# Explaining the Unexplainable: Role of XAI for Flight Take-Off Time Delay Prediction

Waleed Jmoona, Mobyen Uddin Ahmed, Mir Riyanul Islam, Shaibal Barua, Shahina Begum, Ana Ferreira, Nicola Cavagnetto

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## To cite this version:

Waleed Jmoona, Mobyen Uddin Ahmed, Mir Riyanul Islam, Shaibal Barua, Shahina Begum, et al. Explaining the Unexplainable: Role of XAI for Flight Take-Off Time Delay Prediction. 19th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Jun 2023, León, Spain. pp.81-93, 10.1007/978-3-031-34107-6_7. hal-05498092

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# Explaining the Unexplainable: Role of XAI for Flight Take-Off Time Delay Prediction

Waleed Jmoona<fnref n="1" />(✉)D, Mobyen Uddin Ahmed¹D, Mir Riyanul Islam<fnref n="1" />, Shaibal Barua¹D, Shahina Begum¹D, Ana Ferreira², and Nicola Cavagnetto²

¹ School of Innovation, Design and Engineering, Mälardalen University, 72123 Västerås, Sweden

{waleed.jmoona,mobyen.uddin.ahmed,mir.riyanul.islam, shaibal.barua,shahina.begum}@mdu.se

² Deep Blue, Rome, Italy
{ana.ferreira,nicola.cavagnetto}@dblue.it

**Abstract.** Flight Take-Off Time (TOT) delay prediction is essential to optimizing capacity-related tasks in Air Traffic Management (ATM) systems. Recently, the ATM domain has put afforded to predict TOT delays using machine learning (ML) algorithms, often seen as “black boxes”, therefore it is difficult for air traffic controllers (ATCOs) to understand how the algorithms have made this decision. Hence, the ATCOs are reluctant to trust the decisions or predictions provided by the algorithms. This research paper explores the use of explainable artificial intelligence (XAI) in explaining flight TOT delay to ATCOs predicted by ML-based predictive models. Here, three post hoc explanation methods are employed to explain the models’ predictions. Quantitative and user evaluations are conducted to assess the acceptability and usability of the XAI methods in explaining the predictions to ATCOs. The results show that the post hoc methods can successfully mimic the inference mechanism and explain the models’ individual predictions. The user evaluation reveals that user-centric explanation is more usable and preferred by ATCOs. These findings demonstrate the potential of XAI to improve the transparency and interpretability of ML models in the ATM domain.

**Keywords:** Explainable Artificial Intelligence · LIME · SHAP · DALEX · Flight Take-off Time Delay Prediction · Air Traffic Management

## 1 Introduction

Artificial Intelligence (AI) has seen a surge in interest over the past decade due to the availability of massive volumes of data and the efficient calculation of learning algorithms using computer graphics card processors<fnref n="1" />. AI has been applied to

<footnote n="1">¹ https://www.coe.int/en/web/artificial-intelligence/history-of-ai.</footnote>
various domains, including ATM [8]. The availability of vast amounts of data in aviation compared to other transportation modes has contributed to this trend [15]. However, despite previous research in AI for the ATM domain, it has not been fully operational, nor has it brought significant benefits to end-users [8]. The slow progress in the application of AI in ATM is due to safety concerns, as the domain involves critical situations with human lives at stake. Nevertheless, the use of AI models such as Deep Convolutional Neural Networks (DCNN), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Gradient Boosting Machine (GBM) has gained significant attention for predicting delays in the ATM, as evidenced by recent studies [1, 6, 9, 18]. Understanding the reasons behind congestion, trajectory routes, and delay is essential for enhancing traffic. However, one of the challenges in using these algorithms is their lack of explainability, which has prompted the XAI community to focus on developing techniques such as Local Interpretable Model-agnostic Explanation (LIME) [14], Shapley Additive Explanations (SHAP) [13], and Model-Agnostic Language for Exploration and Explanations (DALEX) [2] to enhance the transparency of these models. XAI has emerged as a field of research that aims to provide transparency to high-consequence decisions made by AI systems in various domains, including healthcare, criminal justice, and ATM [11]. Further, in the XAI domain, the aim is to empower users in data analysis and decision-making processes.

