Automation in visual inspection tasks: X-ray luggage screening supported by a system of direct, indirect or adaptable cueing with low and high system reliability

Chavaillaz, Alain; Schwaninger, Adrian; Michel, Stefan; Sauer, Juergen

doi:10.1080/00140139.2018.1481231

Cited by 21 publications

(32 citation statements)

References 51 publications

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“…With advances in Artificial Intelligence, more and more intelligent agents are being deployed to aid human operators in completing tasks more effectively and efficiently [4]. Human-Agent Collaboration (HAC) often requires users to validate or invalidate agents' decisions in Command and Control (C2) scenarios, such as X-ray luggage screening [3,30], collaborative bomb disposal robots [8] or intensive care monitoring agents [16]. In these safety critical scenarios, collaborative agents facilitate the completion of tasks by aiding in the decision-making process [11].…”

Section: Introductionmentioning

confidence: 99%

Impact of Agent Reliability and Predictability on Trust in Real Time Human-Agent Collaboration

Daronnat

Azzopardi

Halvey

et al. 2020

Proceedings of the 8th International Conference on Human-Agent Interaction

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Trust is a prerequisite for effective human-agent collaboration. While past work has studied how trust relates to an agent's reliability, it has been mainly carried out in turn based scenarios, rather than during real-time ones. Previous research identified the performance of an agent as a key factor influencing trust. In this work, we posit that an agent's predictability also plays an important role in the trust relationship, which may be observed based on users' interactions. We designed a 2x2 within-groups experiment with two baseline conditions: (1) no agent (users' individual performance), and (2) near-flawless agent (upper bound). Participants took part in an interactive aiming task where they had to collaborate with different agents that varied in terms of their predictability, and were controlled in terms of their performance. Our results show that agents whose behaviours are easier to predict have a more positive impact on task performance, reliance and trust while reducing cognitive workload. In addition, we modelled the human-agent trust relationship and demonstrated that it is possible to reliably predict users' trust ratings using real-time interaction data. This work seeks to pave the way for the development of trust-aware agents capable of adapting and responding more appropriately to users. CCS CONCEPTS • Human-centered computing → Empirical studies in HCI; Collaborative interaction; User studies; Laboratory experiments.

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Section: Introductionmentioning

confidence: 99%

Impact of Agent Reliability and Predictability on Trust in Real Time Human-Agent Collaboration

Daronnat

Azzopardi

Halvey

et al. 2020

Proceedings of the 8th International Conference on Human-Agent Interaction

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“…A modified version of the Luggage Inspection Simulation (LIS) served as model for the inspection task (see for instance, Chavaillaz et al, 2018). In this task, participants had to decide as fast and as accurately as possible whether each X-ray image contained a prohibited item (either a gun or a knife) or not (i.e.…”

Section: Methodsmentioning

confidence: 99%

“…In addition to automated explosive detection, algorithms for detecting guns and knives have been developed in recent years (e.g., Mery et al, 2013) and such systems are currently being tested at airports (Lehr, 2019). While a substantial amount of research is now available on visual inspection and visual search in X-ray images of passengers bags (see Biggs et al, 2018 for a recent review), only few studies have been conducted on automation as diagnostic aid in X-ray baggage screening (Wiegmann et al, 2006; Chavaillaz et al, 2018; Hättenschwiler et al, 2018). The relationship between automation, expertise and different aspects of trust in X-ray baggage screening has not been investigated yet.…”

Section: Introductionmentioning

confidence: 99%

“…Recent studies showed the benefits of implementing automated detection in airport screening, as observed in other work domains such as process control (e.g., Rovira et al, 2007; Chavaillaz et al, 2016). Higher performance levels are achieved by experts in detecting explosives (Hättenschwiler et al, 2018) and by untrained participants in detecting hand guns and knives in cabin baggage (Chavaillaz et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, reliance corresponds to user’s propensity to acknowledge DA recommendations when it indicates having detected no issue. Many studies investigated those aspects in the context of support systems (e.g., Dzindolet et al, 2003; Rovira et al, 2007; Chavaillaz et al, 2018), but it seems that no research considered the impact of expertise in an inspection task. The current work addressed this issue for trust perception and behaviors.…”

