This study examines dataset annotation practices in influential NeurIPS research. Datasets employed in highly cited NeurIPS papers were assessed based on criteria concerning their item population, labelling schema, and annotation process. While high-level information, such as the presence of human labellers and item population, is present in most cases, procedural details of the annotation process are poorly reported. Notably, 48% of datasets lack details on annotator training, 43% omit inter-rater reliability, and 28% are not publicly accessible. Temporal comparisons show minor improvements, but no substantial progress in reporting annotation methodology. A complementary analysis of 49 NeurIPS papers published since 2020 shows that researchers often discuss the broader impact of their work, yet do not include datasets or their annotations in these assessments. These findings highlight a lack of standardisation in annotation reporting and call for more robust practices that ensure transparency, auditability, and reproducibility in machine learning research.

With the worsening of climate change, the complications brought on by floods every year create an increasing need for forecasting systems that humanitarian organizations can use to help populations in danger. This research presents a literature review of machine-learning models for impact-based flood forecasting, and compares them with existing humanitarian projects. The results examine the characteristics of the models surveyed, while the discussion focuses on understanding how these characteristics can define whether the machine learning models proposed can actually be translated to humanitarian settings. The main takeaways include the prevalent choice of deep learning and ensemble models, used to improve the adaptability of the models, the problems with data availability and data quality in different areas considered, and the difference between lead times, usability, and scalability of the models proposed in contrast with already used humanitarian projects. This study then highlights the importance of transparency and reproducibility of the survey by detailing the queries and databases used, ensuring accessibility of selected articles, and explaining the selection criteria and methodology. Ultimately, the review concludes with the key insights on the connection between academic prototypes and real-life humanitarian projects, as well as key areas for future research.

Natural disasters frequently cause casualties and property losses. Predicting and mitigating the impact of such threats is crucial to the work of humanitarian organizations. The interactions between hazards are best represented through a multi-hazard approach, and machine learning models are well suited for natural hazard prediction. This study presents a systematized literature survey of machine learning in multi-hazard disaster forecasting in the years 2019-2025, focusing on the used models and performance metrics, their applications and feasibility of use, as well as potential cross-applications. There is a wide variety of models and metrics used. The most commonly used models are random forest and support vector machine and the most prevalent performance metric is the ROC-AUC score. The machine learning models generally perform well, with AUC scores above 0.8, though patterns in performance are difficult to examine. Feasibility is defined here as readiness to be used in practice, and the models are rated in the factors that define it. Most of the articles are feasible. Consideration of cross-application is rare and should be extended. This research summarizes the main trends in the field of disaster forecasting, providing a clear reference point for other academics.

The output of machine learning (ML) models can be only as good as the data that is fed into them. Because of this, when making datasets for creating ML models, it is important to ensure the quality of the data. This is especially true of human labeled data, which can be hard to standardize and assess the quality of. To assess the annotation practices of human labeled data in the field of machine learning, this paper investigates the datasets used in the highest cited papers in the AAAI Conference on Artificial Intelligence, an influential machine learning conference. After extracting the datasets from 75 papers in three overlapping publication periods, the top 20 datasets were evaluated from each period. The results showed that the majority of datasets do not use or underreport significant annotation practices, specifically about the annotators and the annotation process. This raises concern for the conference and the field more broadly, as the most influential papers build their machine learning algorithms on quite possibly low quality data. However, there is some hope for the field in this regard as the more recent papers use datasets with better quality annotation practices.

Displacement is a focal point of humanitarian aid efforts, since it affects millions of people globally. Mitigating the consequences of forced migration is important for reducing suffering and one way of doing so is through predicting displacement to prioritise resources in advance. To achieve this, machine learning can be used for its ability to analyse larger amounts of data and identify latent structures more efficiently than human experts. Through a systematized literature review, this research evaluates thoroughly six machine learning tools: UNHCR's Jetson, DRC's Foresight and AHEAD, the EU's EUMigraTool and EPS-Forecasting, and the agent-based simulation Flee, in order to assess their suitability to that end. The analysis compares these tools across several criteria, including the way they use data, algorithmic characteristics, and operational use cases. Finally, it makes recommendations about what should be considered and how to choose amongst the tools for displacement prediction.

