pwc-archive/papers-with-abstracts huggingface dataset:
Detecting Malicious AI Agents Through Simulated Interactions
Authors: Yulu Pi, Ella Bettison, Anna Becker
Date: 2025-03-31 00:00:00
Tasks: Decision Making, Experimental Design

This study investigates malicious AI Assistants' manipulative traits and whether the behaviours of malicious AI Assistants can be detected when interacting with human-like simulated users in various decision-making contexts. We also examine how interaction depth and ability of planning influence malicious AI Assistants' manipulative strategies and effectiveness. Using a controlled experimental design, we simulate interactions between AI Assistants (both benign and deliberately malicious) and users across eight decision-making scenarios of varying complexity and stakes. Our methodology employs two state-of-the-art language models to generate interaction data and implements Intent-Aware Prompting (IAP) to detect malicious AI Assistants. The findings reveal that malicious AI Assistants employ domain-specific persona-tailored manipulation strategies, exploiting simulated users' vulnerabilities and emotional triggers. In particular, simulated users demonstrate resistance to manipulation initially, but become increasingly vulnerable to malicious AI Assistants as the depth of the interaction increases, highlighting the significant risks associated with extended engagement with potentially manipulative systems. IAP detection methods achieve high precision with zero false positives but struggle to detect many malicious AI Assistants, resulting in high false negative rates. These findings underscore critical risks in human-AI interactions and highlight the need for robust, context-sensitive safeguards against manipulative AI behaviour in increasingly autonomous decision-support systems.
The Urban Impact of AI: Modeling Feedback Loops in Next-Venue Recommendation
Authors: Giovanni Mauro, Marco Minici, Luca Pappalardo
Date: 2025-04-10 00:00:00
Tasks: Recommendation Systems

Next-venue recommender systems are increasingly embedded in location-based services, shaping individual mobility decisions in urban environments. While their predictive accuracy has been extensively studied, less attention has been paid to their systemic impact on urban dynamics. In this work, we introduce a simulation framework to model the human-AI feedback loop underpinning next-venue recommendation, capturing how algorithmic suggestions influence individual behavior, which in turn reshapes the data used to retrain the models. Our simulations, grounded in real-world mobility data, systematically explore the effects of algorithmic adoption across a range of recommendation strategies. We find that while recommender systems consistently increase individual-level diversity in visited venues, they may simultaneously amplify collective inequality by concentrating visits on a limited subset of popular places. This divergence extends to the structure of social co-location networks, revealing broader implications for urban accessibility and spatial segregation. Our framework operationalizes the feedback loop in next-venue recommendation and offers a novel lens through which to assess the societal impact of AI-assisted mobility-providing a computational tool to anticipate future risks, evaluate regulatory interventions, and inform the design of ethic algorithmic systems.
Reinforcing Clinical Decision Support through Multi-Agent Systems and Ethical AI Governance
Authors: Ying-Jung Chen, Ahmad Albarqawi, Chi-Sheng Chen
Date: 2025-03-25 00:00:00
Tasks: Explainable artificial intelligence, Fairness, Mortality Prediction, Prediction

Recent advances in the data-driven medicine approach, which integrates ethically managed and explainable artificial intelligence into clinical decision support systems (CDSS), are critical to ensure reliable and effective patient care. This paper focuses on comparing novel agent system designs that use modular agents to analyze laboratory results, vital signs, and clinical context, and to predict and validate results. We implement our agent system with the eICU database, including running lab analysis, vitals-only interpreters, and contextual reasoners agents first, then sharing the memory into the integration agent, prediction agent, transparency agent, and a validation agent. Our results suggest that the multi-agent system (MAS) performed better than the single-agent system (SAS) with mortality prediction accuracy (59%, 56%) and the mean error for length of stay (LOS)(4.37 days, 5.82 days), respectively. However, the transparency score for the SAS (86.21) is slightly better than the transparency score for MAS (85.5). Finally, this study suggests that our agent-based framework not only improves process transparency and prediction accuracy but also strengthens trustworthy AI-assisted decision support in an intensive care setting.
Data over dialogue: Why artificial intelligence is unlikely to humanise medicine
Authors: Joshua Hatherley
Date: 2025-04-10 00:00:00