This research focuses on XAI techniques applied to flight TOT delay prediction, which deals with capacity optimization in the ATM domain. The main contribution of the study is to investigate the acceptability and usability of different XAI methods using both quantitative and user evaluation processes. Here, RF and XGBoost are used to develop two predictive models for delay predictions. The SHAP, LIME, and DALEX are employed to explain the predictions. The performance of ML models and the goodness of explanations are compared in the quantitative evaluation. Two rounds of experiments were conducted in user evaluation to compare the acceptability and usability of the three XAI methods in explaining the prediction to ATCOs. In general, the initial results demonstrate the feasibility and effectiveness of XAI techniques in enhancing the transparency, interpretability, and performance of AI models in ATM, particularly for TOT delay prediction.

# 2 Materials and Methods

## 2.1 Dataset

The dataset used in this study was acquired from EUROCONTROL<fnref n="2" />, which contained the flight data messages from Enhanced Tactical Flow Management System (ETFMS) for all flights during the period of May - October 2019. In particular, the dataset contains approximately 9.5 million instances of EFD messages. The messages include basic information about the flights, their status, information on the previous flight legs, Air Traffic Flow Management (ATFM) regulations, calendar information, etc. A detailed description of the features can be found in the previous research work conducted with similar tasks of delay prediction [6].

<footnote n="2">² https://www.eurocontrol.int/.</footnote>
## 2.2 Prediction Models

A vast range of ML and AI models has been developed regarding ATM tasks. However, RF and XGBoost have been mostly used in prediction tasks (regression) based on our previous literature study reported in [11]. Therefore, in this paper, two different prediction models were developed with RF and XGBoost. The RF algorithm, which consists of a collection of randomised decision trees, is one of the well-known ensemble algorithms of ML utilised in numerous ATM-linked tasks [3]. The motivation behind using RF is that it can generalise better with both tabular and categorical data without assuming the independence of the features [3]. Concurrently, XGBoost was chosen with a view to enhance the performance of flight TOT delay prediction models developed with predecessor algorithms in the previous work by Dalmau et al. [7]. In particular, XGBoost is a scalable ML algorithm for tree boosting used in regression and classification applications [5]. It uses an ensemble of weak prediction models to make the final prediction, and a weak prediction model refers to the randomised decision trees.

## 2.3 Explanation Models

To explain the flight TOT delay prediction, LIME and SHAP were chosen as the most widely used explanation generation tool for heterogeneous data [11]. These tools can produce an explanation of the prediction from the models perspective. In addition, DALEX was invoked to provide users with provisions of interactions by selecting their preferred features while generating explanations. LIME [14] is a tool that uses an interpretable model to approximate each individual prediction made by any black box ML model. LIME uses a three-step process to determine the specific contributions of the chosen features: perturbing the original data points, feeding them to the black-box model, and then observing the related predictions. For example, in our delay prediction case, each prediction of a flight TOT delay is shown together with a list of features that contributes to the delay and their weights in that prediction. SHAP is a mathematical technique [13] that was developed based on the “Shapley Values” proposed by Shapley in the cooperative game theory [16]. Shapley values are a mechanism to fairly assign impact to features that might not have an equal influence on the predictions. To generate additive explanations for predictions from black-box models, the Shapley value concept was incorporated. In delay prediction, to explain the decisions from the model (i.e., prediction), SHAP calculates the contribution of each feature in the prediction from the model. DALEX is a python library built upon the software for explainable machine learning proposed by Biecek [2]. The main goal of the DALEX tool is to create a level of abstraction around a model that makes it easier to explore and explain the model. Explanation deals with two uncertainty levels, model level and explanation level. The underlying idea is to capture the contribution of a feature to the model's prediction by computing the shift in the expected value of the prediction while fixing the values of other features. In this paper, for delay prediction, DALEX was used to generate an interactive Breakdown plot, which detects local interactions of user-selected features.
## 2.4 Validation Approaches