As humanitarian needs increase while donor budgets decrease, anticipatory strategies are essential for effective crisis response. In this context, machine learning (ML) has emerged as a promising tool for crisis forecasting, offering the potential to support timely interventions and humanitarian decision-making. However, despite rapid developments in ML-based prediction models, questions remain about their practical utility and trustworthiness in real-world humanitarian settings. This study presents a systematic scoping review of 32 academic and gray literature sources to assess the reliability and feasibility of ML systems for conflict forecasting. By analyzing these systems across dimensions such as forecasting scope, data sources, modeling approaches, validation practices, and ethical considerations, the study finds that while some models demonstrate strong predictive performance and methodological rigor, many lack transparent validation, robust error analysis, and operational applicability. The review concludes that while ML systems hold substantial potential for enhancing conflict anticipation, their current real-world readiness is uneven and context-dependent.

This study investigates annotation and reporting practices in machine learning (ML) research, focusing on societally impactful applications presented at the IEEE/CVF Computer Vision and Pattern Recognition (CVPR) conferences. By structurally analyzing the 75 most-cited CVPR papers from the past 2, 5, and 15 years, we evaluate how the human annotations foundation of supervised ML is documented. We introduce a 27-field annotation-reporting schema and apply it to 60 datasets, revealing that nearly 30% of relevant information is routinely omitted. Key findings include the pervasive underreporting of annotator details such as training, prescreening, and inter-rater reliability (IRR) metrics. While popular datasets like COCO and ImageNet exhibit widespread use, transparency about annotation methodologies remains inconsistent. The impact of a few fields shows that basic metadata, such as the selection process of annotators and how the labels' overlap is managed, strongly anticipate overall documentation quality. Our findings support previous calls for standardization and underscore the need for institutionalized reporting practices to ensure reproducibility, fairness, and trust in ML systems.

High-impact vision research still rests on datasets whose labels arrive via opaque, rarely documented pipelines. To understand how serious the problem is inside a large venue, we audited 75 TPAMI papers (2009-2024) that rely or introduce datasets. Each dataset was coded against a 27-item checklist adapted from Garbage in, Garbage out, spanning annotator recruitment, training, compensation, overlap-resolution and more. Across the corpus, 37% of the expected annotation metadata is missing; the rate changes little between recent (2022-24) and older cohorts. The scarcest fields are labeller-population rationale (76.6% absent), prescreening criteria (73.4%), total annotators (68.8%), compensation (67.2%) and training procedures (62.5%). Documentation quality shows virtually no correlation with a paper’s citation impact, suggesting community prestige does not buy transparency. A handful of well—curated datasets achieve >75% completeness, proving that thorough documentation is possible when incentives align. The median TPAMI benchmark still ships with an unverifiable "ground truth", threatening the reproducibility and fairness claims of downstream models. We advocate that journals and conferences require a concise, checklist-based annotation statement, mirroring existing ethics and reproducibility forms, to ensure future vision systems are built (and evaluated) on transparent, trustworthy data foundations.

This study gives an overview of the data collection and annotation practices of the datasets used by the most impactful papers published by the Association of Computational Linguistics (ACL). This was achieved by selecting the most highly cited papers published within the ACL anthology across 3 periods (published in the past 2, 5 and 15 years). Afterwards, the datasets used by those papers were extracted and filtered to retain the most impactful ones. Finally, a carefully crafted annotation schema was used to find out information regarding key aspects of the datasets in order to qualitatively analyze them. As a result of this analysis, it was first found that (1) there are fewer datasets used on average in the past 2 years and that there is little overlap with the datasets used by papers published in the past 5 or 15 years. (2) Secondly, there are various concerns related to those key aspects, such as the relatively high (∼36%) and unregulated use of the Amazon Mechanical Turk crowdsourcing platform for the construction of datasets. Another concern is information frequently missing about any rationale regarding labeller population, prescreening, inter-rater reliability and rationale regarding sample size - missing ∼77%, ∼63%, ∼19-56%, and ∼81% of the time. However, reporting practices for most of those issues have slightly improved within datasets used in the past 2 years. (3) Finally, around one third of the information sought was missing across all periods. However, the state of the domain has been generally improving, with a lower one fourth of the information missing from datasets used in the past 2 years. Some recommendations are given in order to overcome those challenges, the most important of which being that each academic organization should require their submissions to include a reporting template in their papers.

