<figure><img src="image_1.png" /></figure>

# Human-in-the-Loop: Decision Making in AI-Supported Non-Destructive Testing

**Daniel KANZLER**<fnref n="1" />, **Marija BERTOVIC**<fnref n="2" />

## Abstract

The increasing integration of automation and artificial intelligence (AI) in non-destructive testing (NDT) is not only changing the inspection processes themselves, but also the way decisions are made. While technical systems can reduce error-proneness and support data processing, the ultimate responsibility remains with the human. This paper examines the role of intuition in decision-making and analyses typical errors of judgment using prospect theory and insights from cognitive psychology. It also shows how well-informed decisions can be supported in AI-supported NDT processes - through training, explainable systems, user-centred design, suitable metrics, and a targeted distribution of tasks between people and technology. Rather than replacing human intuition, AI systems should be designed to complement it. To engage effectively with such systems, inspectors require not only technical expertise, but also competencies in risk assessment, probabilistic reasoning, and critical reflection on both their own judgments and the outputs provided by AI.

**Keywords:** Non-destructive testing (NDT), Artificial intelligence (AI), Decision Making, Cognitive Biases, Reliability, Human-Machine-Interaction, Standardisation

## 1. Introduction

Everyone knows the stories of firefighters who suddenly leave a burning house for no apparent reason - just seconds before it collapses. Or the parent who instinctively makes the right move to save their child from falling off the climbing frame. For millions of years, this intuition has protected us from danger and ensured our survival - a reason to be proud of. But what exactly is intuition and why should we think about it - especially at a time when artificial intelligence (AI), automation and machine intelligence are becoming increasingly important?

Intuition is not just something that helps in everyday life or emergencies—it also plays a role when experts make tough decisions under pressure. In non-destructive testing (NDT), for example, inspectors often work with uncertainty and incomplete information. Their gut feeling, shaped by experience, can be just as important as technical data. But now, with AI and automation becoming more common, this way of working is starting to change.

It cannot be assumed that humans will be completely taken out from the processes of production, maintenance, and infrastructure construction - at least not in the foreseeable future. If this does happen at some point, it will probably be long after our time. Meanwhile, modern NDT relies heavily on skilled human expertise, which is under increasing pressure. This creates urgent challenges that require immediate solutions. Among these challenges are the shrinking pool of experience, a problem that will only get worse given the declining interest in science subjects among schoolchildren [1], or an increasing complexity of components and new manufacturing processes such as additive manufacturing. These challenges are visualised in Figure 1, along with a range of tools that can be used to address them. These include predictive NDT methods, such as risk-based and structural integrity analyses, which are increasingly supported by AI systems that learn from past inspection data to anticipate failures before they occur. Furthermore, AI can improve systematic methods, such as probabilistic evaluations, by analysing large amounts of inspection data and helping with real-time decisions that consider uncertainty. Finally, standards and guidelines ensure consistency, safety, and traceability. They

<footnote n="1">¹ AV-NDT, Berlin, Germany; +49 159 0454 2678; KanzlerD@av-ndt.com</footnote>

<footnote n="2">² Federal Institute for Materials Research and Testing (BAM Bundesanstalt für Materialforschung und -prüfung), Berlin, Germany; marija.bertovic@bam.de</footnote>
also support the responsible use of AI by defining requirements for system validation, explainability, and the changing role of inspectors.

<figure><img src="image_2.png" /><figcaption>Figure 1. Challenges of modern NDT and tools to address them</figcaption></figure>

At first glance, the development of AI and automation appears to be the solution to these challenges. For example, with the use of AI the “simple” inspector becomes superfluous (although not in every area of NDT), and the problem of a lack of experience is solved. But is it indeed?

In reality, there are cases where AI fails in every industry [2][3]. Even validation systems, such as those required by the European Union Artificial Intelligence Act (EU AI Act) [4], reach their limits - benchmark systems, for example, fail [5]. In the end, humans remain the decisive authority over the AI systems. But are they able to take on this responsibility? And should they base their decisions - as so many do in everyday life - on their intuition?

One thing is certain: with AI, the role of inspectors is going to change fundamentally. They will no longer just be responsible for carrying out and documenting the inspection. With the introduction of AI and automation in NDT, they will be taking on new tasks - such as critically evaluating and validating the outputs or recommendations of automated decision systems, as well as recognizing system limits, and determining appropriate actions. This not only increases responsibility, but also the complexity of the decisions to be made.

