The floods caused by the Isolated High-Level Depression (DANA) in the Valencian Community in October 2024 destroyed and damaged hundreds of thousands of personal photographs, erasing key pieces of collective and emotional memory. Within the project Recuperar las Memorias, we present an AI-based system for automated photo reconstruction, designed to support the recovery of more than 200,000 affected images. The system integrates YOLOv8 and SAM2 for automatic detection of damaged regions, followed by context-aware inpainting to restore visual coherence. Special modules are included for facial restoration, preserving identity in one of the most emotionally critical aspects of personal photographs. The tool is deployed as a web application that enables both single-image and batch restoration, making it accessible to non-expert users. Preliminary evaluation, combining human perceptual studies and automatic metrics (LPIPS), shows consistent alignment between subjective and objective assessments of quality. This demonstration highlights how advances in computer vision can be mobilised in real-world crisis contexts, placing AI at the service of cultural heritage, dignity, and memory preservation.

Many areas of artificial intelligence, and machine learning in particular, aim at being probably correct, i.e., valid on average, rather than pursuing the idealistic goal of being provably valid for all inputs. However, AI systems could still be predictably valid, such as an imperfect robot deliverer for which we can reliably and precisely predict the task instances for which it is correct and safe, its valid operating range. “Predictable AI” is a nascent research area that explores ways of anticipating key validity indicators (e.g., performance, safety) of present and future AI ecosystems. We argue that achieving predictability is crucial for fostering trust, liability, control, alignment and safety of AI, and thus should be prioritised over performance. We formally characterise predictability, explore its most relevant components, illustrate what can be predicted, describe alternative candidates for predictors, as well as the trade-offs between maximising validity and predictability. To illustrate these concepts, we bring an array of illustrative examples covering diverse ecosystem configurations. “Predictable AI” is related to other areas of technical and non-technical AI research, but have distinctive questions, hypotheses, techniques and challenges. This paper aims to elucidate them, calls for identifying paths towards a landscape of predictably valid AI systems and outlines the potential impact of this emergent field.

Abstract In traditional machine teaching, a teacher needs to teach a concept to a learner by means of a finite set of examples, the witness set. But concepts can have many equivalent representations. This redundancy strongly affects the search space, to the extent that teacher and learner may not be able to easily determine the equivalence class of each representation. In this common situation, instead of teaching concepts, we explore the idea of teaching representations. We work with several teaching schemas that exploit representation and witness size (Eager, Greedy and Optimal) and analyze the gains in teaching effectiveness, both theoretically, and also experimentally for languages where redundancy can vary (DNF expressions and Turing-complete P3 programs). Our theoretical and experimental results indicate that there are various types of redundancy, related, e.g,. to the spread of the redundant representations, handled better by the new Greedy schema introduced here than by the Eager schema. For P3 programs witness sets found by Greedy are usually smaller than the programs they identify, corroborating previous results that conveying information efficiently is a leitmotif of machine teaching.

Microplastics and microfibres are now widespread in aquatic ecosystems, as oceans and rivers. A serious portion of these microplastics come from urban wastewater treatment plants. Traditional methods for detecting and quantifying them are labour-intensive and time-consuming. This paper introduces MicroFiberDetect, a novel application designed to enhance the detection and quantification of microfibres within sludge samples. Leveraging the power of deep learning, this innovative tool provides detection accuracy and insights into the size and colour of each identified fibre. Reducing time and manpower required for analysis while increasing accuracy and throughput. The application has been deployed as a desktop application that allows field experts to quantify and analyse microfibres in sludge samples.

Large language models have become multimodal, and many of them are said to integrate their modalities using common representations. If this were true, a drawing of a car as an image, for instance, should map to a similar area in the latent space as a textual description of the strokes that form the drawing. To explore this in a black-box access regime to these models, we propose the use of machine teaching, a theory that studies the minimal set of examples a teacher needs to choose so that the learner captures the concept. In this paper, we evaluate the complexity of teaching vision-language models a subset of objects in the Quick, Draw! dataset using two presentations: raw images as bitmaps and trace coordinates in TikZ format. The results indicate that image-based representations generally require fewer segments and achieve higher accuracy than coordinate-based representations. But, surprisingly, the teaching size usually ranks concepts similarly across both modalities, even when controlling for (a human proxy of) concept priors, suggesting that the simplicity of concepts may be an inherent property that transcends modality representations.

