pwc-archive/papers-with-abstracts huggingface dataset:
Computational Studies in Influencer Marketing: A Systematic Literature Review
Authors: Haoyang Gui, Thales Bertaglia, Catalina Goanta, Gerasimos Spanakis
Date: 2025-06-17 00:00:00
Tasks: Fairness, Marketing, Systematic Literature Review

Influencer marketing has become a crucial feature of digital marketing strategies. Despite its rapid growth and algorithmic relevance, the field of computational studies in influencer marketing remains fragmented, especially with limited systematic reviews covering the computational methodologies employed. This makes overarching scientific measurements in the influencer economy very scarce, to the detriment of interested stakeholders outside of platforms themselves, such as regulators, but also researchers from other fields. This paper aims to provide an overview of the state of the art of computational studies in influencer marketing by conducting a systematic literature review (SLR) based on the PRISMA model. The paper analyses 69 studies to identify key research themes, methodologies, and future directions in this research field. The review identifies four major research themes: Influencer identification and characterisation, Advertising strategies and engagement, Sponsored content analysis and discovery, and Fairness. Methodologically, the studies are categorised into machine learning-based techniques (e.g., classification, clustering) and non-machine-learning-based techniques (e.g., statistical analysis, network analysis). Key findings reveal a strong focus on optimising commercial outcomes, with limited attention to regulatory compliance and ethical considerations. The review highlights the need for more nuanced computational research that incorporates contextual factors such as language, platform, and industry type, as well as improved model explainability and dataset reproducibility. The paper concludes by proposing a multidisciplinary research agenda that emphasises the need for further links to regulation and compliance technology, finer granularity in analysis, and the development of standardised datasets.
One Size Fits None: Rethinking Fairness in Medical AI
Authors: Roland Roller, Michael Hahn, Ajay Madhavan Ravichandran, Bilgin Osmanodja, Florian Oetke, Zeineb Sassi, Aljoscha Burchardt, Klaus Netter, Klemens Budde, Anne Herrmann, Tobias Strapatsas, Peter Dabrock, Sebastian Möller
Date: 2025-06-17 00:00:00
Tasks: Decision Making, Fairness

Machine learning (ML) models are increasingly used to support clinical decision-making. However, real-world medical datasets are often noisy, incomplete, and imbalanced, leading to performance disparities across patient subgroups. These differences raise fairness concerns, particularly when they reinforce existing disadvantages for marginalized groups. In this work, we analyze several medical prediction tasks and demonstrate how model performance varies with patient characteristics. While ML models may demonstrate good overall performance, we argue that subgroup-level evaluation is essential before integrating them into clinical workflows. By conducting a performance analysis at the subgroup level, differences can be clearly identified-allowing, on the one hand, for performance disparities to be considered in clinical practice, and on the other hand, for these insights to inform the responsible development of more effective models. Thereby, our work contributes to a practical discussion around the subgroup-sensitive development and deployment of medical ML models and the interconnectedness of fairness and transparency.
hyperFA*IR: A hypergeometric approach to fair rankings with finite candidate pool
Authors: Mauritz N. Cartier van Dissel, Samuel Martin-Gutierrez, Lisette Espín-Noboa, Ana María Jaramillo, Fariba Karimi
Date: 2025-06-17 00:00:00
Tasks: Fairness

Ranking algorithms play a pivotal role in decision-making processes across diverse domains, from search engines to job applications. When rankings directly impact individuals, ensuring fairness becomes essential, particularly for groups that are marginalised or misrepresented in the data. Most of the existing group fairness frameworks often rely on ensuring proportional representation of protected groups. However, these approaches face limitations in accounting for the stochastic nature of ranking processes or the finite size of candidate pools. To this end, we present hyperFA*IR, a framework for assessing and enforcing fairness in rankings drawn from a finite set of candidates. It relies on a generative process based on the hypergeometric distribution, which models real-world scenarios by sampling without replacement from fixed group sizes. This approach improves fairness assessment when top-$k$ selections are large relative to the pool or when protected groups are small. We compare our approach to the widely used binomial model, which treats each draw as independent with fixed probability, and demonstrate$-$both analytically and empirically$-$that our method more accurately reproduces the statistical properties of sampling from a finite population. To operationalise this framework, we propose a Monte Carlo-based algorithm that efficiently detects unfair rankings by avoiding computationally expensive parameter tuning. Finally, we adapt our generative approach to define affirmative action policies by introducing weights into the sampling process.
Fair for a few: Improving Fairness in Doubly Imbalanced Datasets
Authors: Ata Yalcin, Asli Umay Ozturk, Yigit Sever, Viktoria Pauw, Stephan Hachinger, Ismail Hakki Toroslu, Pinar Karagoz
Date: 2025-06-17 00:00:00
Tasks: Attribute, Decision Making, Fairness