These tasks require more than just technical knowledge or experience—they demand intuition, critical thinking, and the ability to weigh uncertain information in real time. It is thus becoming increasingly important to prepare people specifically for this new role - not only through training, but also through a holistic, user-centred design of human-machine interaction [7]. This means, among other things, understanding the limits of human cognition while meeting those challenges by designing systems that are comprehensible, explainable, and intuitive—with appropriate task distribution between humans and AI and clearly defined responsibilities.

In this paper, we focus on human decision making. We explore how NDT personnel make decisions in an increasingly automated environment, the roles that intuition, experience and cognitive biases play, and what support they need to make effective decisions in their new role.

## 2. Chess Grandmasters, Traders, and Inspectors: Understanding Intuition and Risk in NDT

Intuition can be understood as “beliefs or heuristics, based on prior knowledge, experience, or pattern recognition” that guide people in deciding when to trust or override automated systems
[6] In expert domains such as NDT, intuition emerges from repeated exposure to signals, feedback, and contextual cues, allowing inspectors to make rapid but informed judgments under uncertainty.

Anyone who enjoys playing chess knows the special dynamics of blitz chess. This variant requires quick decisions, as an entire game must be completed within a few minutes. Thinking is only possible in particularly critical positions - the rest is based on intuition. Nevertheless, experienced players usually dominate. In this simple case, intuition can be understood as the sum of accumulated experience in the game. Grandmasters have the most knowledge and the most playing experience - no wonder Magnus Carlsen also became “co-” blitz world champion [8]. The bottom line: intuition is important and helpful, even for complex tasks. Share trading provides a counterexample. The stereotypical trader à la Wolf of Wall Street may be perfect for the big screen, but in reality, buying and selling stocks on pure intuition in the short term rarely leads to long-term wealth [9][10]. Here, intuition is more of a hindrance, as it leads to impulsive decisions that are not necessarily rational or strategically advantageous. These contrasting examples show that intuition is not universally reliable. Whether it supports or hinders decision-making depends on the nature of the task, the environment, and the stakes involved.

This brings us to NDT. As NDT experts, we usually see ourselves as risk-averse - as people who try to minimize risks. In reality, however, we are both risk-averse and risk-seeking, depending on the situation. This finding was scientifically substantiated by Daniel Kahneman [9, 10, 11] and led to the development of prospect theory, as shown in Figure 2. The degree of risk-seeking is often characterized as v. The theory states that people tend to act cautiously in winning situations, preferring a sure win rather than taking a higher risk for a potentially greater gain. Conversely, when a loss seems likely, people tend to take more risks in the hope of avoiding it. The different gains and losses are weighted individually (with the factor), depending on our reference point - i.e. how much money or resources are currently available to us. The amount of the potential gain or loss also influences our decision-making.

<figure><img src="image_3.png" /><figcaption>Figure 2. Prospect theory according to D. Kahneman</figcaption></figure>

The question now arises: what world does NDT operate in? Daniel Kahneman introduced the concept of “probabilistic insurance” as a thought experiment [9]. This is an insurance policy
that does not guarantee to pay out in the event of a claim, but only with a certain probability. This highlights an important idea: people are generally uncomfortable with uncertainty, even when it is quantified and clearly communicated. Even if this situation initially appears theoretical, it shows clear parallels to NDT. Like probabilistic insurance, NDT does not provide absolute certainty. It can only reliably detect defects with a certain probability, depending on factors such as defect size, material properties, inspection conditions, and human performance. As a result, even the best-executed NDT cannot guarantee that a component is defect-free, but only that it is so with a certain degree of confidence. Understanding this probabilistic nature is essential for both interpreting results and making decisions under uncertainty.

### 3. The counter-intuitive world of the quality inspector

The concept of “risk seeking in the event of losses” is something that we do not really want to transfer to NDT. Nevertheless, examples from quality management show that similar considerations play a role in practice - such as the cases of OceanGate’ Titan submersible implosion in 2023 and Tesla’s 2025 recall of nearly 380,000 vehicles due to power steering failures [12, 13]. The often-heard statement “NDT only costs money” also follows this line of thought.

Nevertheless, it is clear that NDT is an essential tool in quality and risk management. Its central function is to reduce the probability of component failure by describing the current condition of a component. If a defect is detected, for example, it is possible to react in good time, significantly reducing the risk of failure.

