Undesirable biases encoded in the data are key drivers of algorithmic discrimination. Their importance is widely recognized in the algorithmic fairness literature, as well as legislation and standards on anti-discrimination in AI. Despite this recognition, data biases remain understudied, hindering the development of computational best practices for their detection and mitigation. In this work, we present three common data biases and study their individual and joint effect on algorithmic discrimination across a variety of datasets, models, and fairness measures. We find that underrepresentation of vulnerable populations in training sets is less conducive to discrimination than conventionally affirmed, while combinations of proxies and label bias can be far more critical. Consequently, we develop dedicated mechanisms to detect specific types of bias, and combine them into a preliminary construct we refer to as the Data Bias Profile (DBP). This initial formulation serves as a proof of concept for how different bias signals can be systematically documented. Through a case study with popular fairness datasets, we demonstrate the effectiveness of the DBP in predicting the risk of discriminatory outcomes and the utility of fairness-enhancing interventions. Overall, this article bridges algorithmic fairness research and anti-discrimination policy through a data-centric lens.

Motivated by applications such as cloud platforms allocating GPUs to users or governments deploying mobile health units across competing regions, we study the dynamic allocation of a reusable resource to strategic agents with private valuations. Our objective is to simultaneously (i) maximize social welfare, (ii) satisfy multi-dimensional long-term cost constraints, and (iii) incentivize truthful reporting. We begin by numerically evaluating primal-dual methods widely used in constrained online optimization and find them to be highly fragile in strategic settings -- agents can easily manipulate their reports to distort future dual updates for future gain. To address this vulnerability, we develop an incentive-aware framework that makes primal-dual methods robust to strategic behavior. Our design combines epoch-based lazy updates -- where dual variables remain fixed within each epoch -- with randomized exploration rounds that extract approximately truthful signals for learning. Leveraging carefully designed online learning subroutines that can be of independent interest for dual updates, our mechanism achieves $\tilde{\mathcal{O}}(\sqrt{T})$ social welfare regret, satisfies all cost constraints, and ensures incentive alignment. This matches the performance of non-strategic allocation approaches while being robust to strategic agents.

Fluid antenna systems represent an innovative approach in wireless communication, recently applied in multiple access to optimize the signal-to-interference-plus-noise ratio through port selection. This letter frames the port selection problem as a multi-label classification task for the first time, improving best-port selection with limited port observations. We address this challenge by leveraging liquid neural networks (LNNs) to predict the optimal port under emerging fluid antenna multiple access scenarios alongside a more general $α$-$μ$ fading model. We also apply hyperparameter optimization to refine LNN architectures for different observation scenarios. Our approach yields lower outage probability values than existing methods.

We revise the definition of clickbait, which lacks current consensus, and argue that the creation of a curiosity gap is the key concept that distinguishes clickbait from other related phenomena such as sensationalism and headlines that do not deliver what they promise or diverge from the article. Therefore, we propose a new definition: clickbait is a technique for generating headlines and teasers that deliberately omit part of the information with the goal of raising the readers' curiosity, capturing their attention and enticing them to click. We introduce a new approach to clickbait detection datasets creation, by refining the concept limits and annotations criteria, minimizing the subjectivity in the decision as much as possible. Following it, we created and release TA1C (for Te Ahorré Un Click, Spanish for Saved You A Click), the first open source dataset for clickbait detection in Spanish. It consists of 3,500 tweets coming from 18 well known media sources, manually annotated and reaching a 0.825 Fleiss' κ inter annotator agreement. We implement strong baselines that achieve 0.84 in F1-score.