**Evaluation Metrics.** In this study, evaluation metrics are used as prediction accuracy metrics, namely Mean Absolute Error (MAE), for both the prediction and explanation models. MAE is computed as the average difference between the actual values ($y_i$) and the observed values ($\hat{y}_i$) from the model using the following equation:

$$MAE = \frac{1}{n} \sum_{i=1}^{n} |y_i - \hat{y}_i| \qquad (1)$$

For the prediction models, MAE was calculated using Eq. 1, considering the delays from the test dataset as actual values in comparison with the predicted delays by the developed regression models as the observed values.

To assess the similarity between the inference mechanisms of the prediction and explanation models, the evaluation prediction has been carried out. This involves calculating the MAE by comparing the observed values of the explanation model predictions with the actual values obtained from the regression models. This metric will be termed as the *local accuracy* in the discussions presented in Sect. 4.

To assess the quality of the feature attribution from the explanation models with respect to the prediction models, Normalised Discounted Cumulative Gain (nDCG) scores were examined. By definition, the nDCG score compares the order of retrieved documents in information retrieval tasks [4, 17]. Here, nDCG scores were observed to compare the order of the features from the regression and explanation models in terms of their importance scores resulting in the corresponding predictions.

<table><caption>**Table 1.** Questionnaires to assess ATCOs' satisfaction and acceptance of the delay prediction XAI tool.</caption><thead><tr><th>Objective</th><th>Questionnaire</th></tr></thead><tbody><tr><td>Level of Understanding (Post Condition)</td><td><strong>Question 1:</strong> ATCOs' understand why the delay value (time) is influenced by the selected features<br><strong>Question 2:</strong> ATCOs' understand the contribution of each feature to the overall delay value (time)<br><strong>Question 3:</strong> ATCOs' understand why the tool selected the features based on their operational relevance<br><strong>Question 4:</strong> Having access to an explanation would increase ATCOs' accuracy in making an impact assessment in operations.</td></tr><tr><td>Overall Delay Prediction Outcome (Final Questionnaire)</td><td><strong>Question 5:</strong> ATCOs' find the information presented is clear and understandable<br><strong>Question 6:</strong> The unit in which information is presented is usable in operations<br><strong>Question 7:</strong> Knowing the features that influence the overall delay would help ATCOs' optimize runway use</td></tr></tbody></table>
**User Evaluation on the Satisfaction of Explanation.** The objective of the user evaluation was to assess the impact of different levels of explainability on controllers' acceptance. The hypothesis was: ATCOs self-reported acceptance would differ between the explanation methods and explanation levels. Hoffman et al. [10] suggested using a list of checklists through questionnaires to measure user satisfaction that addresses key attributes of explanation e.g., understandability, the sufficiency of detail, usefulness, etc. Therefore, to assess user satisfaction and acceptance of the explanation, the study utilized self-report questionnaires designed using a Likert scale [12]. The Likert scale used a five-level format ranging from "Strongly disagree" to "Strongly agree". Two categories of questionnaires were administered (see Table 1) - one after each condition i.e., explanations generated by SHAP, LIME, and DALEX, and one final questionnaire after all tasks were completed. The "post condition" questionnaire focused on understanding the AI outcome and the impact on work performance, while the final questionnaire evaluated the general usability and impact on work performance of a delay prediction tool.

## 3 Explaining Take-Off Delay Prediction

This section outlines the methodology we followed to answer the research question of this study. The methodology consists of three phases: Data Preparation, Model Development, and Validation. This section provides an overview of each phase and describes how they are related to each other. Figure 1 illustrates the phases of our research study.

