pwc-archive/papers-with-abstracts huggingface dataset:
FeedQUAC: Quick Unobtrusive AI-Generated Commentary
Authors: Tao Long, Kendra Wannamaker, Jo Vermeulen, George Fitzmaurice, Justin Matejka
Date: 2025-04-23 00:00:00

Design thrives on feedback. However, gathering constant feedback throughout the design process can be labor-intensive and disruptive. We explore how AI can bridge this gap by providing effortless, ambient feedback. We introduce FeedQUAC, a design companion that delivers real-time AI-generated commentary from a variety of perspectives through different personas. A design probe study with eight participants highlights how designers can leverage quick yet ambient AI feedback to enhance their creative workflows. Participants highlight benefits such as convenience, playfulness, confidence boost, and inspiration from this lightweight feedback agent, while suggesting additional features, like chat interaction and context curation. We discuss the role of AI feedback, its strengths and limitations, and how to integrate it into existing design workflows while balancing user involvement. Our findings also suggest that ambient interaction is a valuable consideration for both the design and evaluation of future creativity support systems.
Tinker Tales: Interactive Storytelling Framework for Early Childhood Narrative Development and AI Literacy
Authors: Nayoung Choi, Peace Cyebukayire, Jinho D. Choi
Date: 2025-04-17 00:00:00
Tasks: AI Agent

This paper presents Tinker Tales, an interactive storytelling framework in the format of a board game, designed to support both narrative development and AI literacy in early childhood. The framework integrates tangible and speech-based interactions with AI through NFC chip-attached pawns and tokens, along with a speaker and microphone. Children select and define key story elements-such as characters, places, items, and emotions-using the pawns and tokens, providing further details to the AI and receiving proper assistance, similar to how adults prompt AI for specific tasks (e.g., writing). For evaluation, several game sessions were simulated with a child AI agent, and the quality and safety of the generated stories were assessed from various perspectives. This work highlights the potential of combining physical and digital elements in AI literacy, offering a safe and engaging way for children to learn how to effectively collaborate with AI.
Differentiable Nonlinear Model Predictive Control
Authors: Jonathan Frey, Katrin Baumgärtner, Gianluca Frison, Dirk Reinhardt, Jasper Hoffmann, Leonard Fichtner, Sebastien Gros, Moritz Diehl
Date: 2025-05-02 00:00:00
Tasks: model, Model Predictive Control

The efficient computation of parametric solution sensitivities is a key challenge in the integration of learning-enhanced methods with nonlinear model predictive control (MPC), as their availability is crucial for many learning algorithms. While approaches presented in the machine learning community are limited to convex or unconstrained formulations, this paper discusses the computation of solution sensitivities of general nonlinear programs (NLPs) using the implicit function theorem (IFT) and smoothed optimality conditions treated in interior-point methods (IPM). We detail sensitivity computation within a sequential quadratic programming (SQP) method which employs an IPM for the quadratic subproblems. The publication is accompanied by an efficient open-source implementation within the framework, providing both forward and adjoint sensitivities for general optimal control problems, achieving speedups exceeding 3x over the state-of-the-art solver mpc.pytorch.
FairPlay: A Collaborative Approach to Mitigate Bias in Datasets for Improved AI Fairness
Authors: Tina Behzad, Mithilesh Kumar Singh, Anthony J. Ripa, Klaus Mueller
Date: 2025-04-22 00:00:00
Tasks: Decision Making, Fairness

The issue of fairness in decision-making is a critical one, especially given the variety of stakeholder demands for differing and mutually incompatible versions of fairness. Adopting a strategic interaction of perspectives provides an alternative to enforcing a singular standard of fairness. We present a web-based software application, FairPlay, that enables multiple stakeholders to debias datasets collaboratively. With FairPlay, users can negotiate and arrive at a mutually acceptable outcome without a universally agreed-upon theory of fairness. In the absence of such a tool, reaching a consensus would be highly challenging due to the lack of a systematic negotiation process and the inability to modify and observe changes. We have conducted user studies that demonstrate the success of FairPlay, as users could reach a consensus within about five rounds of gameplay, illustrating the application's potential for enhancing fairness in AI systems.
Reflexive Prompt Engineering: A Framework for Responsible Prompt Engineering and Interaction Design
Authors: Christian Djeffal
Date: 2025-04-22 00:00:00
Tasks: Fairness, Prompt Engineering

