# Bringing NLP Explainability to Critical Sectors: A Case Study on NOTAMs in Aviation

Vincent Mussot, François Hoofd, Fanny Jourdan, Antonin Poché

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Vincent Mussot, François Hoofd, Fanny Jourdan, Antonin Poché. Bringing NLP Explainability to Critical Sectors: A Case Study on NOTAMs in Aviation. 13th European Congress of Embedded Real Time Systems (ERTS), Feb 2026, Toulouse, France. 10.82331/ERTS.2026.40. [hal-05513839](https://hal.science/hal-05513839v1)

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# Bringing NLP Explainability to Critical Sectors: A Case Study on NOTAMs in Aviation

Vincent Mussot¹, François Hoofd¹,², Fanny Jourdan¹, and Antonin Poché¹,³

¹ IRT Saint Exupéry, ² THALES AVIONICS, ³ IRIT – all at Toulouse, France

firstname.lastname@irt-saintexupery.com

## ABSTRACT

The aviation industry operates within a highly critical and regulated context, where safety and reliability are paramount. As Natural Language Processing (NLP) systems become increasingly integrated into such domains, ensuring their trustworthiness and transparency is essential. This paper addresses the importance of explainability (XAI) in critical sectors like aviation by studying NOTAMs (Notice to Airmen), a core component of aviation communication. We provide a comprehensive overview of XAI methods applied to NLP classification task, proposing a categorization framework tailored to practical needs in critical applications. We also propose a new method to create aggregated explanations from local attributions. Using real-world examples, we demonstrate how XAI can uncover biases in models and datasets, leading to actionable insights for improving both. This work highlights the role of XAI in building safer and more robust NLP systems for critical sectors and also shows that academic efforts must be pursued to achieve trust in models and XAI itself.

## 1. INTRODUCTION

Following its widespread success across industries, Artificial Intelligence (AI) is gradually entering critical domains, where it faces new safety-related challenges, and where its probabilistic nature becomes a barrier to adoption. In Europe especially, regulation governing the use of AI has recently started to emerge, following the entry into force of the AI Act [40].

In the aviation domain, where safety is paramount, the norms and regulations in construction often place *explainability*, *robustness* and *fairness* at the center of the discussions. However, it is not always clear what is expected from these broad attributes and how they can be leveraged to increase the trustworthiness of systems that contain AI-based models.

The definition of *Explainability for AI* (XAI) usually revolves around the “capability to provide the human with understandable, reliable and relevant information [...] on how an AI/ML application produces results” [8]. This active field of research has emerged due to the shift of paradigm from traditional software to data-centric and learning-based algorithms, which produces highly efficient but inherently opaque models. Unlike traditional software, the behavior of these models cannot be analysed with traditional logging, debugging or instrumentation methods as their responses are directly encoded in their

weights (representing the function they approximate), rather than in interpretable code or logic. Consequently, XAI research aims to develop tools and methods to uncover and clarify how the decisions of these models are formed, in the hope of increasing trust in AI within critical applications.

In civil aviation, the development of complex airborne systems is primarily governed by the Aerospace Recommended Practice ARP 4754B [33], which provides a rigorous engineering process which covers every aspect of the development lifecycle, from requirements capture to integration, verification and validation. At software level, the standard DO-178C [31] classifies applications according to their criticality level and ensures that softwares perform correctly under all expected operating conditions, thanks to exhaustive documentation, robust testing and verification by aviation regulatory bodies such as the Federal Aviation Administration (FAA) or European Union Aviation Safety Agency (EASA).

Secondly, the standard DO-178C [31] (Software considerations in airborne systems and equipment certification) provides guidelines for software development and safety verification to ensure that the software performs correctly under all expected operating conditions. The standard defines various levels of criticality, from Level A (most critical, where failure could result in catastrophic events) to Level D (least critical, where failure has minimal impact).

Compliance with DO-178C requires comprehensive documentation, robust testing, and rigorous reviews. The process includes activities such as requirements capture, design verification, and verification testing, with an emphasis on traceability between each of this stages. The certification process is overseen by aviation regulatory bodies such as the FAA or EASA, which ensure that the software meets all safety and operational criteria before it can be used in airborne systems.

However, none of these existing standards integrate the specificities of the ML-based systems, which is why the redaction of a new standard named ED-324/ARP-6983 has recently started. Its current draft introduces explainability recommendations, lists commonly used techniques, and highlights that each applicant must choose and justify the methods that are the most appropriate for the intended application.

This paper aims at showing the steps and the questions that an applicant may encounter in his choices around the application of explainability methods. To ground these questions, we will use a classifier of Notices to Airmen (NOTAMs) as an illustrative use case. These essential messages are issued to inform pilots of all recent information that could be useful along their

<footnote>Vincent Mussot et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</footnote>
flight route, but their volume and complexity have become a problem for pilots [1]. Their analysis is currently performed manually, and this tedious process could benefit from NLP techniques for filtering, prioritization or even summarization.