The quest for transparency in black-box models has gained significant momentum in recent years. In particular, discovering the underlying machine learning technique type (or model family) from the performance of a black-box model is a real important problem both for better understanding its behaviour and for developing strategies to attack it by exploiting the weaknesses intrinsic to the learning technique. In this paper, we tackle the challenging task of identifying which kind of machine learning model is behind the predictions when we interact with a black-box model. Our innovative method involves systematically querying a black-box model (oracle) to label an artificially generated dataset, which is then used to train different surrogate models using machine learning techniques from different families (each one trying to partially approximate the oracle’s behaviour). We present two approaches based on similarity measures, one selecting the most similar family and the other using a conveniently constructed meta-model. In both cases, we use both crisp and soft classifiers and their corresponding similarity metrics. By experimentally comparing all these methods, we gain valuable insights into the explanatory and predictive capabilities of our model family concept. This provides a deeper understanding of the black-box models and increases their transparency and interpretability, paving the way for more effective decision making.

Large Language Models (LLMs) have become the state-of-the-art technology in a variety of language understanding tasks. Accordingly, many commercial organizations have been increasingly trying to integrate LLMs in multiple areas of their production and analytics. A typical scenario is the need for answering questions over a domain-specific, private collection of documents, such that the answer is supported by evidence clearly referenced from those documents. The Retrieval-Augmented Generation (RAG) framework has been recently used by many applications for this kind of scenarios, as it intuitively bridges dedicated data collections and state-of-the-art generative models. Yet, LLMs are known to present data contamination, a phenomenon in which their performance on evaluation data relevant to a task is influenced by said data being already incorporated to the LLM during training phase. In this paper, we assess the performance of LLMs within the domain of Equinor, the largest energy company in Norway. Specifically, we address question answering with a RAG-based approach over a novel data collection not available for well-established LLMs during training, in order to study the effect of data contamination for this task. Beyond shedding light on LLM performance for a highly-demanded, realistic industrial scenario, we also analyze its potential impact for an ensemble of personas in Equinor with particular information needs and contexts.

Abstract The automation of data science and other data manipulation processes depend on the integration and formatting of ‘messy’ data. Data wrangling is an umbrella term for these tedious and time-consuming tasks. Tasks such as transforming dates, units or names expressed in different formats have been challenging for machine learning because (1) users expect to solve them with short cues or few examples, and (2) the problems depend heavily on domain knowledge. Interestingly, large language models today (1) can infer from very few examples or even a short clue in natural language, and (2) can integrate vast amounts of domain knowledge. It is then an important research question to analyse whether language models are a promising approach for data wrangling, especially as their capabilities continue growing. In this paper we apply different variants of the language model Generative Pre-trained Transformer (GPT) to five batteries covering a wide range of data wrangling problems. We compare the effect of prompts and few-shot regimes on their results and how they compare with specialised data wrangling systems and other tools. Our major finding is that they appear as a powerful tool for a wide range of data wrangling tasks. We provide some guidelines about how they can be integrated into data processing pipelines, provided the users can take advantage of their flexibility and the diversity of tasks to be addressed. However, reliability is still an important issue to overcome.

Over the past decades in the field of machine teaching, several restrictions have been introduced to avoid ‘cheating’, such as collusion-free or non-clashing teaching. However, these restrictions forbid several teaching situations that we intuitively consider natural and fair, especially those ‘changes of mind’ of the learner as more evidence is given, affecting the likelihood of concepts and ultimately their posteriors. Under a new generalised probabilistic teaching, not only do these non-cheating constraints look too narrow but we also show that the most relevant machine teaching models are particular cases of this framework: the consistency graph between concepts and elements simply becomes a joint probability distribution. We show a simple procedure that builds the witness joint distribution from the ground joint distribution. We prove a chain of relations, also with a theoretical lower bound, on the teaching dimension of the old and new models. Overall, this new setting is more general than the traditional machine teaching models, yet at the same time more intuitively capturing a less abrupt notion of non-cheating teaching.

Large language models have recently shown a remarkable ability for few-shot learning, including patterns of algorithmic nature. However, it is still an open question to determine what kind of patterns these models can capture and how many examples they need in their prompts. We frame this question as a teaching problem with strong priors, and study whether language models can identify simple algorithmic concepts from small witness sets. In particular, we explore how several GPT architectures, program induction systems and humans perform in terms of the complexity of the concept and the number of additional examples, and how much their behaviour differs. This first joint analysis of language models and machine teaching can address key questions for artificial intelligence and machine learning, such as whether some strong priors, and Occam’s razor in particular, can be distilled from data, making learning from a few examples possible.