Fairness has been identified as an important aspect of Machine Learning and Artificial Intelligence solutions for decision making. Recent literature offers a variety of approaches for debiasing, however many of them fall short when the data collection is imbalanced. In this paper, we focus on a particular case, fairness in doubly imbalanced datasets, such that the data collection is imbalanced both for the label and the groups in the sensitive attribute. Firstly, we present an exploratory analysis to illustrate limitations in debiasing on a doubly imbalanced dataset. Then, a multi-criteria based solution is proposed for finding the most suitable sampling and distribution for label and sensitive attribute, in terms of fairness and classification accuracy
Innovating China's Intangible Cultural Heritage with DeepSeek + MidJourney: The Case of Yangliuqing theme Woodblock Prints
Authors: Ruikun Yang, ZhongLiang Wei, Longdi Xian
Date: 2025-06-17 00:00:00

Yangliuqing woodblock prints, a cornerstone of China's intangible cultural heritage, are celebrated for their intricate designs and vibrant colors. However, preserving these traditional art forms while fostering innovation presents significant challenges. This study explores the DeepSeek + MidJourney approach to generating creative, themed Yangliuqing woodblock prints focused on the fight against COVID-19 and depicting joyous winners. Using Fr\'echet Inception Distance (FID) scores for evaluation, the method that combined DeepSeek-generated thematic prompts, MidJourney-generated thematic images, original Yangliuqing prints, and DeepSeek-generated key prompts in MidJourney-generated outputs achieved the lowest mean FID score (150.2) with minimal variability ({\sigma} = 4.9). Additionally, feedback from 62 participants, collected via questionnaires, confirmed that this hybrid approach produced the most representative results. Moreover, the questionnaire data revealed that participants demonstrated the highest willingness to promote traditional culture and the strongest interest in consuming the AI-generated images produced through this method. These findings underscore the effectiveness of an innovative approach that seamlessly blends traditional artistic elements with modern AI-driven creativity, ensuring both cultural preservation and contemporary relevance.
Students' Reliance on AI in Higher Education: Identifying Contributing Factors
Authors: Griffin Pitts, Neha Rani, Weedguet Mildort, Eva-Marie Cook
Date: 2025-06-16 00:00:00

The increasing availability and use of artificial intelligence (AI) tools in educational settings has raised concerns about students' overreliance on these technologies. Overreliance occurs when individuals accept incorrect AI-generated recommendations, often without critical evaluation, leading to flawed problem solutions and undermining learning outcomes. This study investigates potential factors contributing to patterns of AI reliance among undergraduate students, examining not only overreliance but also appropriate reliance (correctly accepting helpful and rejecting harmful recommendations) and underreliance (incorrectly rejecting helpful recommendations). Our approach combined pre- and post-surveys with a controlled experimental task where participants solved programming problems with an AI assistant that provided both accurate and deliberately incorrect suggestions, allowing direct observation of students' reliance patterns when faced with varying AI reliability. We find that appropriate reliance is significantly related to students' programming self-efficacy, programming literacy, and need for cognition, while showing negative correlations with post-task trust and satisfaction. Overreliance showed significant correlations with post-task trust and satisfaction with the AI assistant. Underreliance was negatively correlated with programming literacy, programming self-efficacy, and need for cognition. Overall, the findings provide insights for developing targeted interventions that promote appropriate reliance on AI tools, with implications for the integration of AI in curriculum and educational technologies.
Delving Into the Psychology of Machines: Exploring the Structure of Self-Regulated Learning via LLM-Generated Survey Responses
Authors: Leonie V. D. E. Vogelsmeier, Eduardo Oliveira, Kamila Misiejuk, Sonsoles López-Pernas, Mohammed Saqr
Date: 2025-06-16 00:00:00
Tasks: Survey

Large language models (LLMs) offer the potential to simulate human-like responses and behaviors, creating new opportunities for psychological science. In the context of self-regulated learning (SRL), if LLMs can reliably simulate survey responses at scale and speed, they could be used to test intervention scenarios, refine theoretical models, augment sparse datasets, and represent hard-to-reach populations. However, the validity of LLM-generated survey responses remains uncertain, with limited research focused on SRL and existing studies beyond SRL yielding mixed results. Therefore, in this study, we examined LLM-generated responses to the 44-item Motivated Strategies for Learning Questionnaire (MSLQ; Pintrich \& De Groot, 1990), a widely used instrument assessing students' learning strategies and academic motivation. Particularly, we used the LLMs GPT-4o, Claude 3.7 Sonnet, Gemini 2 Flash, LLaMA 3.1-8B, and Mistral Large. We analyzed item distributions, the psychological network of the theoretical SRL dimensions, and psychometric validity based on the latent factor structure. Our results suggest that Gemini 2 Flash was the most promising LLM, showing considerable sampling variability and producing underlying dimensions and theoretical relationships that align with prior theory and empirical findings. At the same time, we observed discrepancies and limitations, underscoring both the potential and current constraints of using LLMs for simulating psychological survey data and applying it in educational contexts.
Accessibility Barriers in Multi-Terabyte Public Datasets: The Gap Between Promise and Practice
Authors: Marc Bara
Date: 2025-06-16 00:00:00
Tasks: Distributed Computing