Yet the introduction of opaque ML models in this highly critical domain brings new risks to the table and brings forth the need for XAI. This aspect is crucial both from a regulation point of view but also for adoption by stakeholders, as they need to obtain insights about how the AI algorithm determines the relevance and prioritization of NOTAMs in order to trust and effectively use these automated solutions. However, an end-user might not require the same kind of explanations as an ML developer.

This paper focuses on the regulatory point of view regarding the use of XAI in ML development, validation, verification, and in-service analysis, in the context of NOTAMs processing. It is organized as follows:

First, Section 2 presents the main families of XAI methods relevant for NLP models and the categorization framework we rely on throughout the paper. Section 3 then introduces our NOTAM use case, describes the system under consideration together with its Operational Design Domain (ODD) [32], and outlines key performance and safety requirements. Section 4 details our experimental setup, including our dataset, the model architectures evaluated and the XAI instrumentation, while Section 5 reports the benchmark performances. Sections 6 and 7 form the core of the paper, focusing respectively on local explanations to inspect individual NOTAMs and on aggregated explanations to obtain class-level insights and detect dataset or model biases. Finally, Section 8 draws the main lessons learned and discuss how these findings relate to the assurance needs identified earlier.

## 2. XAI METHODS FOR NLP

Understanding and interpreting AI models, particularly those based on deep learning, is crucial for improving their performance and ensuring their reliability. In the aeronautics domain, where NLP models handle critical information such as NOTAMs, enhancing model interpretability can directly impact operational safety and efficiency.

A fundamental first step in this process is to employ traditional software engineering practices, such as manual error analysis. By systematically reviewing the errors made by the model, we can gain valuable insights into both the system and the dataset. For instance, we identified the presence of non-English NOTAMs and truncated messages that the model struggled to process. This straightforward approach also highlighted the challenge of assigning a single class to a NOTAM, as these messages often encompass multiple concerns, such as fuel shortages at parking areas or procedures changes for specific runways. While such traditional error-analysis techniques already provide actionable insights, they remain insufficient to understand how complex models form their decisions. This motivates the use of more advanced Explainable AI (XAI) methods.

State-of-the-art explainability techniques provide more sophisticated tools for interpreting complex NLP models. By applying these methods, we can uncover hidden biases, ensure compliance with aviation regulations that demand transparency, and ultimately develop more robust and trustworthy AI systems.

In this context, the ED-324/ARP-6983 proposes a list of explainability methods which details many different elements like Feature Importance, Partial Dependence Plots, Local Interpretable Model-agnostic Explanations, Shapley Additive Explanations, Counterfactual Explanations, Rule-Based Models, Decision Trees, Attention Mechanisms, Sensitivity Analysis, Feature Visualization, and Integrated Gradients.

However, this list mixes methods and families, and contains techniques that are overlapping, while some other may not be suitable for explaining overly complex algorithms like those used in NLP. Therefore, we propose a more precise structuring of the state-of-the-art in NLP explainability, organizing the methods into categories inspired by [16], and presented as Figure 1. First, we focus on the main groups of methods: attribution techniques, rationalization, and concept-based approaches. Within these methods, there are also three paradigms: classical, contrastive, and counterfactual. This refined structure provides clearer guidance for selecting appropriate XAI methods in the context of NLP, especially for applications in aeronautics where model interpretability is crucial.

### 2.1. Attribution methods

Attribution methods assign importance scores to input features, highlighting the components that most significantly affect the model's output.

**Perturbation-based attribution** involves modifying input data and observing changes in the model's output. Occlusion [42] masks or removes words to assess their impact on predictions. Local Interpretable Model-agnostic Explanations (LIME) [29] approximates the model's behavior locally by perturbing inputs and fitting an interpretable model. KernelSHAP [23] estimates feature importance by perturbing input features and approximating Shapley values [35] through a weighted linear regression, where the impact of each perturbation on the model output determines the attribution scores. Sensitivity analysis methods use tools like Sobol indices [38] to quantify the contribution of input features [10, 25].

**Gradient-based attribution** computes gradients to assess each input's contribution. Methods like saliency map [36] calculate gradients of the output with respect to inputs, highlighting influential regions. SmoothGrad [37] and its variants, VarGrad [30] and SquareGrad [13], improve saliency maps by aggregating gradients over noisy input perturbations, using averaging, variance, or squared gradients to reduce noise. Integrated Gradients (IG) [39] computes average gradients along a path from a baseline to the actual input, capturing comprehensive input influence. GradientSHAP [23] combines ideas from IG and SmoothGrad by estimating attributions through gradients computed over randomly sampled baselines and noisy inputs, providing a stochastic approximation of Shapley values.

**Internal-based attribution** leverages model architectures, especially Transformers. Attention weight attribution [4] interprets predictions by analyzing attention weights assigned to input tokens, though its reliability has been questioned. Alternatives like Attention Flow [2] track attention across layers for a more faithful representation of information processing.
<figure><img src="image_2.png" /><figcaption>Figure 1. Categorization of the XAI methods for NLP, inspired by [16].</figcaption></figure>

Collectively, these attribution methods enhance model interpretability by revealing how input features influence predictions. They aid in identifying biases and improving model performance, which is crucial in domains like aeronautics where understanding model decisions is essential.