Nowadays, there is an increasing concern in machine learning about the causes underlying unfair decision making, that is, algorithmic decisions discriminating some groups over others, especially with groups that are defined over protected attributes, such as gender, race and nationality. Missing values are one frequent manifestation of all these latent causes: protected groups are more reluctant to give information that could be used against them, sensitive information for some groups can be erased by human operators, or data acquisition may simply be less complete and systematic for minority groups. However, most recent techniques, libraries and experimental results dealing with fairness in machine learning have simply ignored missing data. In this paper, we present the first comprehensive analysis of the relation between missing values and algorithmic fairness for machine learning: (1) we analyse the sources of missing data and bias, mapping the common causes, (2) we find that rows containing missing values are usually fairer than the rest, which should discourage the consideration of missing values as the uncomfortable ugly data that different techniques and libraries for handling algorithmic bias get rid of at the first occasion, (3) we study the trade-off between performance and fairness when the rows with missing values are used (either because the technique deals with them directly or by imputation methods), and (4) we show that the sensitivity of six different machine-learning techniques to missing values is usually low, which reinforces the view that the rows with missing data contribute more to fairness through the other, nonmissing, attributes. We end the paper with a series of recommended procedures about what to do with missing data when aiming for fair decision making.

Abstract Matrices are a very common way of representing and working with data in data science and artificial intelligence. Writing a small snippet of code to make a simple matrix transformation is frequently frustrating, especially for those people without an extensive programming expertise. We present AUTOMATIX, a system that is able to induce R program snippets from a single (and possibly partial) matrix transformation example provided by the user. Our learning algorithm is able to induce the correct matrix pipeline snippet by composing primitives from a library. Because of the intractable search space—exponential on the size of the library and the number of primitives to be combined in the snippet, we speed up the process with (1) a typed system that excludes all combinations of primitives with inconsistent mapping between input and output matrix dimensions, and (2) a probabilistic model to estimate the probability of each sequence of primitives from their frequency of use and a text hint provided by the user. We validate AUTOMATIX with a set of real programming queries involving matrices from Stack Overflow, showing that we can learn the transformations efficiently, from just one partial example.

CRISP-DM(CRoss-Industry Standard Process for Data Mining) has its origins in the second half of the nineties and is thus about two decades old. According to many surveys and user polls it is still the de facto standard for developing data mining and knowledge discovery projects. However, undoubtedly the field has moved on considerably in twenty years, with data science now the leading term being favoured over data mining. In this paper we investigate whether, and in what contexts, CRISP-DM is still fit for purpose for data science projects. We argue that if the project is goal-directed and process-driven the process model view still largely holds. On the other hand, when data science projects become more exploratory the paths that the project can take become more varied, and a more flexible model is called for. We suggest what the outlines of such a trajectory-based model might look like and how it can be used to categorise data science projects (goal-directed, exploratory or data management). We examine seven real-life exemplars where exploratory activities play an important role and compare them against 51 use cases extracted from the NIST Big Data Public Working Group. We anticipate this categorisation can help project planning in terms of time and cost characteristics.

One classical way of characterising the rich range of machine learning techniques is by defining 'families', according to their formulation and learning strategy (e.g., neural networks, Bayesian methods, etc.).However, this taxonomy of learning techniques does not consider the extent to which models built with techniques from the same or different family agree on their outputs, especially when their predictions have to extrapolate in sparse zones where insufficient training data was available.In this paper we present a new taxonomy of machine learning techniques for classification, where families are clustered according to their degree of (dis)agreement in behaviour considering both dense and sparse zones, using Cohen's kappa statistic.To this end, we use a representative collection of datasets and learning techniques.We finally validate the taxonomy by performing a number of experiments for technique selection.We show that ranking techniques by only following prejudice -the reputation they have for other problems-is worse than selecting techniques based on family diversity.