The promise of "free and open" multi-terabyte datasets often collides with harsh realities. While these datasets may be technically accessible, practical barriers -- from processing complexity to hidden costs -- create a system that primarily serves well-funded institutions. This study examines accessibility challenges across web crawls, satellite imagery, scientific data, and collaborative projects, revealing a consistent two-tier system where theoretical openness masks practical exclusivity. Our analysis demonstrates that datasets marketed as "publicly accessible" typically require minimum investments of \$1,000+ for meaningful analysis, with complex processing pipelines demanding \$10,000-100,000+ in infrastructure costs. The infrastructure requirements -- distributed computing knowledge, domain expertise, and substantial budgets -- effectively gatekeep these datasets despite their "open" status, limiting practical accessibility to those with institutional support or substantial resources.
A Game-Theoretic Negotiation Framework for Cross-Cultural Consensus in LLMs
Authors: Guoxi Zhang, Jiawei Chen, Tianzhuo Yang, Jiaming Ji, Yaodong Yang, Juntao Dai
Date: 2025-06-16 00:00:00

The increasing prevalence of large language models (LLMs) is influencing global value systems. However, these models frequently exhibit a pronounced WEIRD (Western, Educated, Industrialized, Rich, Democratic) cultural bias due to lack of attention to minority values. This monocultural perspective may reinforce dominant values and marginalize diverse cultural viewpoints, posing challenges for the development of equitable and inclusive AI systems. In this work, we introduce a systematic framework designed to boost fair and robust cross-cultural consensus among LLMs. We model consensus as a Nash Equilibrium and employ a game-theoretic negotiation method based on Policy-Space Response Oracles (PSRO) to simulate an organized cross-cultural negotiation process. To evaluate this approach, we construct regional cultural agents using data transformed from the World Values Survey (WVS). Beyond the conventional model-level evaluation method, We further propose two quantitative metrics, Perplexity-based Acceptence and Values Self-Consistency, to assess consensus outcomes. Experimental results indicate that our approach generates consensus of higher quality while ensuring more balanced compromise compared to baselines. Overall, it mitigates WEIRD bias by guiding agents toward convergence through fair and gradual negotiation steps.
Rethinking Test-Time Scaling for Medical AI: Model and Task-Aware Strategies for LLMs and VLMs
Authors: Gyutaek Oh, Seoyeon Kim, Sangjoon Park, Byung-Hoon Kim
Date: 2025-06-16 00:00:00

Test-time scaling has recently emerged as a promising approach for enhancing the reasoning capabilities of large language models or vision-language models during inference. Although a variety of test-time scaling strategies have been proposed, and interest in their application to the medical domain is growing, many critical aspects remain underexplored, including their effectiveness for vision-language models and the identification of optimal strategies for different settings. In this paper, we conduct a comprehensive investigation of test-time scaling in the medical domain. We evaluate its impact on both large language models and vision-language models, considering factors such as model size, inherent model characteristics, and task complexity. Finally, we assess the robustness of these strategies under user-driven factors, such as misleading information embedded in prompts. Our findings offer practical guidelines for the effective use of test-time scaling in medical applications and provide insights into how these strategies can be further refined to meet the reliability and interpretability demands of the medical domain.
Rethinking Optimization: A Systems-Based Approach to Social Externalities
Authors: Pegah Nokhiz, Aravinda Kanchana Ruwanpathirana, Helen Nissenbaum
Date: 2025-06-15 00:00:00
Tasks: Descriptive