## 2.2. Concept-based methods

Concept-based methods build on human-understandable concepts to explain the decisions made by ML models. These concepts can be either predefined by humans (supervised) or discovered automatically (unsupervised).

**Supervised concept-based** XAI contains methods like Concept Bottleneck Models (CBMs) [19] train models where intermediate bottlenecks represent specific, human-interpretable concepts, ensuring that final predictions are directly dependent on these interpretable representations. On the Post-hoc side, Testing with Concept Activation Vectors (TCAV) [18] which quantifies the influence of predefined concepts on model predictions by analyzing the directional derivatives of the model's output with respect to internal activations corresponding to these concepts.

**Unsupervised concept-based** XAI aims to automate concept extraction without human input [9]. The ACE method [11] uses clustering algorithms to identify interpretable concepts in the activations of the models. COCKATIEL [17] automatically discovers pertinent concepts in the network's activation space using Non-negative Matrix Factorization (NMF) [21] and identified input features aligning with each concept using Sobol Indices [38] for NLP models.

## 2.3. Rationalization

Rationalization methods aim to build self-interpretable models that generate predictions together with human-readable explanations, typically in the form of minimal excerpts of the input supporting the model's decision called "Rationales".

Early work such as [22] introduced generator-encoder architectures to extract rationales, initially relying on reinforcement learning, later simplified through reparameterization and sparsity constraints [5, 15]. Some approaches further bridge rationalization and concept-based methods by associating rationales with predefined concepts learned in an unsupervised manner [6, 3].

While rationales are often highly plausible and intuitive for humans, they may not faithfully reflect the true decision process of the underlying model [14].

## 2.4. Others methods

While striving for an exhaustive overview of XAI methods, it is important to acknowledge that other approaches, such as logical explainability and example-based explanations, exist in the field. However, these methods are not commonly applied to NLP models. The complexity and nuances of modern NLP architectures, like Transformers, may render these methods less effective. Consequently, they are currently seldom used in practice for NLP tasks and will not be detailed here.

# 3. THE NOTAMs USE CASE

In this section, we provide a detailed description of our case of NOTAMs classification using AI-based system. Even if it is merely an illustration of an ML-based system and not a real product, our goal is to follow existing methodologies and tools to understand the benefits and identify potential gaps.

## 3.1. System Description

The system under consideration is a NOTAM classification application designed to help pilots treat information relative to their flight and reduce their workload when building their flight plan. The primary functionalities of this application are:

**NOTAM Classification:** The application takes as input all NOTAMs applicable to a single flight and assigns them one label (class). The definition of these classes follows a predefined classification scheme outlined in [24].

**NOTAM Retrieval and Presentation:** Upon request, the application must retrieve all NOTAMs belonging to a specified class and present them to the user (the pilot) in an accessible format.

## 3.2. Performance and safety Requirements

The integration of the NOTAM classification system into aviation operations requires careful considerations regarding performance and safety requirements. According to the Aviation Rule FAR 91.103 [26], pilots are mandated to be well-versed
in all relevant information about their flight before departure. Therefore, an AI-based system designed to assist in processing NOTAMs must ensure that it accurately conveys critical information to the pilot, while also preserving the entirety of the list of NOTAMs available.

Following the **Objective SA-01** from the EASA regulatory guidelines [8], we identified two main concerns regarding the introduction of AI in our application:

*   **Missed information:** The system might fail to correctly classify a critical NOTAM, leading to the pilot not being aware of an essential piece of information.

*   **Information overload:** The system may generate a high number of false positives (for a specific class, the amount of messages that do not belong here), which could overwhelm the pilot with excessive information, increasing the noise and the cognitive load. It would be ironic, as the data overload is one of the current system problems.

Regarding the first item, although systematically providing the entire list of NOTAMs ensures compliance with FAR 91.103, the application becomes useless if its performance is inadequate. Indeed, if the pilot loses trust in the system (feeling compelled to manually check the complete list to ensure that no NOTAMs have been missed for a given class), then the application fails to serve its purpose. Therefore, both achieving very high performance and maintaining confidence in the system are crucial in this case. However, reaching 100% accuracy is not feasible in practice, due to the statistical nature of AI-based system. It means that to ensure the adoption of the automated system by pilots, we have no choice but to try to relax this constraint into what could be considered an acceptable performance.

To define the requirements for this performance, we need to mitigate the risks identified, and we focus on recall and precision as the two main performance metrics for our classification system. Indeed, in terms of severity, as defined in [31], the consequence of each of the two identified risks is not equivalent. Missing a NOTAM could be associated with a major failure condition, expressed as a "significant increase in flight crew workload", while the inclusion of a single additional NOTAM in a class to which it does not belong only affects the trust that pilots have in the application.

**Recall requirement:** Based on the severity analysis, we can establish a target recall rates for each NOTAM class to minimize the likelihood of missing critical information. Specifically, for each class, we can set very low false negative rates depending on the worst type of NOTAM that could be missed in that specific class.

**Precision requirement:** On the other hand, we can use a less constrained value for the target precision. This is convenient to be able to reach a very high recall if needed<fnref n="1" /> while still ensuring that the system does not excessively burden the pilot.

