pwc-archive/papers-with-abstracts huggingface dataset:
Taxonomizing Representational Harms using Speech Act Theory
Authors: Emily Corvi, Hannah Washington, Stefanie Reed, Chad Atalla, Alexandra Chouldechova, P. Alex Dow, Jean Garcia-Gathright, Nicholas Pangakis, Emily Sheng, Dan Vann, Matthew Vogel, Hanna Wallach
Date: 2025-04-01 00:00:00
Tasks: Fairness, valid

Representational harms are widely recognized among fairness-related harms caused by generative language systems. However, their definitions are commonly under-specified. We make a theoretical contribution to the specification of representational harms by introducing a framework, grounded in speech act theory (Austin, 1962), that conceptualizes representational harms caused by generative language systems as the perlocutionary effects (i.e., real-world impacts) of particular types of illocutionary acts (i.e., system behaviors). Building on this argument and drawing on relevant literature from linguistic anthropology and sociolinguistics, we provide new definitions of stereotyping, demeaning, and erasure. We then use our framework to develop a granular taxonomy of illocutionary acts that cause representational harms, going beyond the high-level taxonomies presented in previous work. We also discuss the ways that our framework and taxonomy can support the development of valid measurement instruments. Finally, we demonstrate the utility of our framework and taxonomy via a case study that engages with recent conceptual debates about what constitutes a representational harm and how such harms should be measured.
Role and Use of Race in AI/ML Models Related to Health
Authors: Martin C. Were, Ang Li, Bradley A. Malin, Zhijun Yin, Joseph R. Coco, Benjamin X. Collins, Ellen Wright Clayton, Laurie L. Novak, Rachele Hendricks-Sturrup, Abiodun Oluyomi, Shilo Anders, Chao Yan
Date: 2025-04-01 00:00:00
Tasks: Decision Making

The role and use of race within health-related artificial intelligence and machine learning (AI/ML) models has sparked increasing attention and controversy. Despite the complexity and breadth of related issues, a robust and holistic framework to guide stakeholders in their examination and resolution remains lacking. This perspective provides a broad-based, systematic, and cross-cutting landscape analysis of race-related challenges, structured around the AI/ML lifecycle and framed through "points to consider" to support inquiry and decision-making.
Investigating the Capabilities and Limitations of Machine Learning for Identifying Bias in English Language Data with Information and Heritage Professionals
Authors: Lucy Havens, Benjamin Bach, Melissa Terras, Beatrice Alex
Date: 2025-04-01 00:00:00
Tasks: Fairness

Despite numerous efforts to mitigate their biases, ML systems continue to harm already-marginalized people. While predominant ML approaches assume bias can be removed and fair models can be created, we show that these are not always possible, nor desirable, goals. We reframe the problem of ML bias by creating models to identify biased language, drawing attention to a dataset's biases rather than trying to remove them. Then, through a workshop, we evaluated the models for a specific use case: workflows of information and heritage professionals. Our findings demonstrate the limitations of ML for identifying bias due to its contextual nature, the way in which approaches to mitigating it can simultaneously privilege and oppress different communities, and its inevitability. We demonstrate the need to expand ML approaches to bias and fairness, providing a mixed-methods approach to investigating the feasibility of removing bias or achieving fairness in a given ML use case.
Towards Adaptive AI Governance: Comparative Insights from the U.S., EU, and Asia
Authors: Vikram Kulothungan, Deepti Gupta
Date: 2025-04-01 00:00:00