Responsible prompt engineering has emerged as a critical framework for ensuring that generative artificial intelligence (AI) systems serve society's needs while minimizing potential harms. As generative AI applications become increasingly powerful and ubiquitous, the way we instruct and interact with them through prompts has profound implications for fairness, accountability, and transparency. This article examines how strategic prompt engineering can embed ethical and legal considerations and societal values directly into AI interactions, moving beyond mere technical optimization for functionality. This article proposes a comprehensive framework for responsible prompt engineering that encompasses five interconnected components: prompt design, system selection, system configuration, performance evaluation, and prompt management. Drawing from empirical evidence, the paper demonstrates how each component can be leveraged to promote improved societal outcomes while mitigating potential risks. The analysis reveals that effective prompt engineering requires a delicate balance between technical precision and ethical consciousness, combining the systematic rigor and focus on functionality with the nuanced understanding of social impact. Through examination of real-world and emerging practices, the article illustrates how responsible prompt engineering serves as a crucial bridge between AI development and deployment, enabling organizations to fine-tune AI outputs without modifying underlying model architectures. This approach aligns with broader "Responsibility by Design" principles, embedding ethical considerations directly into the implementation process rather than treating them as post-hoc additions. The article concludes by identifying key research directions and practical guidelines for advancing the field of responsible prompt engineering.
BalancEdit: Dynamically Balancing the Generality-Locality Trade-off in Multi-modal Model Editing
Authors: Dongliang Guo, Mengxuan Hu, Zihan Guan, Thomas Hartvigsen, Sheng Li
Date: 2025-05-02 00:00:00
Tasks: knowledge editing, Model Editing

Large multi-modal models inevitably decay over time as facts change and previously learned information becomes outdated. Traditional approaches such as fine-tuning are often impractical for updating these models due to their size and complexity. Instead, direct knowledge editing within the models presents a more viable solution. Current model editing techniques, however, typically overlook the unique influence ranges of different facts, leading to compromised model performance in terms of both generality and locality. To address this issue, we introduce the concept of the generality-locality trade-off in multi-modal model editing. We develop a new model editing dataset named OKEDIT, specifically designed to effectively evaluate this trade-off. Building on this foundation, we propose BalancEdit, a novel method for balanced model editing that dynamically achieves an optimal balance between generality and locality. BalancEdit utilizes a unique mechanism that generates both positive and negative samples for each fact to accurately determine its influence scope and incorporates these insights into the model's latent space using a discrete, localized codebook of edits, without modifying the underlying model weights. To our knowledge, this is the first approach explicitly addressing the generality-locality trade-off in multi-modal model editing. Our comprehensive results confirm the effectiveness of BalancEdit, demonstrating minimal trade-offs while maintaining robust editing capabilities. Our code and dataset will be available.
Leveraging Social Media Analytics for Sustainability Trend Detection in Saudi Arabias Evolving Market
Authors: Kanwal Aalijah
Date: 2025-04-22 00:00:00

