Transparent Automated Advice to Mitigate the Impact of Variation in Automation Reliability

OBJECTIVE

To examine the extent to which increased automation transparency can mitigate the potential negative effects of low and high automation reliability on disuse and misuse of automated advice, and perceived trust in automation.

BACKGROUND

Automated decision aids that vary in the reliability of their advice are increasingly used in workplaces. Low-reliability automation can increase disuse of automated advice, while high-reliability automation can increase misuse. These effects could be reduced if the rationale underlying automated advice is made more transparent.

METHODS

Participants selected the optimal UV to complete missions. The Recommender (automated decision aid) assisted participants by providing advice; however, it was not always reliable. Participants determined whether the Recommender provided accurate information and whether to accept or reject advice. The level of automation transparency (medium, high) and reliability (low: 65%, high: 90%) were manipulated between-subjects.

RESULTS

With high- compared to low-reliability automation, participants made more accurate (correctly accepted advice and identified whether information was accurate/inaccurate) and faster decisions, and reported increased trust in automation. Increased transparency led to more accurate and faster decisions, lower subjective workload, and higher usability ratings. It also eliminated the increased automation disuse associated with low-reliability automation. However, transparency did not mitigate the misuse associated with high-reliability automation.

CONCLUSION

Transparency protected against low-reliability automation disuse, but not against the increased misuse potentially associated with the reduced monitoring and verification of high-reliability automation.

APPLICATION

These outcomes can inform the design of transparent automation to improve human-automation teaming under conditions of varied automation reliability.

Looking for Age Differences in Self-Driving Vehicles: Examining the Effects of Automation Reliability, Driving Risk, and Physical Impairment on Trust

PURPOSE

Self-driving cars are an extremely high level of autonomous technology and represent a promising technology that may help older adults safely maintain independence. However, human behavior with automation is complex and not straightforward (Parasuraman and Riley, 1997; Parasuraman, 2000; Rovira et al., 2007; Parasuraman and Wickens, 2008; Parasuraman and Manzey, 2010; Parasuraman et al., 2012). In addition, because no fully self-driving vehicles are yet available to the public, most research has been limited to subjective survey-based assessments that depend on the respondents' limited knowledge based on second-hand reports and do not reflect the complex situational and dispositional factors known to affect trust and technology adoption.

METHODS

To address these issues, the current study examined the specific factors that affect younger and older adults' trust in self-driving vehicles.

RESULTS

The results showed that trust in self-driving vehicles depended on multiple interacting variables, such as the age of the respondent, risk during travel, impairment level of the hypothesized driver, and whether the self-driving car was reliable.

CONCLUSION

The primary contribution of this work is that, contrary to existing opinion surveys which suggest broad distrust in self-driving cars, the ratings of trust in self-driving cars varied with situational characteristics (reliability, driver impairment, risk level). Specifically, individuals reported less trust in the self-driving car when there was a failure with the car technology; and more trust in the technology in a low risk driving situation with an unimpaired driver when the automation was unreliable.

Target Detection and Identification Performance Using an Automatic Target Detection System

OBJECTIVE

We investigated the effects of automatic target detection (ATD) on the detection and identification performance of soldiers.

BACKGROUND

Prior studies have shown that highlighting targets can aid their detection. We provided soldiers with ATD that was more likely to detect one target identity than another, potentially acting as an implicit identification aid.

METHOD

Twenty-eight soldiers detected and identified simulated human targets in an immersive virtual environment with and without ATD. Task difficulty was manipulated by varying scene illumination (day, night). The ATD identification bias was also manipulated (hostile bias, no bias, and friendly bias). We used signal detection measures to treat the identification results.

RESULTS

ATD presence improved detection performance, especially under high task difficulty (night illumination). Identification sensitivity was greater for cued than uncued targets. The identification decision criterion for cued targets varied with the ATD identification bias but showed a "sluggish beta" effect.

CONCLUSION

ATD helps soldiers detect and identify targets. The effects of biased ATD on identification should be considered with respect to the operational context.

APPLICATION

Less-than-perfectly-reliable ATD is a useful detection aid for dismounted soldiers. Disclosure of known ATD identification bias to the operator may aid the identification process.

Human factors assessment of conflict resolution aid reliability and time pressure in future air traffic control

Though it has been reported that air traffic controllers' (ATCos') performance improves with the aid of a conflict resolution aid (CRA), the effects of imperfect automation on CRA are so far unknown. The main objective of this study was to examine the effects of imperfect automation on conflict resolution. Twelve students with ATC knowledge were instructed to complete ATC tasks in four CRA conditions including reliable, unreliable and high time pressure, unreliable and low time pressure, and manual conditions. Participants were able to resolve the designated conflicts more accurately and faster in the reliable versus unreliable CRA conditions. When comparing the unreliable CRA and manual conditions, unreliable CRA led to better conflict resolution performance and higher situation awareness. Surprisingly, high time pressure triggered better conflict resolution performance as compared to the low time pressure condition. The findings from the present study highlight the importance of CRA in future ATC operations. Practitioner Summary: Conflict resolution aid (CRA) is a proposed automation decision aid in air traffic control (ATC). It was found in the present study that CRA was able to promote air traffic controllers' performance even when it was not perfectly reliable. These findings highlight the importance of CRA in future ATC operations.