Optimization is widely used for decision making across various domains, valued for its ability to improve efficiency. However, poor implementation practices can lead to unintended consequences, particularly in socioeconomic contexts where externalities (costs or benefits to third parties outside the optimization process) are significant. To propose solutions, it is crucial to first characterize involved stakeholders, their goals, and the types of subpar practices causing unforeseen outcomes. This task is complex because affected stakeholders often fall outside the direct focus of optimization processes. Also, incorporating these externalities into optimization requires going beyond traditional economic frameworks, which often focus on describing externalities but fail to address their normative implications or interconnected nature, and feedback loops. This paper suggests a framework that combines systems thinking with the economic concept of externalities to tackle these challenges. This approach aims to characterize what went wrong, who was affected, and how (or where) to include them in the optimization process. Economic externalities, along with their established quantification methods, assist in identifying "who was affected and how" through stakeholder characterization. Meanwhile, systems thinking (an analytical approach to comprehending relationships in complex systems) provides a holistic, normative perspective. Systems thinking contributes to an understanding of interconnections among externalities, feedback loops, and determining "when" to incorporate them in the optimization. Together, these approaches create a comprehensive framework for addressing optimization's unintended consequences, balancing descriptive accuracy with normative objectives. Using this, we examine three common types of subpar practices: ignorance, error, and prioritization of short-term goals.
Rethinking Hate Speech Detection on Social Media: Can LLMs Replace Traditional Models?
Authors: Daman Deep Singh, Ramanuj Bhattacharjee, Abhijnan Chakraborty
Date: 2025-06-15 00:00:00
Tasks: Hate Speech Detection, Transliteration

Hate speech detection across contemporary social media presents unique challenges due to linguistic diversity and the informal nature of online discourse. These challenges are further amplified in settings involving code-mixing, transliteration, and culturally nuanced expressions. While fine-tuned transformer models, such as BERT, have become standard for this task, we argue that recent large language models (LLMs) not only surpass them but also redefine the landscape of hate speech detection more broadly. To support this claim, we introduce IndoHateMix, a diverse, high-quality dataset capturing Hindi-English code-mixing and transliteration in the Indian context, providing a realistic benchmark to evaluate model robustness in complex multilingual scenarios where existing NLP methods often struggle. Our extensive experiments show that cutting-edge LLMs (such as LLaMA-3.1) consistently outperform task-specific BERT-based models, even when fine-tuned on significantly less data. With their superior generalization and adaptability, LLMs offer a transformative approach to mitigating online hate in diverse environments. This raises the question of whether future works should prioritize developing specialized models or focus on curating richer and more varied datasets to further enhance the effectiveness of LLMs.
The Synthetic Mirror -- Synthetic Data at the Age of Agentic AI
Authors: Marcelle Momha
Date: 2025-06-15 00:00:00
Tasks: Synthetic Data Generation

Synthetic data, which is artificially generated and intelligently mimicking or supplementing the real-world data, is increasingly used. The proliferation of AI agents and the adoption of synthetic data create a synthetic mirror that conceptualizes a representation and potential distortion of reality, thus generating trust and accountability deficits. This paper explores the implications for privacy and policymaking stemming from synthetic data generation, and the urgent need for new policy instruments and legal framework adaptation to ensure appropriate levels of trust and accountability for AI agents relying on synthetic data. Rather than creating entirely new policy or legal regimes, the most practical approach involves targeted amendments to existing frameworks, recognizing synthetic data as a distinct regulatory category with unique characteristics.
Fairness Research For Machine Learning Should Integrate Societal Considerations
Authors: Yijun Bian, Lei You
Date: 2025-06-14 00:00:00
Tasks: Fairness

Enhancing fairness in machine learning (ML) systems is increasingly important nowadays. While current research focuses on assistant tools for ML pipelines to promote fairness within them, we argue that: 1) The significance of properly defined fairness measures remains underestimated; and 2) Fairness research in ML should integrate societal considerations. The reasons include that detecting discrimination is critical due to the widespread deployment of ML systems and that human-AI feedback loops amplify biases, even when only small social and political biases persist.
Towards Fairness Assessment of Dutch Hate Speech Detection
Authors: Julie Bauer, Rishabh Kaushal, Thales Bertaglia, Adriana Iamnitchi
Date: 2025-06-14 00:00:00
Tasks: counterfactual, Fairness, Hate Speech Detection, Sentence

Numerous studies have proposed computational methods to detect hate speech online, yet most focus on the English language and emphasize model development. In this study, we evaluate the counterfactual fairness of hate speech detection models in the Dutch language, specifically examining the performance and fairness of transformer-based models. We make the following key contributions. First, we curate a list of Dutch Social Group Terms that reflect social context. Second, we generate counterfactual data for Dutch hate speech using LLMs and established strategies like Manual Group Substitution (MGS) and Sentence Log-Likelihood (SLL). Through qualitative evaluation, we highlight the challenges of generating realistic counterfactuals, particularly with Dutch grammar and contextual coherence. Third, we fine-tune baseline transformer-based models with counterfactual data and evaluate their performance in detecting hate speech. Fourth, we assess the fairness of these models using Counterfactual Token Fairness (CTF) and group fairness metrics, including equality of odds and demographic parity. Our analysis shows that models perform better in terms of hate speech detection, average counterfactual fairness and group fairness. This work addresses a significant gap in the literature on counterfactual fairness for hate speech detection in Dutch and provides practical insights and recommendations for improving both model performance and fairness.