Diabetic retinopathy (DR) is a leading cause of vision loss in working-age adults. While screening reduces the risk of blindness, traditional imaging is often costly and inaccessible. Artificial intelligence (AI) algorithms present a scalable diagnostic solution, but concerns regarding fairness and generalization persist. This work evaluates the fairness and performance of image-trained models in DR prediction, as well as the impact of disentanglement as a bias mitigation technique, using the diverse mBRSET fundus dataset. Three models, ConvNeXt V2, DINOv2, and Swin V2, were trained on macula images to predict DR and sensitive attributes (SAs) (e.g., age and gender/sex). Fairness was assessed between subgroups of SAs, and disentanglement was applied to reduce bias. All models achieved high DR prediction performance in diagnosing (up to 94% AUROC) and could reasonably predict age and gender/sex (91% and 77% AUROC, respectively). Fairness assessment suggests disparities, such as a 10% AUROC gap between age groups in DINOv2. Disentangling SAs from DR prediction had varying results, depending on the model selected. Disentanglement improved DINOv2 performance (2% AUROC gain), but led to performance drops in ConvNeXt V2 and Swin V2 (7% and 3%, respectively). These findings highlight the complexity of disentangling fine-grained features in fundus imaging and emphasize the importance of fairness in medical imaging AI to ensure equitable and reliable healthcare solutions.

Fraud detection is one of the most important challenges that financial systems must address. Detecting fraudulent transactions is critical for payment gateway companies like Flow Payment, which process millions of transactions monthly and require robust security measures to mitigate financial risks . Increasing transaction authorization rates while reducing fraud is essential for providing a good user experience and building a sustainable business. For this reason, discovering novel and improved methods to detect fraud requires continuous research an d investment for any company that wants to succeed in this industry. In this work, we introduce d a novel method for detecting transactional fraud by incorporating the Relative Distance Rotating Encoding ( ReDRE) in the RoFormer model . The incorporation of angle rotation using ReDRE enhances the characterization of time series data within a Transformer, leading to improved fraud detection by better capturing temporal dependencies and event relationships.

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This study evaluates the recently proposed Document Attention Network (DAN) for extracting key-value information from Uruguayan birth certificates, handwritten in Spanish. We investigate two annotation strategies for automatically transcribing handwritten documents, fine-tuning DAN with minimal training data and annotation effort. Experiments were conducted on two datasets containing the same images (201 scans of birth certificates written by more than 15 different writers) but with different annotation methods. Our findings indicate that normalized annotation is more effective for fields that can be standardized, such as dates and places of birth, whereas diplomatic annotation performs much better for fields containing names and surnames, which can not be standardized.

Proto-personas are commonly used during early-stage Product Discovery, such as Lean Inception, to guide product definition and stakeholder alignment. However, the manual creation of proto-personas is often time-consuming, cognitively demanding, and prone to bias. In this paper, we propose and empirically investigate a prompt engineering-based approach to generate proto-personas with the support of Generative AI (GenAI). Our goal is to evaluate the approach in terms of efficiency, effectiveness, user acceptance, and the empathy elicited by the generated personas. We conducted a case study with 19 participants embedded in a real Lean Inception, employing a qualitative and quantitative methods design. The results reveal the approach's efficiency by reducing time and effort and improving the quality and reusability of personas in later discovery phases, such as Minimum Viable Product (MVP) scoping and feature refinement. While acceptance was generally high, especially regarding perceived usefulness and ease of use, participants noted limitations related to generalization and domain specificity. Furthermore, although cognitive empathy was strongly supported, affective and behavioral empathy varied significantly across participants. These results contribute novel empirical evidence on how GenAI can be effectively integrated into software Product Discovery practices, while also identifying key challenges to be addressed in future iterations of such hybrid design processes.

Essays are considered a valuable mechanism for evaluating learning outcomes in writing. Textual cohesion is an essential characteristic of a text, as it facilitates the establishment of meaning between its parts. Automatically scoring cohesion in essays presents a challenge in the field of educational artificial intelligence. The machine learning algorithms used to evaluate texts generally do not consider the individual characteristics of the instances that comprise the analysed corpus. In this meaning, item response theory can be adapted to the context of machine learning, characterising the ability, difficulty and discrimination of the models used. This work proposes and analyses the performance of a cohesion score prediction approach based on item response theory to adjust the scores generated by machine learning models. In this study, the corpus selected for the experiments consisted of the extended Essay-BR, which includes 6,563 essays in the style of the National High School Exam (ENEM), and the Brazilian Portuguese Narrative Essays, comprising 1,235 essays written by 5th to 9th grade students from public schools. We extracted 325 linguistic features and treated the problem as a machine learning regression task. The experimental results indicate that the proposed approach outperforms conventional machine learning models and ensemble methods in several evaluation metrics. This research explores a potential approach for improving the automatic evaluation of cohesion in educational essays.