Artificial intelligence (AI) trends vary significantly across global regions, shaping the trajectory of innovation, regulation, and societal impact. This variation influences how different regions approach AI development, balancing technological progress with ethical and regulatory considerations. This study conducts a comparative analysis of AI trends in the United States (US), the European Union (EU), and Asia, focusing on three key dimensions: generative AI, ethical oversight, and industrial applications. The US prioritizes market-driven innovation with minimal regulatory constraints, the EU enforces a precautionary risk-based framework emphasizing ethical safeguards, and Asia employs state-guided AI strategies that balance rapid deployment with regulatory oversight. Although these approaches reflect different economic models and policy priorities, their divergence poses challenges to international collaboration, regulatory harmonization, and the development of global AI standards. To address these challenges, this paper synthesizes regional strengths to propose an adaptive AI governance framework that integrates risk-tiered oversight, innovation accelerators, and strategic alignment mechanisms. By bridging governance gaps, this study offers actionable insights for fostering responsible AI development while ensuring a balance between technological progress, ethical imperatives, and regulatory coherence.
Can LLMs Assist Computer Education? an Empirical Case Study of DeepSeek
Authors: Dongfu Xiao, Chen Gao, Zhengquan Luo, Chi Liu, Sheng Shen
Date: 2025-04-01 00:00:00
Tasks: Large Language Model

This study presents an empirical case study to assess the efficacy and reliability of DeepSeek-V3, an emerging large language model, within the context of computer education. The evaluation employs both CCNA simulation questions and real-world inquiries concerning computer network security posed by Chinese network engineers. To ensure a thorough evaluation, diverse dimensions are considered, encompassing role dependency, cross-linguistic proficiency, and answer reproducibility, accompanied by statistical analysis. The findings demonstrate that the model performs consistently, regardless of whether prompts include a role definition or not. In addition, its adaptability across languages is confirmed by maintaining stable accuracy in both original and translated datasets. A distinct contrast emerges between its performance on lower-order factual recall tasks and higher-order reasoning exercises, which underscores its strengths in retrieving information and its limitations in complex analytical tasks. Although DeepSeek-V3 offers considerable practical value for network security education, challenges remain in its capability to process multimodal data and address highly intricate topics. These results provide valuable insights for future refinement of large language models in specialized professional environments.
From Intuition to Understanding: Using AI Peers to Overcome Physics Misconceptions
Authors: Ruben Weijers, Denton Wu, Hannah Betts, Tamara Jacod, Yuxiang Guan, Vidya Sujaya, Kushal Dev, Toshali Goel, William Delooze, Reihaneh Rabbany, Ying Wu, Jean-François Godbout, Kellin Pelrine
Date: 2025-04-01 00:00:00
Tasks: Misconceptions

Generative AI has the potential to transform personalization and accessibility of education. However, it raises serious concerns about accuracy and helping students become independent critical thinkers. In this study, we designed a helpful AI "Peer" to help students correct fundamental physics misconceptions related to Newtonian mechanic concepts. In contrast to approaches that seek near-perfect accuracy to create an authoritative AI tutor or teacher, we directly inform students that this AI can answer up to 40% of questions incorrectly. In a randomized controlled trial with 165 students, those who engaged in targeted dialogue with the AI Peer achieved post-test scores that were, on average, 10.5 percentage points higher - with over 20 percentage points higher normalized gain - than a control group that discussed physics history. Qualitative feedback indicated that 91% of the treatment group's AI interactions were rated as helpful. Furthermore, by comparing student performance on pre- and post-test questions about the same concept, along with experts' annotations of the AI interactions, we find initial evidence suggesting the improvement in performance does not depend on the correctness of the AI. With further research, the AI Peer paradigm described here could open new possibilities for how we learn, adapt to, and grow with AI.
Using complex prompts to identify fine-grained biases in image generation through ChatGPT-4o
Authors: Marinus Ferreira
Date: 2025-04-01 00:00:00
Tasks: Image Generation, Sentiment Analysis