A look at DIN EN 16991 [14] confirms this role of NDT in reducing the probability of failure. In a typical risk matrix, see Figure 3, the operating point on the y-axis shifts the probability of failure downwards. However, NDT, which is often perceived as “expensive”, is only used when it makes economic sense. In the further analysis, minor effects of failures are therefore initially disregarded. An interesting aspect is the “black area”, which is not explicitly shown in classic risk matrices, but is covered by the legal offense of negligence. The case of OceanGate [12] provides a tragic example of the consequences of negligence in safety-critical areas. Clear rules and standards and justified criticism from safety and quality management experts were disregarded, which led to the world-famous accident involving the submarine.

<figure><img src="image_4.png" /><figcaption>Figure 3: NDT Risk Matrix</figcaption></figure>

In addition, there are important differences in the representation of risk:

* The probability of failure is depicted on a logarithmic scale: Technical failures occur very rarely in absolute terms, so a linear representation would not be meaningful.
* The effects of failure often follow a non-linear rise. Major technical disasters - such as airplane crashes - can have extreme consequences, be it dramatic price losses on the stock market or massive slumps in sales figures.

NDT is usually used for low *probabilities* of failure, however, for large *effect* of failure. In this region, these correlations make it clear that NDT is far more than a mere “cost centre” - it is a strategic instrument for minimizing risk and, in the worst case, can prevent consequences that could threaten a company's existence.

### 3.1. Cognitive biases in NDT

Increasing automation in NDT is not only changing the inspection processes, but also the way in which decisions are made. As emphasized in a study on the use of automated defect detection in NDT, new technologies also place new demands on inspection personnel [15, 16] - especially with respect to their role as decision-makers in an increasingly digitalized environment [7].

Although we humans possess incredible intuition, our decision-making process is a sophisticated but often flawed mechanism. Numerous studies show that people are prone to cognitive biases and systematic errors of judgment under uncertainty and time pressure [9][10][15][16][17]. Here are some examples of these cognitive biases and how they can lead to biased decision-making in NDT:

* **Risk-taking behaviour in the event of impending losses:** Prospect theory [9, 11] shows that people tend to take more risks in the hope of avoiding a loss. In NDT, the primary concern is to avoid negative outcomes (loss). This can lead to decision-makers being more willing to take risks despite potential dangers instead of acting preventively.

* **The possibility effect:** a tendency to overestimate very low probabilities, especially if the consequences are seen as severe [9]. This can result in overly cautious decisions. An example for this is the collapse of the Carola Bridge in Dresden [18]: before the accident the probability of structural failure was likely underestimated and then, after the accident, the public risk perception increased significantly, leading to an overestimation of the threat, even though there was almost no change in the actual statistical risk.

* **The availability heuristic:** a tendency to estimate the probability of an event based on the ease with which similar cases come to mind. Events that are recent, emotionally salient, or highly publicised are perceived as more likely than they objectively are [9]. In the context of NDT, inspectors may overestimate the likelihood of similar failures—not based on empirical data, but because a recent incident (e.g. Carola bridge collapse) is easily retrievable from memory. This bias can result in disproportionately conservative testing strategies. This, in turn, also means that the consequences of a failure, that has not yet happened, are difficult to grasp. Examples of this phenomenon can be found in [19][20], among others.

* **Automation bias:** a tendency to uncritically accept the output of automated systems or AI-based tools without verifying or critically questioning them [9]. In NDT, this can lead to critical indications being overlooked because too much trust and, thus, reliance is placed on the automated (AI) evaluation [15] [16].

* **Anchoring bias:** a tendency to rely too heavily on initial information (the so-called “anchors”) when making subsequent judgments [9]. In NDT, for example, an initial AI judgment (e.g. “no defect”) can shape the subsequent human assessment - even if there are later indications to the contrary.

* **Confirmation bias:** a tendency to select or interpret information in such a way that it confirms one's own expectations or preconceptions [9]. For example, if an inspector already
has a hypothesis (e.g. "no error"), AI results that confirm this are more likely to be accepted - and deviating indications may be ignored.

Such and many other cognitive biases are normal human thought patterns and shortcuts that help us all to act efficiently in everyday life—but in NDT, where the main task is increasingly shifting towards assessment and decision-making, we need to be more aware of these tendencies than ever.

## 3.2. Risk assessment in practice: a comparison with gambling

Applied to NDT, the prospect theory [9] and the game of chances (see Figure 2) mean that if, for example, the use of an inspection procedure is perceived as expensive or time-consuming, decision-makers may be inclined to forego the inspection - even if this results in a higher risk of failure. The decision appears situationally rational but is strategically risky.