The quality of the decisions made by a machine learning model depends on the data and the operating conditions during deployment. Often, operating conditions such as class distribution and misclassification costs have changed during the time since the model was trained and evaluated. When deploying a binary classifier that outputs scores, once we know the new class distribution and the new cost ratio between false positives and false negatives, there are several methods in the literature to help us choose an appropriate threshold for the classifier’s scores. However, on many occasions, the information that we have about this operating condition is uncertain . Previous work has considered ranges or distributions of operating conditions during deployment, with expected costs being calculated for ranges or intervals, but still the decision for each point is made as if the operating condition were certain. The implications of this assumption have received limited attention: a threshold choice that is best suited without uncertainty may be suboptimal under uncertainty. In this paper we analyse the effect of operating condition uncertainty on the expected loss for different threshold choice methods, both theoretically and experimentally. We model uncertainty as a second conditional distribution over the actual operation condition and study it theoretically in such a way that minimum and maximum uncertainty are both seen as special cases of this general formulation. This is complemented by a thorough experimental analysis investigating how different learning algorithms behave for a range of datasets according to the threshold choice method and the uncertainty level.

A more effective vision of machine learning systems entails tools that are able to improve task after task and to reuse the patterns and knowledge that are acquired previously for future tasks. This incremental, long-life view of machine learning goes beyond most of state-of-the-art machine learning techniques that learn throw-away models. In this paper we present a long-life knowledge acquisition, evaluation and consolidation framework that is designed to work with any rule-based machine learning or inductive inference engine and integrate it into a long-life learner. In order to do that we work over the graph of working memory rules and introduce several topological metrics over it from which we derive an oblivion criterion to drop useless rules from working memory and a consolidation process to promote the rules to the knowledge base. We evaluate the framework on a series of tasks in a chess rule learning domain.

Distance‐based and generalization‐based methods are two families of artificial intelligence techniques that have been successfully used over a wide range of real‐world problems. In the first case, general algorithms can be applied to any data representation by just changing the distance. The metric space sets the search and learning space, which is generally instance‐oriented. In the second case, models can be obtained for a given pattern language, which can be comprehensible. The generality‐ordered space sets the search and learning space, which is generally model‐oriented. However, the concepts of distance and generalization clash in many different ways, especially when knowledge representation is complex (e.g., structured data). This work establishes a framework where these two fields can be integrated in a consistent way. We introduce the concept of distance‐based generalization, which connects all the generalized examples in such a way that all of them are reachable inside the generalization by using straight paths in the metric space. This makes the metric space and the generality‐ordered space coherent (or even dual). Additionally, we also introduce a definition of minimal distance‐based generalization that can be seen as the first formulation of the Minimum Description Length (MDL)/Minimum Message Length (MML) principle in terms of a distance function. We instantiate and develop the framework for the most common data representations and distances, where we show that consistent instances can be found for numerical data, nominal data, sets, lists, tuples, graphs, first‐order atoms, and clauses. As a result, general learning methods that integrate the best from distance‐based and generalization‐based methods can be defined and adapted to any specific problem by appropriately choosing the distance, the pattern language and the generalization operator.

Quantification is the name given to a novel machine learning task which deals with correctly estimating the number of elements of one class in a set of examples. The output of a quantifier is a real value, since training instances are the same as a classification problem, a natural approach is to train a classifier and to derive a quantifier from it. Some previous works have shown that just classifying the instances and counting the examples belonging to the class of interest classify count typically yields bad quantifiers, especially when the class distribution may vary between training and test. Hence, adjusted versions of classify count have been developed by using modified thresholds. However, previous works have explicitly discarded (without a deep analysis) any possible approach based on the probability estimations of the classifier. In this paper, we present a method based on averaging the probability estimations of a classifier with a very simple scaling that does perform reasonably well, showing that probability estimators for quantification capture a richer view of the problem than methods based on a threshold.

A sensible use of classifiers must be based on the estimated reliability of their predictions. A cautious classifier would delegate the difficult or uncertain predictions to other, possibly more specialised, classifiers. In this paper we analyse and develop this idea of delegating classifiers in a systematic way. First, we design a two-step scenario where a first classifier chooses which examples to classify and delegates the difficult examples to train a second classifier. Secondly, we present an iterated scenario involving an arbitrary number of chained classifiers. We compare these scenarios to classical ensemble methods, such as bagging and boosting. We show experimentally that our approach is not far behind these methods in terms of accuracy, but with several advantages: (i) improved efficiency, since each classifier learns from fewer examples than the previous one; (ii) improved comprehensibility, since each classification derives from a single classifier; and (iii) the possibility to simplify the overall multi-classifier by removing the parts that lead to delegation.