There are not one but two dimensions of bias that can be revealed through the study of large AI models: not only bias in training data or the products of an AI, but also bias in society, such as disparity in employment or health outcomes between different demographic groups. Often training data and AI output is biased for or against certain demographics (i.e. older white people are overrepresented in image datasets), but sometimes large AI models accurately illustrate biases in the real world (i.e. young black men being disproportionately viewed as threatening). These social disparities often appear in image generation AI outputs in the form of 'marked' features, where some feature of an individual or setting is a social marker of disparity, and prompts both humans and AI systems to treat subjects that are marked in this way as exceptional and requiring special treatment. Generative AI has proven to be very sensitive to such marked features, to the extent of over-emphasising them and thus often exacerbating social biases. I briefly discuss how we can use complex prompts to image generation AI to investigate either dimension of bias, emphasising how we can probe the large language models underlying image generation AI through, for example, automated sentiment analysis of the text prompts used to generate images.
Do Chinese models speak Chinese languages?
Authors: Andrea W Wen-Yi, Unso Eun Seo Jo, David Mimno
Date: 2025-03-31 00:00:00
Tasks: Reading Comprehension

The release of top-performing open-weight LLMs has cemented China's role as a leading force in AI development. Do these models support languages spoken in China? Or do they speak the same languages as Western models? Comparing multilingual capabilities is important for two reasons. First, language ability provides insights into pre-training data curation, and thus into resource allocation and development priorities. Second, China has a long history of explicit language policy, varying between inclusivity of minority languages and a Mandarin-first policy. To test whether Chinese LLMs today reflect an agenda about China's languages, we test performance of Chinese and Western open-source LLMs on Asian regional and Chinese minority languages. Our experiments on Information Parity and reading comprehension show Chinese models' performance across these languages correlates strongly (r=0.93) with Western models', with the sole exception being better Mandarin. Sometimes, Chinese models cannot identify languages spoken by Chinese minorities such as Kazakh and Uyghur, even though they are good at French and German. These results provide a window into current development priorities, suggest options for future development, and indicate guidance for end users.
Machine Learning for Identifying Potential Participants in Uruguayan Social Programs
Authors: Christian Beron Curti, Rodrigo Vargas Sainz, Yitong Tseo
Date: 2025-03-31 00:00:00

This research project explores the optimization of the family selection process for participation in Uruguay's Crece Contigo Family Support Program (PAF) through machine learning. An anonymized database of 15,436 previous referral cases was analyzed, focusing on pregnant women and children under four years of age. The main objective was to develop a predictive algorithm capable of determining whether a family meets the conditions for acceptance into the program. The implementation of this model seeks to streamline the evaluation process and allow for more efficient resource allocation, allocating more team time to direct support. The study included an exhaustive data analysis and the implementation of various machine learning models, including Neural Networks (NN), XGBoost (XGB), LSTM, and ensemble models. Techniques to address class imbalance, such as SMOTE and RUS, were applied, as well as decision threshold optimization to improve prediction accuracy and balance. The results demonstrate the potential of these techniques for efficient classification of families requiring assistance.
Are clinicians ethically obligated to disclose their use of medical machine learning systems to patients?
Authors: Joshua Hatherley
Date: 2025-03-31 00:00:00
Tasks: Ethics

It is commonly accepted that clinicians are ethically obligated to disclose their use of medical machine learning systems to patients, and that failure to do so would amount to a moral fault for which clinicians ought to be held accountable. Call this "the disclosure thesis." Four main arguments have been, or could be, given to support the disclosure thesis in the ethics literature: the risk-based argument, the rights-based argument, the materiality argument, and the autonomy argument. In this article, I argue that each of these four arguments are unconvincing, and therefore, that the disclosure thesis ought to be rejected. I suggest that mandating disclosure may also even risk harming patients by providing stakeholders with a way to avoid accountability for harm that results from improper applications or uses of these systems.
Empirical Analysis of Digital Innovations Impact on Corporate ESG Performance: The Mediating Role of GAI Technology
Authors: Jun Cui
Date: 2025-03-31 00:00:00