<figure><img src="image_5.png" /><figcaption>**Fig. 1.** Experimental methodology of explaining flight TOT delay prediction.</figcaption></figure>

### 3.1 Phase 1: Data Preparation

The success of any machine learning model depends heavily on the quality and relevance of the dataset used for training. In this phase, we describe the steps taken data pre-processing and feature engineering to optimise the models' performances. During pre-processing, all the messages in the dataset with missing features and outliers were dropped to contain complete information of the flights. Considering the time of pre-tactical phase, i.e., 6 h [6], only those messages were considered which were received within the time interval from zero (inclusive)
to three-hundred and sixty minutes (exclusive): (0,360] from the Estimated Off-Block Time (EOBT). In summary, the dataset was analyzed to extract 42 features based on air traffic control experts' determination, as described in [6]. The target variable was calculated from the difference between the Actual Take-off Time (ATOT) and the Estimated Take-off Time (ETOT). The features are classified as categorical or numerical, and whether it changes (dynamic) or not (static) during the progress of the flight, from the individual flight plan (IFP) to the ATOT. The final dataset contained 7,613,584 instances for 609,202 flights flown by 18,214 distinct aircraft with an average of 12 flights per day and approximately 15 min of average take-off delay. Finally, to improve the prediction performance of the trained models, a split of the time series data was performed where the training set consisted of instances from May to August, while the test set included instances from September to October. This split was preferred over the more standard 80/20 training/testing split as it preserved the chronological order of the data (i.e., each row of data in the time series has a time dependency) and accounted for any seasonal patterns or trends in the time series, leading to more accurate predictions.

## 3.2 Phase 2: Development

In the second phase, the focus is on developing predictive and explainable models for the study. Once the data has been prepared in Phase 1, it is then processed in Phase 2 to implement two AI models and three explainability tools.

Both the models, RF and XGBoost were trained with the parameter values selected through a grid search 5-fold cross-validation with arbitrary value grids that were selected based on the experience from the previous works. In total, 144 different combinations of parameters were tested for each of the RF and XGBoost models. Finally, the RF was trained with the following hyper-parameters: `max_depth` = 7, `max_features` = 16 and `nstimators` = 500. And, the final XGBoost model was trained with the following hyperparameters: `learning_rate` = 0.1, `max_depth` = 7, `min_child_weight` = 1, `subsample` = 0.5, `colsample_bytree` = 0.5, `n_nestimators` = 500.

To generate explanations on the flight TOT delay prediction, three explainability tools, namely, SHAP, LIME and DALEX were used. Each of the two trained prediction models and the test dataset were fed into the tools to compute the individual contributions of each feature to the final prediction for each instance. To present the individual contributions of the features in a comprehensible manner to the users, breakdown plots were drawn. Figure 2c illustrates a breakdown plot that explains the prediction of 7.31 min. In the figure, the blue bar corresponds to the predicted delay. The red and green bars with corresponding values represent increase and decrease of delay time respectively.

## 3.3 Phase 3: Validation

The final phase of this methodology involves two validation scenarios: quantitative evaluation and user validation.
**Quantitative Evaluation.** The evaluation of the take-off delay prediction was performed from two perspectives: prediction models and explainability methods. To show the performance of our models (RF and XGBoost), two TOT delay prediction models reported in Dalmau's work [6], ETFMS and gradient-boosted decision tree (GBDT), were selected to comprehensively compare prediction models. The ETFMS was used as a benchmark for comparison as it is widely used in aviation for delay prediction. Besides, to investigate its potential in flight delay prediction, our aim was to explore the performance of the GBDT model, given its good results in other time series prediction tasks. Additionally, two explainability methods, LIME and SHAP, were used to explain the predictions of the XGBoost model based on the previous results of the model.