In musical (jazz) improvisation, musicians that are just starting out can often feel uncomfortable when being put on the spot by their fellow players. However, when a musician is on their own when practising or leisurely playing, this prevents them from listening to fellow musicians. When a musician wants to experience some notion of co-play when they are on their own, computers and musical generative techniques may be a source of help. We study the extent to which music improvisation algorithms can facilitate such interactions by proposing an experimental framework to evaluate and compare these different algorithms. We achieve this by developing MILES ('Mixed-Initiative musicaL interactivE System'), a generic music improvisation system that allows a musician to improvise with various musical improvisation models and facilitates comparative evaluation of these models. MILES makes use of the 'trading fours' paradigm, where two or more musicians exchange four measures of solo material. We conduct experiments with novice and advanced musicians in expert-pupil and peer-to-peer settings that compare differing algorithms, as well as different variations of similar algorithms. These comparisons are based on self-assessed opinions and third-party grading and ordering of recordings, based on improvisational reciprocity and enjoyment. Symbolic music recording analysis further quantifies the interactivity between the musician and the algorithms. With this experimental setup, we are able to track familiarity and enjoyment of using music improvisation algorithms, and compare different iterations of similar music improvisation algorithms.

Machine Learning (ML) systems are increasingly used in high-stakes, socially impactful domains, requiring attention to improve explainability and trust. However, current Requirements Engineering (RE) techniques often fail to address these human-centered qualities. This research investigates how Behavior-Driven Development (BDD) and Goal-Oriented Requirements Engineering (GORE) can improve the identification and visualization of requirements for explainability and trust. We conducted expert interviews and a survey to see how different stakeholders rate certain BDD scenarios and what they think of conceptual GORE models. Our results show that participants value concise, human-readable BDD scenarios and particularly like the GORE framework of i* to understand stakeholder relationships and system behaviors. The other GORE framework, GR4ML, was found to align more with business goals and addresses other stakeholder perspectives less. We conclude that BDD and GORE can improve explainability in ML system development. Future work should refine modeling tools to better integrate ethical and fairness considerations.

Central banks communicate their monetary policy plans to the public through meeting minutes or transcripts. These communications can have immense effects on markets and are often the subjects of studies in the financial literature. The recent advancements in Natural Language Processing have prompted researchers to analyze these communications using Transformer-based Large Language Model (LLM) classifiers. The use of LLMs in finance and other high-stakes domains calls for a high level of trustworthiness and explainability of those models. We focus on Counterfactual Explanations, a form of Explainable AI that explains a model's classification by proposing an alternative to the original input. We use three types of CE generators for LLM classifiers on a recent dataset consisting of sentences taken from FOMC communications to assess the usability of their explanations. We perform three experiments comparing different types of generators, one using a selection of quantitative metrics and two involving human evaluators, including central bank employees. Our findings suggest that non-expert and expert evaluators prefer counterfactual methods that apply minimal changes to the texts; however, the methods we analyze might not handle the domain-specific vocabulary well enough to generate plausible explanations for our task. We discuss shortcomings in the choice of evaluation metrics in the literature on text CE generators and propose refined definitions of the fluency and plausibility qualitative metrics.