### 3.3. Operational Design Domain

The Operational Design Domain defines the specific operating conditions under which a system is designed to function. The

ODD of our system, equivalent to that of the machine learning component it contains, is defined as follows:

*   **Scope of NOTAMs:** The system processes all NOTAMs that could be retrieved for a single flight from anywhere in the world.

*   **Language and type:** The system handles NOTAMs written in English only, and of types N (New) and R (Replace). NOTAMs of type C (Cancel) are considered out of the ODD: the NOTAM is canceled anyway.

### 3.4. Explainability constraints

One of the key frameworks guiding the development and the future certification of AI-based systems is the *W-shaped process*, described in [8]. We defined the Table 1 to identify the potential **entry points** for XAI within this process. In the remainder of this paper, the XAI methods we use are technical and computationally intensive, and are therefore hardly suitable for direct use by end-users, but they can be exploited to support incident investigators when analysing failures cases. The same analyses can, in principle, be repeated during V&V and at deployment, but their primary value is during model development, since discovering biases or misalignments typically requires returning to earlier stages of the lifecycle (data collection, model training), with a significant cost impact. Finally, some of these tools could also be applied periodically in operation to monitor potential drifts in the alignment between the model's decision process and expert expectations.

## 4. EXPERIMENTAL SETUP

This section presents our experimental setup that will serve as a reference for the subsequent XAI analyses. We describe the dataset and class definitions, the model families considered, and the way the models are instrumented for XAI.

### 4.1. Data and classes

Our objective behind the construction of the dataset was not to solve the problem of NOTAMs as a whole. We aimed for the creation of an industrially relevant use case, based on real aeronautical data, providing a realistic playground to evaluate tools like XAI and assess their suitability for certification concerns. A key limitation of this work is that annotation was performed by a single domain expert, whereas good practice would involve multiple independent annotators and dedicated procedures (e.g. for consensus and consistency checking) [41], but implementing such protocols was beyond the scope and resources of this exploratory research effort.

Our starting point was a dataset of 7 000 NOTAMS, labelled with poorly defined categories, and with potential overlaps in meaning. Our first activity was therefore to rework the categories and their definition and then to proceed with cleaning and relabelling of the dataset. The definition of the classes can be found in [24].

To increase coverage and reduce class imbalance, we then incorporated additional unlabelled NOTAMs from an earlier scraping campaign and complemented them with a new crawl that ran over several months. We identified under-represented classes and augmented them by combining keyword-based and Q-code searches with semi-automatic selection using a model

<footnote n="1">¹Maintaining a very high recall often comes at the cost of a lower precision.</footnote>
<table><caption>Table 1. Expectations from XAI depending on the stage of the lifecycle and the target audience</caption><thead><tr><td>Stage</td><td>Target Audience</td><td>XAI Expectations</td></tr></thead><tbody><tr><td>Development</td><td>ML Developer</td><td>Ensure the appropriate behavior of the model, using explanations to detect unintended or harmful biases and iterate over the model/learning dataset and obtain insights about the model's decisions and how it represents the problem</td></tr><tr><td>Operational Use</td><td>End-user</td><td>Obtain additional information about a decision, to understand the origin of the models' decisions and gain trust in the capacity of the model to handle the task</td></tr><tr><td>Post-Operation Analysis</td><td>Incident Investigator</td><td>Trace and identify the root cause leading to errors, either to identify biases in the model or data, and define workarounds or usage limitations or to estimate accountability in the case of catastrophic events</td></tr><tr><td>Verification and Validation</td><td>IVVQ Engineer</td><td>Exploit explanations independently on the test dataset to ensure the absence of unintended or harmful biases in the model</td></tr><tr><td>Deployment</td><td>ML Developer</td><td>Verify the preservation of the results obtained during verification and validation stage on the deployed model</td></tr><tr><td>Monitoring</td><td>ML Maintainer</td><td>Raise alerts when specific XAI-related metrics are not satisfied or thresholds are not reached</td></tr></tbody></table>

trained on the initial dataset, followed in all cases by systematic human verification. One might argue that relying on Q-codes alone should be sufficient to classify NOTAMs, but these fields are frequently missing in real data, and, when present, are often incorrect or inconsistently used, which means that systematic manual verification is required in any case.

## 4.2. Models

In this work, we focus on three representative deep learning families: a convolutional architecture (CNN [20]), a recurrent architecture (LSTM [12]), and a transformer-based model (DistilBERT [34]). Our analysis is deliberately restricted to deep and transformer-based models, which now constitute the de facto standard for complex NLP tasks and are precisely those for which recent, more sophisticated XAI methods have been developed. Traditional machine learning approaches can reach acceptable performance on this task and are often intrinsically more transparent, which reduces the added value of complex XAI procedures. Our objective here is therefore not to identify the most interpretable possible model, but to assess to what extent modern XAI techniques can provide useful insights on realistic, opaque architectures that are likely to be required for more complex future use cases. We also defined a pre-processing which includes entity masking for domain-specific keywords or specific values (like runway and taxiway identifiers, frequencies or altitude information). Finally, we compare models under two tokenization setups: a “base” tokenizer which is an off-the-shelf publicly available tokenizer pretrained on general English, and a “trained” tokenizer which is a domain-adapted tokenizer that we retrained on our NOTAM corpus.