<figure><img src="image_6.png" /><figcaption>**Fig. 2.** An example of scenario presentation and associated prediction of delay with explanation for user validation.</figcaption></figure>

**User Evaluation.** In the user evaluation phase, a web platform was developed to conduct the evaluation of three XAI methods used to explain the prediction results to end users. The selected methods were LIME, SHAP, and DALEX. In the user validation exercise, a group of 9 Air Traffic Controllers (ATCOs) with varying levels of expertise participated. The participants, who were all male and aged between 30 and 60, had a minimum of 5 years of experience as ATCOs. For LIME and SHAP, each participant watched an introductory video and was presented with a scenario context with narrative (Fig. 2a) and illustration (Fig. 2b)
of a delayed flight. In addition, a prediction of the delay with explanation was
also presented (Fig. 2c) which varied based on the explainability tool used to
generate the explanation. The system based on LIME and SHAP then presented
a breakdown plot that contained the most important features contributing to
the delay. At the end of each scenario, the participant was asked to fill in ques-
tions 1 to 4 in the questionnaire presented in Table 1 to evaluate factors linked
to human performance. For DALEX, each participant watched an introductory
video and was presented with a scenario context of a delayed flight. They are
then asked to select five features from a list of features. Based on the chosen
features, the system presented a breakdown plot containing the five selected fea-
tures contributing to the delay. At the end of the scenario, participants were
asked to report the questionnaire (questions 1 to 4). Once all scenarios were
completed, the participants were asked to answer a final questionnaire ( ques-
tions 5 to 7) to evaluate the effectiveness of the XAI methods in explaining the
prediction results. Further, qualitative feedback was collected with open-ended
questions from the representative of ANACNA (Italian Air Traffic Controllers
Association).

# 4 Results and Discussion

## 4.1 Quantitative Evaluation

The quantitative evaluation of the delay prediction task was performed from two
perspectives: prediction models and explainability methods.

**Performance Comparison of RF and XGBoost Models for TOT Delay Prediction.** Here, the models were trained using flight data from May to August 2019 (5.9M instances) and tested on flight data from September to October 2019 (1.7M instances) to predict the TOT delay. Mean absolute error (MAE) values were used to assess the performance of the predictors. Both models performed better than previous similar work [6] in terms of MAE when predicting the takeoff delay on the whole test set. To assess performance in more detail, the dataset was sliced based on the time remaining in minutes until the EOBT. Table 2 lists the chunks with the prediction performance of existing ETFMS, GBDT [6], RF, and XGBoost. The table shows that RF and XGBoost performed better than previous models for all intervals, with XGBoost outperforming RF.

**Interpretation and Explanation of XGBoost Model's Decision-making Process Using LIME and SHAP.** Based on the outcome, XGBoost outperforms RF models, so we have only considered explanation models to explain the predictions of XGBoost only. As described in Sect. 2, LIME and SHAP were used to explain the predictions on the flight TOT delay made by the XGBoost model. These tools try to mimic the prediction of the trained models and determine the important features to explain the prediction. To evaluate the similarity between the predictions of the explanation tools and XGBoost, local accuracy
<table><caption>**Table 2.** Comparison of performances on take-off delay prediction from ETFMS, GBDT, RF and XGBoost using the MAE (in minutes), lower is better, and the lowest values are highlighted. The MAE values for the ETFMS and GBDT are considered as a reference from experimentation performed by Dalmau et al. [6].</caption><thead><tr><th>Time to EOBT</th><th>ETFMS [6]</th><th>GBDT [6]</th><th>RF</th><th>XGBoost</th></tr></thead><tbody><tr><td>(0, 15]</td><td>10.7</td><td>8.8</td><td><strong>7.22</strong></td><td>7.51</td></tr><tr><td>(15, 30]</td><td>12.4</td><td>10.2</td><td>8.75</td><td><strong>8.47</strong></td></tr><tr><td>(30, 60]</td><td>13.3</td><td>10.5</td><td><strong>9.05</strong></td><td><strong>9.05</strong></td></tr><tr><td>(60, 90]</td><td>14.3</td><td>10.8</td><td>9.46</td><td><strong>9.12</strong></td></tr><tr><td>(90, 120]</td><td>14.3</td><td>11.1</td><td>9.83</td><td><strong>9.50</strong></td></tr><tr><td>(120, 180]</td><td>19.1</td><td>13.5</td><td>11.09</td><td><strong>10.50</strong></td></tr><tr><td>(180, 240]</td><td>23.0</td><td>15.4</td><td>11.58</td><td><strong>11.46</strong></td></tr><tr><td>(240, 360]</td><td>21.2</td><td>15.1</td><td><strong>11.93</strong></td><td>12.02</td></tr></tbody></table>