Large language models have achieved breakthroughs in many natural language processing tasks. One of their main appeals is the ability to tackle problems that lack sufficient training data to create a dedicated solution. Manga translation is one such task, a still budding and underdeveloped field, that at the same time deals with a problem even more complex than standard translation. One of its biggest challenges is successfully incorporating the visual modality in translation, to resolve ambiguities. Recently, a new type of LLMs -- multimodal LLMs -- has emerged and showed potential in understanding visual symbolism in narrative pieces like memes and comics. In this work, we investigate whether these models could be useful in the field of manga translation, ultimately helping manga authors and publishers make their works available to wider audiences. Specifically, we evaluate a number of methods based on GPT-4-Turbo for text only translation, image informed translation and volume-level translation. We perform both automatic and human evaluation. Moreover, we contribute new evaluation data -- the first parallel Japanese-Polish manga translation benchmark. Our findings show that our proposed methods are able to achieve state of the art results for English, and set a new standard for Polish. We conclude that while this is not a sufficient replacement for a professional human translator, it could help speed up the translation process or be used as a learning aid.

Counterfactual explanations can be applied to algorithmic recourse, which is concerned with helping individuals in the real world overturn undesirable algorithmic decisions. They aim to provide explanations to opaque machine learning models. Not all generated points are equally faithful to the model, nor equally plausible. On the other hand, predictive uncertainty quantification is used to measure the degree of certainty a model has in its predictions. Previously, it has been shown that it is possible to generate more plausible counterfactual explanations utilising predictive uncertainty. This work investigates this further by using multiple models innately supporting uncertainty quantification and comparing the produced counterfactual explanations to those produced by the models' ordinary counter-part. Predictive uncertainty tends to enhance the plausibility of the counterfactuals on visual datasets. Furthermore, we are positive that predictive uncertainty correlates proportionally with plausibility. This correlation has important implications for both research and real-world applications, as it suggests that integrating uncertainty quantification in model development can improve the quality and trustworthiness of algorithmic explanations.

Adversarial Training has emerged as the most reliable technique to make neural networks robust to gradient-based adversarial perturbations on input data. Besides improving model robustness, preliminary evidence presents an interesting consequence of adversarial training -- increased explainability of model behaviour. Prior work has explored the effects of adversarial training on gradient stability and interpretability, as well as visual explainability of counterfactuals. Our work presents the first quantitative, empirical analysis of the impact of model robustness on model explainability by comparing the plausibility of faithful counterfactuals for both robust and standard networks. We seek to determine whether robust networks learn more plausible decision boundaries and representations of the data than regular models, and whether the strength of the adversary used to train robust models affects their explainability. Our finidngs indicate that robust networks for image data learn more explainable decision boundaries and representations of data than regular models, with more robust models producing more plausible counterfactuals. Robust models for tabular data, however, only conclusively exhibit this phenomenon along decision boundaries and not for its overall data representations, possibly due to its high robustness-accuracy trade-off and the difficulties associated with traditional adversarial training due to its innate properties. We believe our work can help guide future research towards improving the robustness of machine learning models keeping their explainability in mind.

In recent years, the need for explainable artificial intelligence (XAI) has become increasingly important as complex black-box models are used in critical applications. While many methods have been developed to interpret these models, there is also potential in enhancing the models themselves to improve their inherent explainability. This paper investigates various techniques aimed at improving the explainability of black-box models. Through a systematic literature review, these techniques are categorized, and their impact on predictive uncertainty, adversarial robustness, and generative capacity is analyzed to understand how these factors contribute to the overall explainability. The snowballing methodology is used for the systematic literature review, starting with papers retrieved from four databases: IEEExplore, Scopus, ArXiv, and the ACM Digital Library to form the initial set. This process continued with backward and forward snowballing through four iterations, resulting in a total of 50 papers reviewed. Only papers focused on improving model explainability are included in the review. Due to time limitations, additional search constraints are applied for feasibility. The initial set of papers is filtered to those published since 2013. These constraints and their possible impacts are considered when interpreting the results. Findings reveal that techniques such as Bayesian approaches and variational inference, adversarial robustness, model compression and distillation, uncertainty and ensembles, regularization, self-explaining models, and hybrid techniques are used for advancing model explainability. The paper concludes with a discussion on the implications of these techniques for future research.