This study investigates the relationship between corporate digital innovation and Environmental, Social, and Governance (ESG) performance, with a specific focus on the mediating role of Generative artificial intelligence technology adoption. Using a comprehensive panel dataset of 8,000 observations from the CMARS and WIND database spanning from 2015 to 2023, we employ multiple econometric techniques to examine this relationship. Our findings reveal that digital innovation significantly enhances corporate ESG performance, with GAI technology adoption serving as a crucial mediating mechanism. Specifically, digital innovation positively influences GAI technology adoption, which subsequently improves ESG performance. Furthermore, our heterogeneity analysis indicates that this relationship varies across firm size, industry type, and ownership structure. Finally, our results remain robust after addressing potential endogeneity concerns through instrumental variable estimation, propensity score matching, and differenc in differences approaches. This research contributes to the growing literature on technologydriven sustainability transformations and offers practical implications for corporate strategy and policy development in promoting sustainable business practices through technological advancement.
One Person, One Bot
Authors: Liat Lavi
Date: 2025-03-31 00:00:00
Tasks: AI Agent

This short paper puts forward a vision for a new democratic model enabled by the recent technological advances in agentic AI. It therefore opens with drawing a clear and concise picture of the model, and only later addresses related proposals and research directions, and concerns regarding feasibility and safety. It ends with a note on the timeliness of this idea and on optimism. The model proposed is that of assigning each citizen an AI Agent that would serve as their political delegate, enabling the return to direct democracy. The paper examines this models relation to existing research, its potential setbacks and feasibility and argues for its further development.
Measuring Online Hate on 4chan using Pre-trained Deep Learning Models
Authors: Adrian Bermudez-Villalva, Maryam Mehrnezhad, Ehsan Toreini
Date: 2025-03-30 00:00:00
Tasks: Multi-class Classification

Online hate speech can harmfully impact individuals and groups, specifically on non-moderated platforms such as 4chan where users can post anonymous content. This work focuses on analysing and measuring the prevalence of online hate on 4chan's politically incorrect board (/pol/) using state-of-the-art Natural Language Processing (NLP) models, specifically transformer-based models such as RoBERTa and Detoxify. By leveraging these advanced models, we provide an in-depth analysis of hate speech dynamics and quantify the extent of online hate non-moderated platforms. The study advances understanding through multi-class classification of hate speech (racism, sexism, religion, etc.), while also incorporating the classification of toxic content (e.g., identity attacks and threats) and a further topic modelling analysis. The results show that 11.20% of this dataset is identified as containing hate in different categories. These evaluations show that online hate is manifested in various forms, confirming the complicated and volatile nature of detection in the wild.
Carbon Footprint Evaluation of Code Generation through LLM as a Service
Authors: Tina Vartziotis, Maximilian Schmidt, George Dasoulas, Ippolyti Dellatolas, Stefano Attademo, Viet Dung Le, Anke Wiechmann, Tim Hoffmann, Michael Keckeisen, Sotirios Kotsopoulos
Date: 2025-03-30 00:00:00
Tasks: Code Generation, Language Modeling, Language Modelling

Due to increased computing use, data centers consume and emit a lot of energy and carbon. These contributions are expected to rise as big data analytics, digitization, and large AI models grow and become major components of daily working routines. To reduce the environmental impact of software development, green (sustainable) coding and claims that AI models can improve energy efficiency have grown in popularity. Furthermore, in the automotive industry, where software increasingly governs vehicle performance, safety, and user experience, the principles of green coding and AI-driven efficiency could significantly contribute to reducing the sector's environmental footprint. We present an overview of green coding and metrics to measure AI model sustainability awareness. This study introduces LLM as a service and uses a generative commercial AI language model, GitHub Copilot, to auto-generate code. Using sustainability metrics to quantify these AI models' sustainability awareness, we define the code's embodied and operational carbon.
Artificial intelligence and democracy: Towards digital authoritarianism or a democratic upgrade?
Authors: Fereniki Panagopoulou
Date: 2025-03-30 00:00:00