Saudi Arabias rapid economic growth and social evolution under Vision 2030 present a unique opportunity to track emerging trends in real time. Uncovering trends in real time can open up new avenues for business and investment opportunities. This paper explores how AI and social media analytics can uncover and monitor these trends across sectors like sustainability, construction, food beverages industry, tourism, technology, and entertainment. This paper focus on use of AI-driven methodology to identify sustainability trends across Saudi Arabia. We processed millions of social media posts, news, blogs in order to understand sustainability trends in the region. The paper presents an AI approach that can help economists, businesses, government to understand sustainability trends and make better decisions around them. This approach offers both sector-specific and cross-sector insights, giving decision-makers a reliable, up to date snapshot of Saudi Arabias market shifts. Beyond Saudi Arabia, this framework also shows potential for adapting to other regions. Overall, our findings highlight how by using AI-methodologies, give decision makers a reliable method to understand how initiatives are perceived and adopted by the public and understand growth of trends.
Few-shot Hate Speech Detection Based on the MindSpore Framework
Authors: Zhenkai Qin, Dongze Wu, Yuxin Liu, Guifang Yang
Date: 2025-04-22 00:00:00
Tasks: Data Augmentation, Hate Speech Detection

The proliferation of hate speech on social media poses a significant threat to online communities, requiring effective detection systems. While deep learning models have shown promise, their performance often deteriorates in few-shot or low-resource settings due to reliance on large annotated corpora. To address this, we propose MS-FSLHate, a prompt-enhanced neural framework for few-shot hate speech detection implemented on the MindSpore deep learning platform. The model integrates learnable prompt embeddings, a CNN-BiLSTM backbone with attention pooling, and synonym-based adversarial data augmentation to improve generalization. Experimental results on two benchmark datasets-HateXplain and HSOL-demonstrate that our approach outperforms competitive baselines in precision, recall, and F1-score. Additionally, the framework shows high efficiency and scalability, suggesting its suitability for deployment in resource-constrained environments. These findings highlight the potential of combining prompt-based learning with adversarial augmentation for robust and adaptable hate speech detection in few-shot scenarios.
CostFilter-AD: Enhancing Anomaly Detection through Matching Cost Filtering
Authors: Zhe Zhang, Mingxiu Cai, Hanxiao Wang, Gaochang Wu, Tianyou Chai, Xiatian Zhu
Date: 2025-05-02 00:00:00
Tasks: Anomaly Detection, Unsupervised Anomaly Detection

Unsupervised anomaly detection (UAD) seeks to localize the anomaly mask of an input image with respect to normal samples. Either by reconstructing normal counterparts (reconstruction-based) or by learning an image feature embedding space (embedding-based), existing approaches fundamentally rely on image-level or feature-level matching to derive anomaly scores. Often, such a matching process is inaccurate yet overlooked, leading to sub-optimal detection. To address this issue, we introduce the concept of cost filtering, borrowed from classical matching tasks, such as depth and flow estimation, into the UAD problem. We call this approach {\em CostFilter-AD}. Specifically, we first construct a matching cost volume between the input and normal samples, comprising two spatial dimensions and one matching dimension that encodes potential matches. To refine this, we propose a cost volume filtering network, guided by the input observation as an attention query across multiple feature layers, which effectively suppresses matching noise while preserving edge structures and capturing subtle anomalies. Designed as a generic post-processing plug-in, CostFilter-AD can be integrated with either reconstruction-based or embedding-based methods. Extensive experiments on MVTec-AD and VisA benchmarks validate the generic benefits of CostFilter-AD for both single- and multi-class UAD tasks. Code and models will be released at https://github.com/ZHE-SAPI/CostFilter-AD.
Towards responsible AI for education: Hybrid human-AI to confront the Elephant in the room
Authors: Danial Hooshyar, Gustav Šír, Yeongwook Yang, Eve Kikas, Raija Hämäläinen, Tommi Kärkkäinen, Dragan Gašević, Roger Azevedo
Date: 2025-04-22 00:00:00
Tasks: Benchmarking, Fairness