## 3.3. Two major uncertainties in the NDT

In addition to the human component, there are two other major systemic uncertainties that influence every decision in NDT:

* Unclear follow-up costs: the long-term consequences of damage are often not fully foreseeable. Particularly in areas such as bridge construction or infrastructure, it is difficult to determine the exact value of a tested component or its cost in the case of a failure.

* Indirect link between an inspection method and actual failure rate: Even if an inspection method has a high defect detection rate, this does not necessarily mean that the failure rate can be reduced linearly in reality.

These uncertainties show that objective decision-making is not just a question of technology - it remains a complex balancing process that requires experience, contextual understanding, and system knowledge. However, one thing is clear: without knowledge of the limits of an inspection method, it is neither possible to estimate the probability of failure nor to make a well-founded decision.

# 4. Support for decision-making in AI-supported NDT

The preceding sections have shown that human intuition can be both a strength and a weakness in NDT decision-making. While cognitive biases may lead to misjudgements under uncertainty, experience-based intuition can also yield significant benefits when systematically trained and supported. In practice, inspectors often detect weak or ambiguous indications that automated systems may overlook. When coupled with feedback mechanisms and explainable AI tools, such trained intuition can generate measurable "gains"—for instance, by improving detection reliability, optimising inspection intervals, or extending component lifetime—an aspect also emphasised in recent work on intuition in human–AI decision-making [6]. Recognising and fostering these constructive aspects of intuitive expertise provides an important foundation for the following strategies that support effective and responsible human–AI collaboration in NDT.

While systematic training alone cannot solve every challenge, it remains the foundation for developing the competencies that allow inspectors to make well-reasoned and confident decisions—both to avoid errors and to use their expertise effectively. Of course, targeted
qualification is essential - for example as part of the internationally proposed "NDT Engineer" [21] or the "capable person" required by DIN 19293 [22], who is responsible for assessing the reliability of inspection methods and making decisions on their use. Well-informed decision-making is particularly important for safety-critical applications with a low probability of failure.

However, one crucial question remains unanswered: Where should these skills be acquired and how can they be systematically demonstrated? Currently, the responsibility often lies with the respective companies, which define the relevant competencies in their quality manuals. This approach has some clear benefits: a) it connects NDT more closely with quality management and everyday decision-making; and b) it gives organisations more responsibility and flexibility to apply inspection methods in a way that fits their specific needs and ensures reliability.

At the same time, studies such as [15][16][17] and practical experience show that qualification alone is not enough. Decision-making can and should also be supported by the following factors, among others:

* Transparent and explainable systems (keyword: explainable AI) that not only support oversight but also create valuable feedback loops through which inspectors can refine their intuition and calibrate trust in automated results, Intuitive, user-friendly, and trustworthy interfaces that avoid information overload,

* Realistic expectations of AI-supported systems that avoid both excessive and insufficient use,

* Organisational support that creates conditions where inspectors can apply and further develop their expertise through open error communication, realistic decision-making (without pressure), and clearly defined responsibilities.

Informed decision-making in NDT is not just a matter of individual expertise. It results from the interplay between human skills, intuitive and transparent technologies, and supportive organisational structures. Even in highly automated environments, safety and quality can only be ensured when systems are designed with usability, responsibility, and human factors in mind.

The following aspects highlight how decision-making in AI-supported NDT can be effectively supported:

* **Human factors:** Decisions in NDT are significantly influenced by human factors - e.g. intuition, experience, attention, or cognitive biases. These factors must be taken into account when designing systems, processes and training concepts.

* **Task allocation according to strengths:** In human-AI interaction, the distribution of tasks should not be based exclusively on what is technologically feasible, but on what supports effective and responsible decision-making. If people are to remain responsible for decisions, systems must be designed to play to human strengths—by providing meaningful decision support rather than replacing judgment. Systems should be designed in a way that encourages critical reflection, allowing users to actively engage with AI suggestions instead of accepting them without question [7][16].

* **Explainable AI:** AI systems should provide sufficient transparency to enable reliable human oversight. Visualisations, confidence levels and transparent decision paths enable trust, understanding and control over automated suggestions.