was considered as MAE in minutes. The conditions of comparison for LIME and SHAP are consistent as both generate prediction model-centric explanations. Therefore, the results of their comparison are summarised in Table 3 for all instances, top 100k instances, and top 67k instances with the most accurate predictions, respectively. However, DALEX provides a user-centric explanation that is subjective and varies, which is different from LIME and SHAP. Due to this difference in the characteristics of the explanations, DALEX was excluded from the quantitative evaluation. Finally, based on the comparison, SHAP was used to generate visualisations to explain the predicted flight TOT delay prediction. Figure 2c illustrates an example of a visualisation explaining a single instance of flight TOT delay prediction.

<table><caption>**Table 3.** Comparison of local accuracy and nDCG values for SHAP and LIME. For local accuracy, lower is better, and for nDCG, higher is better. The best values are highlighted.</caption><thead><tr><th rowspan="2">XAI Model</th><th colspan="2">SHAP</th><th colspan="2">LIME</th></tr><tr><th>Local Accuracy</th><th>nDCG</th><th>Local Accuracy</th><th>nDCG</th></tr></thead><tbody><tr><td>All</td><td>3.3 × 10<sup>-6</sup></td><td>0.806</td><td>8.62</td><td><strong>0.882</strong></td></tr><tr><td>100k</td><td>1.1 × 10<sup>-6</sup></td><td>0.722</td><td>4.75</td><td><strong>0.847</strong></td></tr><tr><td>67k</td><td>6.2 × 10<sup>-7</sup></td><td>0.717</td><td>3.13</td><td><strong>0.800</strong></td></tr></tbody></table>

## 4.2 User Evaluation

The user evaluation of the delay prediction task followed Phase 3 as shown in Fig. 1.
**Post Condition Assessment.** The aim was to evaluate the impact of LIME, SHAP and DALEX on the understanding of the influence of selected features and their contribution to the final output, as well as their impact on work performance. The results showed that LIME and DALEX received the highest positive feedback for understanding the influence of selected features. Moreover, SHAP received no negative feedback, resulting in a more balanced condition for the understanding of the influence of the features on the final delay. In contrast, DALEX was the most effective in understanding the contribution of each feature to the final output. Therefore, we could say that the user-centric selection of the features can positively influence the understanding of the contribution to the final delay value. However, none of the conditions received positive feedback about understanding the selected features' operational relevance. This could mean that, even if the influence and the contribution on the final delay of the selected features for SHAP and LIME, and the contribution of the features selected by the users for DALEX, is high, this would not impact the operational relevance of the information received. The rationalization is reflected in the qualitative interview discussed in the Qualitative Feedback section. Additionally, the impact on work performance was generally positive for all three tools, with DALEX being the most useful for operations, confirming the slight preference expressed by the users in the last understanding item (question 4). These findings suggest that the user-centric selection of features can improve understanding of the contribution to the final output value. However, further efforts are needed to improve the understanding of the operational relevance of the selected features. The three methods are compared per question (see Fig. 3).