## 4.3. XAI Instrumentation

In the following, we present results from various XAI methods and metrics applied to specific examples. We focus on Attribution methods, because they are the most mature and commonly used in the domain, and they usually correspond to the starting point of XAI studies. They are accessible through Interpreto [28], which we mostly used with the default hyper-parameters provided. We used word-level aggregation where token are aggregated to words by sum over sub-tokens for better readability of the results.

Moreover, we followed a process of verifying the quality of the XAI methods with dedicated metrics (insertion and deletion [27]) before attempting to exploit their results. These

<figure><img src="image_3.png" /><figcaption>Figure 2. Faithfulness metrics scores for the seven studied attribution methods, evaluated on a DistilBERT model with base tokenizer and raw text, using the whole dataset and aggregated over all predicted classes.</figcaption></figure>

metrics were computed for each class, and then averaged for comparison. In our context, the method *Kernel SHAP* obtained the best mean results for most model configurations, as illustrated in Figure 2. For this reason, the XAI analyses presented throughout this paper come mostly from this method.

# 5. EVALUATION METRICS AND BENCHMARK

We evaluate our NOTAM classifiers (in a single-label multi-class context) using three global metrics: accuracy, balanced accuracy and macro-F₁ and we present the results in Table 2. In our context, **accuracy** will correspond to the proportion of test NOTAMs whose predicted class matches the true class. The problem with this metric is that in situations with class imbalance, it may be dominated by the most frequent classes. Our complete dataset contains between ~400 and ~1600 NOTAM per class, which is why we also provide the **balanced accuracy**, which corresponds to the average of per-class recalls. In our context, it reflects how likely the system is, on average, to correctly retrieve a NOTAM of a given class, regardless of the proportion of this class in the dataset. This metric is completed by the **macro-F₁** which is also robust to
<table><caption>Table 2. Global test performances (in %) for each model architecture and tokenizer/pre-processing configuration. Columns are grouped by metric and then by tokenizer configuration where *base* and *train* correspond to a non-trained (resp. trained) tokenizer, and where *raw* and *proc* correspond to raw (resp. pre-processed) input text. **Bold numbers** show the best result for a given metric across all models and configurations, while **underlined numbers** indicate the best model for each configuration.</caption><thead><tr><th rowspan="2">Model</th><th colspan="4">Accuracy</th><th colspan="4">Balanced Accuracy</th><th colspan="4">Macro-F<sub>1</sub></th></tr><tr><th>base raw</th><th>base proc.</th><th>train. raw</th><th>train. proc.</th><th>base raw</th><th>base proc.</th><th>train. raw</th><th>train. proc.</th><th>base raw</th><th>base proc.</th><th>train. raw</th><th>train. proc.</th></tr></thead><tbody><tr><td>CNN</td><td>94.0</td><td>94.7</td><td>94.6</td><td>95.3</td><td>92.7</td><td>93.2</td><td>93.3</td><td>93.9</td><td>92.8</td><td>93.5</td><td>93.3</td><td>94.1</td></tr><tr><td>LSTM</td><td>93.7</td><td>93.8</td><td>93.0</td><td>93.2</td><td>91.9</td><td>92.2</td><td>91.3</td><td>91.7</td><td>92.3</td><td>92.3</td><td>91.4</td><td>91.8</td></tr><tr><td>DistILBERT</td><td><strong>96.0</strong></td><td><u>95.8</u></td><td>92.6</td><td>93.0</td><td><strong>95.1</strong></td><td><u>94.8</u></td><td>91.2</td><td>91.5</td><td><strong>95.0</strong></td><td><u>94.8</u></td><td>90.8</td><td>91.2</td></tr></tbody></table>

class imbalance, and corresponds to the unweighted mean of the $F_1$ score<fnref n="2" /> computed for each class separately. Because our requirements are more oriented towards class-wise recall target for different NOTAM categories, balanced accuracy and macro-$F_1$ will be the most important for us, as they indicate the overall risk of missing a NOTAM in each category. For the same reason, the macro-$F_1$ score was also used as the metric of interest for model validation and early stopping during training.

Overall, when using the base tokenizer, the DistILBERT model outperforms the others by more than one percentage point on all three metrics. However, the effect of training a tokenizer on the NOTAM corpus is not uniform across architectures: It brings small but consistent gains for the CNN and LSTM models, but degrades the performance of DistILBERT. Given the highly specialised nature of NOTAMs (dense abbreviations, domain-specific language and syntax far from natural language), this behaviour seemed counter-intuitive. A plausible explanation is that DistILBERT's pre-training strongly ties its internal representations to its original tokenizer. Replacing this tokenizer with one trained on NOTAMs creates a mismatch between subword segmentation used at fine-tuning time and the segmentation implicitly encoded in the pre-trained embeddings. By contrast, CNN and LSTM are trained from scratch and do not rely on pre-trained subword embeddings, which makes them free to adapt to the new tokenization scheme and benefit from it. Pre-processing has a modest but consistent positive effect for the CNN and LSTM models, and a slight negative effect for DistILBERT. This is reassuring from an interpretability standpoint, as this pre-processing step makes NOTAMs more readable for humans (by expanding abbreviations, normalising formats, abstracting non-meaningful information around numbers, coordinates, etc.) but does not significantly harm the overall performance of the models.

