals. Experiment 2 compared bar-security professionals, access-security professionals, and non-professionals. Finally, Experiment 3 added a newly created Professional Identity Training Questionnaire to determine whether and how aspects of professionals’ employment predict ID-matching accuracy. Across all three experiments, all participants were susceptible to the LPE regardless of professional status. Neither length/type of professional experience nor length/type of training experience affected ID verification performance. We discuss task performance and survey responses with aims to acknowledge and address this potential problem in real-world screening scenarios.

Visual search behavior and performance in luggage screening: effects of time pressure, automation aid, and target expectancy

Visual inspection of luggage using X-ray technology at airports is a time-sensitive task that is often supported by automated systems to increase performance and reduce workload. The present study evaluated how time pressure and automation support influence visual search behavior and performance in a simulated luggage screening task. Moreover, we also investigated how target expectancy (i.e., targets appearing in a target-often location or not) influenced performance and visual search behavior. We used a paradigm where participants used the mouse to uncover a portion of the screen which allowed us to track how much of the stimulus participants uncovered prior to their decision. Participants were randomly assigned to either a high (5-s time per trial) or a low (10-s time per trial) time-pressure condition. In half of the trials, participants were supported by an automated diagnostic aid (85% reliability) in deciding whether a threat item was present. Moreover, within each half, in target-present trials, targets appeared in a predictable location (i.e., 70% of targets appeared in the same quadrant of the image) to investigate effects of target expectancy. The results revealed better detection performance with low time pressure and faster response times with high time pressure. There was an overall negative effect of automation support because the automation was only moderately reliable. Participants also uncovered a smaller amount of the stimulus under high time pressure in target-absent trials. Target expectancy of target location improved accuracy, speed, and the amount of uncovered space needed for the search.Significance Statement Luggage screening is a safety-critical real-world visual search task which often has to be done under time pressure. The present research found that time pressure compromises performance and increases the risk to miss critical items even with automation support. Moreover, even highly reliable automated support may not improve performance if it does not exceed the manual capabilities of the human screener. Lastly, the present research also showed that heuristic search strategies (e.g., areas where targets appear more often) seem to guide attention also in luggage screening.

Effect of automation transparency in the management of multiple unmanned vehicles

Automated decision aids can undoubtedly benefit system performance, but have the potential to provide incorrect advice, creating the possibility for automation disuse or misuse. This paper examined the extent to which increased automation transparency could improve the accuracy of automation use in a simulation of unmanned vehicle control. Participants were required to assign the best unmanned vehicle to complete missions. An automated recommender system provided advice but was not always reliable. Three levels of automation transparency were manipulated between-participants. Providing transparency regarding the reasoning underlying automated recommendations improved the accuracy of automation use, with no cost to decision time or subjective workload. Higher transparency in the form of projected decision outcome visualizations led to faster decisions, but less accurate automation use and an automation bias. Implications for the design of transparent interfaces to improve human-autonomy teaming outcomes in time-pressured environments are discussed.