<figure><img src="image_7.png" /><figcaption>**Fig. 3.** ATCOs' response on the level of understanding of the explanation using LIME, SHAP, and DALEX.</figcaption></figure>
**Final Questionnaire.** The final questionnaire revealed that the unit's usability in which information was presented was generally rated positively, with no negative feedback on this first usability item, as shown in Fig. 4. The second usability item regarding the clarity and understandability of the information presented also received positive feedback. The question about the impact on work performance showed that only 22% of participants reported negative feedback about the usefulness of a delay prediction tool in optimising the use of a runway, regardless of their condition.

<figure><img src="image_8.png" /><figcaption>Fig. 4. Overall Delay Prediction outcomes.</figcaption></figure>

**Qualitative Feedback.** An Open-ended question interview revealed that most ATCOs found the validation exercise clear and understandable, but some users found some details and features unclear. The tool has the potential for several operational tasks, such as generating the best sequence of departures and optimizing runway usage, optimizing Target Start-up Approval Time (TSAT), Air Traffic Flow and Capacity Management (ATFCM) delay, and airport strategic planning. However, The representative from the ANACNA pointed out that improvements to the DALEX condition should be needed to reduce the variables involved in selecting features. Also, an important finding was that basic training on AI is required for ATCOs to understand such AI tools for better communication with the end user.

Overall, these results highlight the potential and limitations of XAI tools in the aviation industry and can inform the development of future AI tools for air traffic management. Extensive details on user validation can be found in the project's deliverable 6.2³.

<footnote n="3">³ https://doi.org/10.5281/zenodo.7486982.</footnote>
# 5 Conclusions

This study investigates ML and post hoc XAI methods to provide explanations of TOT delay prediction to the ATCOs, and presents a comparative analysis regarding usability and acceptance of the explanation offered by these XAI methods in the ATM domain. Here, we have compared the XGBoost and RF models as prediction models, along with their explanations provided by LIME, SHAP, and DALEX. The results indicate that XGBoost outperforms RF regarding MAE values for predicting TOT delays. Besides, XGBoost is more scalable, faster, and better at optimizing errors than RF and GBDT from an algorithmic perspective. In addition, the study finds that SHAP is more effective than LIME in explaining the predictions of the XGBoost model. SHAP presents better results when considering the nDCG values. Overall, the study highlights the advantages of XGBoost as an ML model and SHAP as an explainability method for predicting TOT delays in the ATM domain.

In terms of user evaluation, three XAI tools (SHAP, LIME, and DALEX) were evaluated for explaining the decisions of the XGBoost model to human operators. The results indicate that DALEX is more usable and preferred by human operators than SHAP and LIME. Feedback from the ATCOs showed that the information presented by the tool was clear and understandable, attributed to the videos and narration, which communicated the content intelligibly. However, the impact of the features on the estimated delay and take-off time was not self-explanatory for all ATCOs, and the level of clarity was an issue due to the synchronisation of video, text, and colour. Most ATCOs suggested that the information provided by the tool would allow them to generate the best sequence of departures, optimising runway usage, increasing the airside capacity of the airport, and reducing runway occupation time.

In conclusion, the study demonstrates the potential of XAI in improving the transparency and interpretability of machine learning models in the ATM domain, particularly in predicting flight TOT delays. The DALEX tool was found to be more effective and usable for explaining the decisions of the XGBoost model to human operators. Further research is needed to identify the most relevant features that should be shown event by event to the ATCOs.

**Acknowledgements.** This work was financed by the European Union's Horizon 2020 within the framework SESAR 2020 research and innovation program under grant agreement N. 894238, project Transparent Artificial Intelligence and Automation to Air Traffic Management Systems (ARTIMATION) and BrainSafeDrive, co-funded by the Vetenskapsrådet - The Swedish Research Council and the Ministero dell'Istruzione dell'Università e della Ricerca della Repubblica Italiana under Italy-Sweden Cooperation Program.
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