* **User-centred system design:** Intuitive, transparent, and explainable interfaces reduce cognitive overload, improve information processing, and increase technology acceptance. Users should be involved in development at an early stage [7][23][24][25].
* **New skills (education & training):** Only those who understand how automated systems work, where their strengths and where their limitations lie, can correctly interpret their results. Working effectively with AI requires new skills—not only technical knowledge but also competencies in risk assessment, probabilistic reasoning, and critical reflection on both their own judgments and the AI outputs. Training programmes must therefore go beyond technical instruction to develop these essential cognitive and decision-making abilities that strengthen both analytical reasoning and experience-based intuition [7].

* **Realistic evaluation of inspection methods:** NDT methods do not provide absolute truths. Metrics such as the probability of detection (POD) must be interpreted in context and uncertainties must be actively communicated in order to support informed and confident judgement.

* **Organisational integration of NDT:** NDT decisions must be clearly integrated into management and quality processes - for example in accordance with DIN 19293 [22]. Responsibilities and decision-making paths must be clearly defined and documented.

* **Economically justified application of NDT:** The use of NDT must be based on a careful evaluation of the associated costs, expected benefits and the potential risks arising from failure [26].

* **Economic and legal framework conditions:** Inspections decisions also involve liability, costs, and responsibility. Standards, guidelines, and legal requirements (e.g. product liability, EU AI Act, national AI standardisation roadmaps) must be known and integrated into the decision-making process. These factors should be integrated into training, system design and decision-making processes from the earliest stage [26][27].

# 5. Conclusion

This paper has explored human decision-making in the context of AI-supported NDT, with a focus on the influence of intuition, experience, and cognitive bias. Intuition remains an essential part of human decision-making, particularly under time pressure or in familiar situations. In the context of NDT (where inspectors often face uncertainty and incomplete information) intuitive judgments can be both helpful and risky, i.e. they may enhance performance when situations align with prior experience but can become unreliable when applied to unfamiliar problems or novel environments.

The increasing integration of AI into NDT systems introduces additional layers of complexity. While AI systems can help address issues such as the loss of experienced personnel or increasing data volumes, they also shift the role of human inspectors. Rather than merely executing inspections, humans are now expected to validate automated results, recognise system limitations, and take appropriate decisions based on probabilistic and often incomplete information. This shift changes not only the cognitive demands placed on inspectors, but also the structure of responsibility and accountability.

To address these challenges, targeted measures are required at several levels. Training must not only convey technical knowledge but also strengthen risk competence, critical reflection, and the ability to interpret probabilistic outputs. At the system level, a user-centred approach to the design of human-AI interaction is essential. This includes explainable AI mechanisms, intuitive interfaces that support informed decision-making under uncertainty, and clearly defined human-machine task allocation based on inspectors' capabilities not merely AI's. Existing standards such as DIN 19293, as well as regulatory frameworks such as the EU AI Act, provide initial guidance for defining and supporting the evolving role of NDT personnel in AI-supported systems. To ensure these frameworks have practical impact, targeted dissemination through training, interdisciplinary cooperation, and standardisation-related committee work is essential.
These aspects underline the need to anchor NDT decision-making not only at the technical level, but also within broader organisational, economic, and legal frameworks ensuring that responsibilities are clearly defined, cost-benefit considerations are transparent, and relevant regulatory requirements are systematically reflected in both practice and training.

NDT will continue to rely on human decision-makers. Rather than eliminating intuition, the goal is to embed it within a structured and supportive system. When integrated with experience, probabilistic reasoning, and appropriate technical tools, intuitive expertise can contribute meaningfully to safe and informed decisions, even in the complex, probabilistic world of modern NDT.

While this paper mainly examined the limits and biases of intuitive judgment, future work should also explore how trained intuition can be systematically harnessed as a positive element in human-AI decision-making. Experience-based intuitive reasoning—rooted in pattern recognition and prior expertise—has the potential to enhance human oversight and contribute to measurable “gains,” such as improved reliability, efficiency, and component lifetime. Understanding how to integrate and quantify such benefits represents a promising direction for future research.

It would be particularly useful for managers and decision-makers if the effects of decision biases could be quantified to better understand their potential economic impact and to prioritise mitigation strategies accordingly. Although biases themselves cannot be measured directly, their influence could be assessed through observable behaviours such as failed checking or omission due to automation bias, as well as through general error rates or verification effort. These measurable outcomes could then be related to reliability and expressed in terms of POD. Quantifying such effects remains an ongoing pursuit, and possible approaches have been outlined in earlier work [28].

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**Note on the use of AI-supported language assistance:** ChatGPT from OpenAI was used to support the linguistic fine-tuning of the manuscript. All content contributions, analyses and sources were provided and checked by the authors themselves.