## 6. LOCAL EXPLANATIONS

The global results of Section 5 show that several model families are able to reach high performance on the NOTAM classification task presented in this paper. However, in a safety-critical context, these high scores are only part of the story. The collected dataset is only a proxy to the real-world data, and it may contain intrinsic biases that the models fed on to fulfil their task in the easiest way possible. XAI is one way to verify if models predict a given class by relying on cues that a domain expert would consider meaningful and relevant. In this view, we can use explanations to inspect single NOTAMs

<figure><img src="image_4.png" /><figcaption>Figure 3. Examples of attributions for Landing navaids</figcaption></figure>

and attempt to identify and validate those cues.

Throughout this section, we use attribution-based methods to analyse how our models make decisions on concrete examples, pursuing two practical objectives: verifying if correct predictions are driven by class-relevant tokens and analysing model errors to determine how a model fails and how sensible it could be two small input changes.

### 6.1. Attribution methods

In all figures of this section, attributions scores are provided by Interpreto [28] under the form of word-level heatmaps where red shading indicates positive attribution (the word supports the predicted class), while blue shading indicates negative attribution, meaning that the word “pulls” the model away from the predicted class.

We use these attributions in two complementary ways. On correctly classified NOTAMs, we ask whether the highlighted words are those a human expert would naturally use to justify the prediction. On misclassified examples, we inspect which words the model appears to rely on and how these changes when small edits are performed. In both cases, the goal is not to prove that the model is right or wrong, but to reveal potential shortcuts and instabilities that are invisible in performance metrics.

### 6.2. Verification of correct predictions

We consider two classes where the best DistILBERT performs well: Parking ($F_1$:92.9%) and Landing Navaids ($F_1$:99.4%).

Figure 3 shows several examples of NOTAMs correctly classified as Landing Navaids. In most cases, the method assigns most of the attribution weight to *ils* which stands for Instrument Landing System. Even on the last example where the attribution is more spread across words, the highest scores are on *rwy* and *gp* (respectively *runway* and *glide path*) which are highly relevant technical terms for this class. This pattern is

<footnote n="2">²Harmonic mean of precision and recall.</footnote>
<figure><img src="image_5.png" /><figcaption>Figure 4. Examples of attributions for the *Parking* class for a DistILBERT model with a base tokenizer</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Figure 5. Attributions on the *Parking* class for a DistILBERT model with a trained tokenizer</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>Figure 6. Effect of meaning-preserving manual changes.</figcaption></figure>

reassuring, as it suggests that the model bases its decisions on the same cues as a human expert would.

Figure 4 illustrates a similar situation for the Parking class. The model in this case focuses on *apron*, *parking*, *stand* and *prkg* which are near-synonyms in operational language. Other parts of the messages like timestamps or coordinates are largely ignored, which is reassuring since they should not determine the class.

These examples illustrate the kind of behaviour we would like to see from an operational model: the explanations are compact, focused on domain-relevant tokens, and largely ignore peripheral information such as timestamps or coordinates. In those cases, XAI acts as an efficient *sanity check* that confirms our trust in the model.

When we apply the same analysis to DistILBERT model that uses a trained tokenizer, the picture changes. Figure 5 shows that for the very same Parking NOTAMs, the predictions remain correct but the attributions become much more diffuse. The third case is even worse: the model does not seem to focus on *apron* and *stand* which are key words for the class.

We go a step further with the same model in Figure 6, where we replace *apron* with *stand*, which should not change the class. Although the correct prediction is preserved, the most influential word becomes the timestamp rather than the lexical cue associated with parking. This does not immediately invalidate the model, but it lowers our confidence that it will generalise robustly when faced with slightly different phrasing.

<figure><img src="image_8.png" /><figcaption>Figure 7. Erroneous predictions induced by replacing color-related words on a DistILBERT model with a trained tokenizer</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>Figure 8. Preservation of correct prediction despite the replacement of color-related words on a DistILBERT model with a base tokenizer</figcaption></figure>

In such cases, explainability is best viewed as an additional lens on the model and the dataset. It does not provide definitive answers, but it may point to aspects of the decision process that require further analysis.

## 6.3. Analysing model errors

When predictions become unreliable, we can leverage XAI to highlight questionable cues, or to understand why similar NOTAMs produce inconsistent predictions across different models.

The following study involves the *Signs and Lights* class, where we focus on a NOTAM misclassified to *Services and Facilities*. The explanation for this model, shown in Figure 7, is very diffuse, and we can notice that the colour word (*green*) receives an unexpectedly high attribution value compared to many important words from the human expert's point of view (such as *approach* *lgt* or *u/s*). In addition, when we modify the colour itself, the prediction can change, indicating a marked instability within the model. The explanations however, continue to highlight the colour term, still indicating its importance in the model's decision. By contrast, as illustrated in Figure 8, the model with a base tokenizer remains stable under the same edits, focusing almost only on lighting-related tokens, in line with the intended semantics of the class.