Despite significant advancements in AI-driven educational systems and ongoing calls for responsible AI for education, several critical issues remain unresolved -- acting as the elephant in the room within AI in education, learning analytics, educational data mining, learning sciences, and educational psychology communities. This critical analysis identifies and examines nine persistent challenges that continue to undermine the fairness, transparency, and effectiveness of current AI methods and applications in education. These include: (1) the lack of clarity around what AI for education truly means -- often ignoring the distinct purposes, strengths, and limitations of different AI families -- and the trend of equating it with domain-agnostic, company-driven large language models; (2) the widespread neglect of essential learning processes such as motivation, emotion, and (meta)cognition in AI-driven learner modelling and their contextual nature; (3) limited integration of domain knowledge and lack of stakeholder involvement in AI design and development; (4) continued use of non-sequential machine learning models on temporal educational data; (5) misuse of non-sequential metrics to evaluate sequential models; (6) use of unreliable explainable AI methods to provide explanations for black-box models; (7) ignoring ethical guidelines in addressing data inconsistencies during model training; (8) use of mainstream AI methods for pattern discovery and learning analytics without systematic benchmarking; and (9) overemphasis on global prescriptions while overlooking localised, student-specific recommendations. Supported by theoretical and empirical research, we demonstrate how hybrid AI methods -- specifically neural-symbolic AI -- can address the elephant in the room and serve as the foundation for responsible, trustworthy AI systems in education.
Enhancing SPARQL Query Rewriting for Complex Ontology Alignments
Authors: Anicet Lepetit Ondo, Laurence Capus, Mamadou Bousso
Date: 2025-05-02 00:00:00

SPARQL query rewriting is a fundamental mechanism for uniformly querying heterogeneous ontologies in the Linked Data Web. However, the complexity of ontology alignments, particularly rich correspondences (c : c), makes this process challenging. Existing approaches primarily focus on simple (s : s) and partially complex ( s : c) alignments, thereby overlooking the challenges posed by more expressive alignments. Moreover, the intricate syntax of SPARQL presents a barrier for non-expert users seeking to fully exploit the knowledge encapsulated in ontologies. This article proposes an innovative approach for the automatic rewriting of SPARQL queries from a source ontology to a target ontology, based on a user's need expressed in natural language. It leverages the principles of equivalence transitivity as well as the advanced capabilities of large language models such as GPT-4. By integrating these elements, this approach stands out for its ability to efficiently handle complex alignments, particularly (c : c) correspondences , by fully exploiting their expressiveness. Additionally, it facilitates access to aligned ontologies for users unfamiliar with SPARQL, providing a flexible solution for querying heterogeneous data.
A closer look at how large language models trust humans: patterns and biases
Authors: Valeria Lerman, Yaniv Dover
Date: 2025-04-22 00:00:00
Tasks: Decision Making

As large language models (LLMs) and LLM-based agents increasingly interact with humans in decision-making contexts, understanding the trust dynamics between humans and AI agents becomes a central concern. While considerable literature studies how humans trust AI agents, it is much less understood how LLM-based agents develop effective trust in humans. LLM-based agents likely rely on some sort of implicit effective trust in trust-related contexts (e.g., evaluating individual loan applications) to assist and affect decision making. Using established behavioral theories, we develop an approach that studies whether LLMs trust depends on the three major trustworthiness dimensions: competence, benevolence and integrity of the human subject. We also study how demographic variables affect effective trust. Across 43,200 simulated experiments, for five popular language models, across five different scenarios we find that LLM trust development shows an overall similarity to human trust development. We find that in most, but not all cases, LLM trust is strongly predicted by trustworthiness, and in some cases also biased by age, religion and gender, especially in financial scenarios. This is particularly true for scenarios common in the literature and for newer models. While the overall patterns align with human-like mechanisms of effective trust formation, different models exhibit variation in how they estimate trust; in some cases, trustworthiness and demographic factors are weak predictors of effective trust. These findings call for a better understanding of AI-to-human trust dynamics and monitoring of biases and trust development patterns to prevent unintended and potentially harmful outcomes in trust-sensitive applications of AI.
Document Retrieval Augmented Fine-Tuning (DRAFT) for safety-critical software assessments
Authors: Regan Bolton, Mohammadreza Sheikhfathollahi, Simon Parkinson, Vanessa Vulovic, Gary Bamford, Dan Basher, Howard Parkinson
Date: 2025-05-02 00:00:00
Tasks: Dataset Generation, Language Modeling, Language Modelling, Large Language Model, RAG, Retrieval, Retrieval-augmented Generation