Do robots vote? Do machines make decisions instead of us? No, (at least not yet), but this is something that could happen. The impact of Artificial Intelligence (AI) on democracy is a complex issue that requires thorough research and careful regulation. At the most important level, that of the electoral process, it is noted that it is not determined by the AI, but it is greatly impacted by its multiple applications. New types of online campaigns, driven by AI applications, are replacing traditional ones. The potential for manipulating voters and indirectly influencing the electoral outcome should not be underestimated. Certainly, instances of voter manipulation are not absent from traditional political campaigns, with the only difference being that digital manipulation is often carried out without our knowledge, e.g. by monitoring our behavior on social media. Nevertheless, we should not overlook the positive impact that AI has in the upgrading of democratic institutions by providing a forum for participation in decision-making. In this context, as a first step, we look into the potential jeopardization of democratic processes posed by the use of AI tools. Secondly, we consider the possibility of strengthening democratic processes by using AI, as well as the democratization of AI itself through the possibilities it offers. And thirdly, the impact of AI on the representative system is also discussed. The paper is concluded with recommendations and conclusions.
Who Owns the Output? Bridging Law and Technology in LLMs Attribution
Authors: Emanuele Mezzi, Asimina Mertzani, Michael P. Manis, Siyanna Lilova, Nicholas Vadivoulis, Stamatis Gatirdakis, Styliani Roussou, Rodayna Hmede
Date: 2025-03-29 00:00:00

Since the introduction of ChatGPT in 2022, Large language models (LLMs) and Large Multimodal Models (LMM) have transformed content creation, enabling the generation of human-quality content, spanning every medium, text, images, videos, and audio. The chances offered by generative AI models are endless and are drastically reducing the time required to generate content and usually raising the quality of the generation. However, considering the complexity and the difficult traceability of the generated content, the use of these tools provides challenges in attributing AI-generated content. The difficult attribution resides for a variety of reasons, starting from the lack of a systematic fingerprinting of the generated content and ending with the enormous amount of data on which LLMs and LMM are trained, which makes it difficult to connect generated content to the training data. This scenario is raising concerns about intellectual property and ethical responsibilities. To address these concerns, in this paper, we bridge the technological, ethical, and legislative aspects, by proposing a review of the legislative and technological instruments today available and proposing a legal framework to ensure accountability. In the end, we propose three use cases of how these can be combined to guarantee that attribution is respected. However, even though the techniques available today can guarantee a greater attribution to a greater extent, strong limitations still apply, that can be solved uniquely by the development of new attribution techniques, to be applied to LLMs and LMMs.
Who is Responsible When AI Fails? Mapping Causes, Entities, and Consequences of AI Privacy and Ethical Incidents
Authors: Hilda Hadan, Reza Hadi Mogavi, Leah Zhang-Kennedy, Lennart E. Nacke
Date: 2025-03-28 00:00:00

The rapid growth of artificial intelligence (AI) technologies has changed decision-making in many fields. But, it has also raised major privacy and ethical concerns. However, many AI incidents taxonomies and guidelines for academia, industry, and government lack grounding in real-world incidents. We analyzed 202 real-world AI privacy and ethical incidents. This produced a taxonomy that classifies incident types across AI lifecycle stages. It accounts for contextual factors such as causes, responsible entities, disclosure sources, and impacts. Our findings show insufficient incident reporting from AI developers and users. Many incidents are caused by poor organizational decisions and legal non-compliance. Only a few legal actions and corrective measures exist, while risk-mitigation efforts are limited. Our taxonomy contributes a structured approach in reporting of future AI incidents. Our findings demonstrate that current AI governance frameworks are inadequate. We urgently need child-specific protections and AI policies on social media. They must moderate and reduce the spread of harmful AI-generated content. Our research provides insights for policymakers and practitioners, which lets them design ethical AI. It also support AI incident detection and risk management. Finally, it guides AI policy development. Improved policies will protect people from harmful AI applications and support innovation in AI systems.
Four Things People Should Know About Migraines
Authors: Mohammad S. Parsa, Lukasz Golab
Date: 2025-03-26 00:00:00

Migraine literacy among the public is known to be low, and this lack of understanding has a negative impact on migraineurs' quality of life. To understand this impact, we use text mining methods to study migraine discussion on the Reddit social media platform. We summarize the findings in the form of "four things people should know about chronic migraines": it is a serious disease that affects people of all ages, it can be triggered by many different factors, it affects women more than men, and it can get worse in combination with the COVID-19 virus.