By looking more precisely at attribution scores on this sentence, we notice that the term *acft* appears to be the most important for the decision. Yet, since it is irrelevant for the class according to our expert, we attempt to further stress this instability by replacing it by a non-informative token, like “X”, as illustrated in Figure 9. This seemingly harmless shifts even more of the attribution weight on this meaningless token while keeping the model in error, revealing the brittleness of the model. Moreover, the attribution weight shift is so important that the colour term no longer influences the prediction. We can localize the source of the problem by looking at another model. As shown in Figure 10, when we repeat the colour experiment on the CNN model that uses a trained tokenizer, we obtain stable results, similar to those of DistILBERT with the base tokenizer. This suggests that the issue does not come from the tokenizer training *per se*, but from the interaction be-
<figure><img src="image_10.png" /><figcaption>Figure 9. Impact of replacing irrelevant words to identify unstable models</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>Figure 10. Preservation of correct prediction despite the replacement of color-related words on a CNN model with a trained tokenizer</figcaption></figure>

tween the trained tokenizer and the transformer-based model which leads to instability in some configuration.

Overall, even if local XAI does not fully explain the underlying cause of these issues, it allows us to look beyond performance and to identify the actual focus of the model when making its decisions and can serve as a guide to pinpoint issues, without having to exhaustively probe every word manually. Such behaviours had already been observed on toy examples in the literature, and our work show that they can also appear on real-world operational data.

## 7. AGGREGATED EXPLANATIONS

The local analyses of Section 6 showed that two models with similar global metrics can rely on very different cues at the sentence level. This is useful for identifying concrete failure modes, and, as such, it represents a valuable tool for ML developers or incident investigators. However, it is impractical for an engineer or an assessor to inspect explanations for thousands of NOTAMs across all classes. We therefore need a way to summarise what explanations tell us *globally* at the level of an entire class.

In this section, we move from individual NOTAMs to *aggregated explanations*. Our goal is to derive, for each class, a small set of high-level keywords that reflect the most important tokens for the model when predicting this class. These tokens can then be inspected by domain experts to check whether the model is aligned with operational concepts or dominated by artefacts. This provides a way to detect dataset biases and to monitor the effect of changes in the whole toolchain, including preprocessing, tokenization and training.

### 7.1. Aggregated explanations from local ones

A straightforward way to aggregate explanations would be to average attribution scores (e.g. SHAP or IG values) over all NOTAMs of a given class. However, these scores are inherently contextual since the score of each word is tied to the sentence where it appears. Simply averaging magnitudes could push rare cases to the top of the global ranking, even though they might not be representative of the class as a whole. To avoid this problem, we focus on *ranks* rather than raw values.

<figure><img src="image_12.png" /><figcaption>Figure 11. Top 20 words retrieved from each method for the class *Terminal enroute navaids*</figcaption></figure>

Concretely, for each class *C* and each word *w* appearing in at least one NOTAM of that class, we proceed as follows:

For a given NOTAM $\eta$, we rank its words by decreasing attribution score and denote $r_{\eta}(w)$ the rank of word $w$ in that NOTAM $\eta$ (with $r_{\eta}(w) = 1$ for the most important word). Then, we aggregate these per-NOTAM rankings with Reciprocal Rank Fusion (RRF) [7] and define the class-level score

$$s_C(w) = \sum_{\eta \in C} \frac{1}{k + r_{\eta}(w)},$$

where *k* is a smoothing constant (we use *k* = 60 as in the original RRF paper). Intuitively, a word that appears near the top of the explanation in many different NOTAMs will accumulate a high RRF score, whereas a word that is highly ranked only once will have a limited impact. In addition, as explained in the RRF paper [7], the constant *k* controls how quickly the contribution decays with rank: it reduces the dominance of top-ranked items while still assigning non-negligible weight to lower-ranked words.

This RRF produces, for each class, a list of words ordered by how often they are highlighted as important across all correctly predicted NOTAMs of that class. In the following, we analyse the lists of ranked words from examples classes (Landings navaids and Airspaces) and we examine whether the top-ranked words correspond to meaningful operational concepts or reveal potentially harmful biases.

### 7.2. Aggregated explanations on specific classes

We use aggregated explanations for multiple attributions methods on each class to retrieve the top-20 words considered important by each, and we use a simple global visualization where words are highlighted in green if they are present in every list and in yellow if they are common to at least 75% of methods.

**Terminal or Enroute Navaids:** For this class, as illustrated on Figure 11, several relevant acronyms (DME, VOR, NDB, and TACAN) appear among the top words for multiple methods. Each of these words refer to a specific type of navigational aid (navaid), so their presence provides further confidence in the model's correct "understanding" of the class, aligned with the expert's own representation.