Safety critical software assessment requires robust assessment against complex regulatory frameworks, a process traditionally limited by manual evaluation. This paper presents Document Retrieval-Augmented Fine-Tuning (DRAFT), a novel approach that enhances the capabilities of a large language model (LLM) for safety-critical compliance assessment. DRAFT builds upon existing Retrieval-Augmented Generation (RAG) techniques by introducing a novel fine-tuning framework that accommodates our dual-retrieval architecture, which simultaneously accesses both software documentation and applicable reference standards. To fine-tune DRAFT, we develop a semi-automated dataset generation methodology that incorporates variable numbers of relevant documents with meaningful distractors, closely mirroring real-world assessment scenarios. Experiments with GPT-4o-mini demonstrate a 7% improvement in correctness over the baseline model, with qualitative improvements in evidence handling, response structure, and domain-specific reasoning. DRAFT represents a practical approach to improving compliance assessment systems while maintaining the transparency and evidence-based reasoning essential in regulatory domains.
Assessing FAIRness of the Digital Shadow Reference Model
Authors: Johannes Theissen-Lipp
Date: 2025-04-22 00:00:00
Tasks: Fairness, Management

Models play a critical role in managing the vast amounts of data and increasing complexity found in the IoT, IIoT, and IoP domains. The Digital Shadow Reference Model, which serves as a foundational metadata schema for linking data and metadata in these environments, is an example of such a model. Ensuring FAIRness (adherence to the FAIR Principles) is critical because it improves data findability, accessibility, interoperability, and reusability, facilitating efficient data management and integration across systems. This paper presents an evaluation of the FAIRness of the Digital Shadow Reference Model using a structured evaluation framework based on the FAIR Data Principles. Using the concept of FAIR Implementation Profiles (FIPs), supplemented by a mini-questionnaire, we systematically evaluate the model's adherence to these principles. Our analysis identifies key strengths, including the model's metadata schema that supports rich descriptions and authentication techniques, and highlights areas for improvement, such as the need for globally unique identifiers and consequent support for different Web standards. The results provide actionable insights for improving the FAIRness of the model and promoting better data management and reuse. This research contributes to the field by providing a detailed assessment of the Digital Shadow Reference Model and recommending next steps to improve its FAIRness and usability.
LLM-based Semantic Augmentation for Harmful Content Detection
Authors: Elyas Meguellati, Assaad Zeghina, Shazia Sadiq, Gianluca Demartini
Date: 2025-04-22 00:00:00
Tasks: Classification, Hateful Meme Classification, Meme Classification, Propaganda detection, text-classification, Text Classification

Recent advances in large language models (LLMs) have demonstrated strong performance on simple text classification tasks, frequently under zero-shot settings. However, their efficacy declines when tackling complex social media challenges such as propaganda detection, hateful meme classification, and toxicity identification. Much of the existing work has focused on using LLMs to generate synthetic training data, overlooking the potential of LLM-based text preprocessing and semantic augmentation. In this paper, we introduce an approach that prompts LLMs to clean noisy text and provide context-rich explanations, thereby enhancing training sets without substantial increases in data volume. We systematically evaluate on the SemEval 2024 multi-label Persuasive Meme dataset and further validate on the Google Jigsaw toxic comments and Facebook hateful memes datasets to assess generalizability. Our results reveal that zero-shot LLM classification underperforms on these high-context tasks compared to supervised models. In contrast, integrating LLM-based semantic augmentation yields performance on par with approaches that rely on human-annotated data, at a fraction of the cost. These findings underscore the importance of strategically incorporating LLMs into machine learning (ML) pipeline for social media classification tasks, offering broad implications for combating harmful content online.