Counterfactual explanations (CEs) can be used to gain useful insights into the behaviour of opaque classification models, allowing users to make an informed decision when trusting such systems. Assuming the CEs of a model are faithful (they well represent the inner workings of the model), an explainable model generates plausible CEs (i.e. CEs fitting the real-world distribution of the data). This raises the question of whether classifiers explicitly designed to model the distribution of the data, such as energy-based models, are inherently more explainable. This work focuses on the evaluation of joint energy-based models (JEMs) in combination with the Energy-Constrained Conformal Counterfactuals (ECCCo) generator, with the goal of identifying if the generative capability of a model influences its explainability. Since ECCCo has been designed specifically to generate more faithful CEs, it makes it possible to use the CEs plausibility as a proxy of the model explainability. Two experiments have been performed to evaluate the effect of variations of generative capability within the same JEM architecture and the difference between JEMs and classically trained classifiers. Despite the experiments not having established a clear correlation between generative capability and explainability of a model, various research avenues are still open to explore in future works

Counterfactual Explanations (CE) are essential for understanding the predictions of black-box models by suggesting minimal changes to input features that would alter the output. Despite their importance in Explainable AI (XAI), there is a lack of standardized metrics to assess the plausibility and faithfulness of these explanations. This paper reviews evaluation procedures in literature and proposes novel formal metrics for evaluating the plausibility and faithfulness of counterfactual explanations, addressing the existing limitations. Plausibility is defined as the coherence of explanations with the true data-generating process, while faithfulness refers to the accuracy of explanations in representing the model's reasoning. We discuss the shortcomings of existing evaluation procedures and metrics for measuring plausibility and faithfulness and consequently compare our proposed metrics with existing ones, highlighting their advantages and disadvantages. The proposed metrics are then empirically validated through experiments across multiple models and datasets, demonstrating their model-agnostic nature and reliability. Our findings indicate that the proposed metrics provide a correct and reliable means to quantify the plausibility and faithfulness of counterfactual explanations, thereby allowing one to gauge their feasibility and trustworthiness consistently.

Counterfactual explanations (CEs) are emerging as a crucial tool in Explainable AI (XAI) for understanding model decisions. This research investigates the impact of various factors on the quality of CEs generated for classification tasks. We explore how inter-class distance, data imbalance, balancing techniques, the presence of biased classifiers, and decision thresholds influence CE quality. To answer these research questions, we conduct experiments on various datasets, classification models and counterfactual generators. The datasets include the MNIST and GMSC dataset. The models include well-established models like MLP and Random Forest, along with the novel NeuroTree model. The generators include the method proposed by Wachter et al. and the REVISE method. We evaluate how different factors affect CE quality by performing an extensive experimental analysis. Our findings demonstrate that increasing inter-class distance degrades CE quality, particularly explanation plausibility. Data imbalance showed minimal impact, while balancing techniques yielded a slight improvement in CE plausibility, especially for the minority class. Classifiers biased towards specific subgroups resulted in lower CE quality for those subgroups. We observed limited evidence for a consistent amplification effect of decision thresholds. This research utilizes various datasets and classification models, including the novel NeuroTree model. Our findings contribute to XAI by providing insights into factors affecting CE quality and highlighting areas for future development, particularly regarding fairness and handling imbalanced data.

Due to climate change, man-made conflicts, and rising inflation, a growing number of people around the world are struggling to have consistent access to safe and nutritious food. This phenomenon is known as food insecurity (FI). Therefore, we take in this thesis the first steps towards developing a monitoring process for assessing FI using Human-Centered AI (HCAI). We developed this process for, and in collaboration with, the Integrated Food Security Phase Classification (IPC). The IPC is an organization that helps countries classify levels of food insecurity in their regions to inform humanitarian decision-making. During our research process, we found that any form of HCAI for the IPC would need to be informed by input from their domain experts, and we concluded that we could not start implementing HCAI until we found a way to formalize their input in a way that was robust and suited their technical capabilities. To this end, we ran an experiment with 18 IPC experts in Malawi to see whether they could quantify their assumptions by setting thresholds for food security drivers. The results are encouraging but show that there is still much to be done to bridge the gap between domain knowledge and technical expertise. We also show in this thesis that there is a lack of real-life case studies on HCAI development and share therefore our lessons learned from our real-world HCAI case study on FI monitoring. In this way, we hope to promote the development of a practice-informed methodology for HCAI.