Section: Introductionmentioning

confidence: 99%

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Expertise, Automation and Trust in X-Ray Screening of Cabin Baggage

et al. 2019

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X-ray screening of passenger baggage is a key component in aviation security. The current study investigated how experts and novices performed in an X-ray baggage screening task while being assisted by an adaptable diagnostic aid. Furthermore, it examined how both groups operated and trusted this automated system. 30 experts (certified screeners) and 31 novices (students) had to indicate whether a target item (either a knife or a gun) was present in a series of X-ray images of cabin baggage. Half of the participants could choose between three different support levels of the diagnostic aid (DA): (1) no support, (2) a cue indicating the presence of a potential target without locating it, or (3) a cue indicating the presence of a potential target by surrounding it with a red frame. As expected, experts achieved higher detection performance (d’), were more self-confident and felt more competent in achieving the task than novices. Furthermore, experts experienced less time pressure and fatigue. Although both groups used the DA in a comparable way (in terms of support level used and frequency of level switches), results showed a performance increase for novices working with the DA compared to novices without support. This benefit of DA was not observed for experts. Interestingly, despite no difference in perceived trust ratings, experts were more compliant (i.e., following DA recommendations when it indicated the presence of a target) and reliant (i.e., following DA recommendations when it indicated the absence of a target) than novices. Altogether, the results of the present study suggested that novices benefited more from a DA than experts. Furthermore, compliance and reliance on DA seemed to depend on expertise with the task. Since experts should be better at assessing the reliability of the DA than novices, they may have used the DA as ‘back-up’ to confirm their decisions based on expertise (confirmatory function), while novices may have used it as a guide to base their decisions on (support function). Finally, trust towards a DA was associated with the degree to which participants found the DA useful.

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The benefits and costs of explainable artificial intelligence in visual quality control: Evidence from fault detection performance and eye movements

Müller,

Reindel,

Stadtfeld

2024

Hum Ftrs & Erg Mfg Svc

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Visual inspection tasks often require humans to cooperate with artificial intelligence (AI)‐based image classifiers. To enhance this cooperation, explainable artificial intelligence (XAI) can highlight those image areas that have contributed to an AI decision. However, the literature on visual cueing suggests that such XAI support might come with costs of its own. To better understand how the benefits and cost of XAI depend on the accuracy of AI classifications and XAI highlights, we conducted two experiments that simulated visual quality control in a chocolate factory. Participants had to decide whether chocolate molds contained faulty bars or not, and were always informed whether the AI had classified the mold as faulty or not. In half of the experiment, they saw additional XAI highlights that justified this classification. While XAI speeded up performance, its effects on error rates were highly dependent on (X)AI accuracy. XAI benefits were observed when the system correctly detected and highlighted the fault, but XAI costs were evident for misplaced highlights that marked an intact area while the actual fault was located elsewhere. Eye movement analyses indicated that participants spent less time searching the rest of the mold and thus looked at the fault less often. However, we also observed large interindividual differences. Taken together, the results suggest that despite its potentials, XAI can discourage people from investing effort into their own information analysis.

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Automation in visual inspection tasks: X-ray luggage screening supported by a system of direct, indirect or adaptable cueing with low and high system reliability

Cited by 21 publications

References 51 publications

Impact of Agent Reliability and Predictability on Trust in Real Time Human-Agent Collaboration

Impact of Agent Reliability and Predictability on Trust in Real Time Human-Agent Collaboration

Expertise, Automation and Trust in X-Ray Screening of Cabin Baggage

The benefits and costs of explainable artificial intelligence in visual quality control: Evidence from fault detection performance and eye movements

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