**Airspaces:** For the Airspaces class, a closer look at the list of ranked words produced by RRF allows us to identify unusual
terms, like *ausots*, *flex*, or *tracks*. After analysis, these are
words emitted by the Australian Organised Track Structure
which correspond to a very specific type of NOTAM for Aus-
tralian airspaces. Further inspection shows that their promi-
nence does not result from the model training, but lies in the
dataset itself: among 529 NOTAMs truly labelled as Airspaces,
129 contain the word *ausots*. In other terms, the model has
significantly learned the Airspaces class as seen through *aus-
sots*-related NOTAMs. This reveals a strong geographical and
procedural bias in the data collection. From an assurance point
of view, this is valuable information: it tells us that the model
is likely to generalise better to *ausots*-like Airspace NOTAMs
than to other kinds of Airspaces notices, and that either the
dataset should be rebalanced, or this limitation should be ex-
plicitly captured in the ODD. It is worth noting that we could
have discovered this issue with a simple statistical analysis of
the most frequent word by class in our dataset, so XAI can be
seen as a safety net here.

**Landing Navaids:** This third example raises a question that would not have been considered without XAI. For the Landing Navaids class and with the DistilBERT model with a trained tokenizer and non-preprocessed text, the top RRF terms are *ILS*, *RWY*, *GP* (resp. Instrument Landing System, Runway, Glide Path) and other technical terms like *LOC* or *DME*. These terms align well with the expected operational concepts of the class. Interestingly, the integers 109 and 110 also appear near the top of the ranking. At first sight, this could be interpreted as an artefact of the tokenizer, but a closer look shows that these numbers correspond to typical ILS frequencies, which lie in the 108-111 MHz range. Moreover, a comparison with an independent database of ILS installations shows that the two numbers identified here are in fact over-represented in the real world, and that our dataset is closer to this distribution than a uniform one: 109 and 110 occur much more often than other frequencies. In this case, aggregated explanations do not reveal a spurious correlation, but they do highlight a domain-specific regularity that required verification. This, in turn, raises useful questions for data curation: should we augment the data to cover less frequent ILS frequencies more systematically, or is their rarity consistent with current ODD definition? It is worth noting that using a preprocessing can remove this particularity, as the numbers can be replaced by a generic word. However, defining a good preprocessing to mask numbers and irrelevant terms remains complex.

Taken together, these examples show how aggregated expla-
nations complement local ones: they transform isolated ob-
servations into class-level signals about dataset biases, com-
plex correlations that require further analyses, or misalign-
ment with what domain experts expect each class to represent.

8. LESSONS LEARNED

Our case study on NOTAM classification confirms that XAI
is neither a magic wand nor an optional add-on. Local and
aggregated explanations are imperfect method-dependent ap-
proximations of what a model does, and they can be misin-
terpreted if taken at face value. Nevertheless, in the context
of safety-related NLP for aviation, our experiments show that
focusing solely on performance and *not* using XAI runs the
risk of leaving critical issues completely invisible.

At the local level, attribution methods exposed behaviours that standard metrics could not reveal, such as an over-emphasis on irrelevant terms (colours, timestamps or arbitrary tokens), or a tendency to change decision abruptly under small, meaningless edits. These observations were essential to diagnose the instability of certain configurations and to understand that, in some cases, high-performing models were relying on fragile or invalid cues.

We also suggested a new method to aggregate local attribu-
tions. The resulting explanations can play a complementary
role, revealing geographical and procedural skew in the data
collection process and helping us detect reliance on artefacts
that needed deeper analysis and mitigation through dataset
corrections or ODD restrictions. They did not, by themselves,
solve the issues identified, but they pointed directly to where
a data or process problem was likely to be hiding.

It is equally important to acknowledge what XAI cannot do. Explanations remain approximate and are highly dependent on the chosen method and hyperparameters. More importantly, they may be biased by confirmation effects on the analyst side, and they cannot, by themselves, guarantee that a model is safe or certifiable. In our study, several questions raised by XAI required additional analysis, external data sources, or expert judgment to be resolved. In other words, XAI did not replace traditional testing or data analysis, it helped us decide *where* to look and *what* to question. Using XAI tools may also require time-consuming and resource-intensive processes, requiring to test each method, on each model, with each metric.

Overall, the key takeaway from this work is that performance
metrics alone are not sufficient to guide the design of AI com-
ponents in a critical system. Explainability will not remove
all uncertainties, but even in the worst case it forces engineers
and assessors to ask questions about the models, the data, and
the operational assumptions. In the best case, it reveals con-
crete weaknesses, biases in the training corpus, and supports
informed choices throughout the whole development chain.
Omitting XAI from the development and assurance process
of safety-related NLP systems would, in our view, be a much
greater risk than embracing it with its limitations.

Finally, the work we presented here can be built upon, for
instance by using the list from aggregated explanations to feed
new iterations of local analyses, focusing on NOTAMs which
do not contain the top-k words from these expert-validated
lists, or by exploiting more complex XAI methods like concept-
based or example-based ones, in a attempt to obtain more
meaningful insights about models’ decisions.

**Acknowledgement** This work was carried out within the DEEL project,<fnref n="3" />
which is part of IRT Saint Exupéry and the ANITI AI cluster. The authors ac-
knowledge the financial support from DEEL’s Industrial and Academic Mem-
bers and the France 2030 program – Grant agreements n°ANR-10-AIRT-01
and n°ANR-23-IACL-0002.

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