Recently, a growing number of experts in artificial intelligence (AI) and medicine have be-gun to suggest that the use of AI systems, particularly machine learning (ML) systems, is likely to humanise the practice of medicine by substantially improving the quality of clinician-patient relationships. In this thesis, however, I argue that medical ML systems are more likely to negatively impact these relationships than to improve them. In particular, I argue that the use of medical ML systems is likely to comprise the quality of trust, care, empathy, understanding, and communication between clinicians and patients.
"i am a stochastic parrot, and so r u": Is AI-based framing of human behaviour and cognition a conceptual metaphor or conceptual engineering?
Authors: Warmhold Jan Thomas Mollema, Thomas Wachter
Date: 2025-04-10 00:00:00
Tasks: Language Acquisition

Given the massive integration of AI technologies into our daily lives, AI-related concepts are being used to metaphorically compare AI systems with human behaviour and/or cognitive abilities like language acquisition. Rightfully, the epistemic success of these metaphorical comparisons should be debated. Against the backdrop of the conflicting positions of the 'computational' and 'meat' chauvinisms, we ask: can the conceptual constellation of the computational and AI be applied to the human domain and what does it mean to do so? What is one doing when the conceptual constellations of AI in particular are used in this fashion? Rooted in a Wittgensteinian view of concepts and language-use, we consider two possible answers and pit them against each other: either these examples are conceptual metaphors, or they are attempts at conceptual engineering. We argue that they are conceptual metaphors, but that (1) this position is unaware of its own epistemological contingency, and (2) it risks committing the ''map-territory fallacy''. Down at the conceptual foundations of computation, (3) it most importantly is a misleading 'double metaphor' because of the metaphorical connection between human psychology and computation. In response to the shortcomings of this projected conceptual organisation of AI onto the human domain, we argue that there is a semantic catch. The perspective of the conceptual metaphors shows avenues for forms of conceptual engineering. If this methodology's criteria are met, the fallacies and epistemic shortcomings related to the conceptual metaphor view can be bypassed. At its best, the cross-pollution of the human and AI conceptual domains is one that prompts us to reflect anew on how the boundaries of our current concepts serve us and how they could be approved.
Counting Hours, Counting Losses: The Toll of Unpredictable Work Schedules on Financial Security
Authors: Pegah Nokhiz, Aravinda Kanchana Ruwanpathirana, Aditya Bhaskara, Suresh Venkatasubramanian
Date: 2025-04-10 00:00:00

Financial instability has become a significant issue in today's society. While research typically focuses on financial aspects, there is a tendency to overlook time-related aspects of unstable work schedules. The inability to rely on consistent work schedules leads to burnout, work-family conflicts, and financial shocks that directly impact workers' income and assets. Unforeseen fluctuations in earnings pose challenges in financial planning, affecting decisions on savings and spending and ultimately undermining individuals' long-term financial stability and well-being. This issue is particularly evident in sectors where workers experience frequently changing schedules without sufficient notice, including those in the food service and retail sectors, part-time and hourly workers, and individuals with lower incomes. These groups are already more financially vulnerable, and the unpredictable nature of their schedules exacerbates their financial fragility. Our objective is to understand how unforeseen fluctuations in earnings exacerbate financial fragility by investigating the extent to which individuals' financial management depends on their ability to anticipate and plan for the future. To address this question, we develop a simulation framework that models how individuals optimize utility amidst financial uncertainty and the imperative to avoid financial ruin. We employ online learning techniques, specifically adapting workers' consumption policies based on evolving information about their work schedules. With this framework, we show both theoretically and empirically how a worker's capacity to anticipate schedule changes enhances their long-term utility. Conversely, the inability to predict future events can worsen workers' instability. Moreover, our framework enables us to explore interventions to mitigate the problem of schedule uncertainty and evaluate their effectiveness.
Synthesizing High-Quality Programming Tasks with LLM-based Expert and Student Agents
Authors: Manh Hung Nguyen, Victor-Alexandru Pădurean, Alkis Gotovos, Sebastian Tschiatschek, Adish Singla
Date: 2025-04-10 00:00:00

Generative AI is transforming computing education by enabling the automatic generation of personalized content and feedback. We investigate its capabilities in providing high-quality programming tasks to students. Despite promising advancements in task generation, a quality gap remains between AI-generated and expert-created tasks. The AI-generated tasks may not align with target programming concepts, could be incomprehensible for students to solve, or may contain critical issues such as incorrect tests. Existing works often require interventions from human teachers for validation. We address these challenges by introducing PyTaskSyn, a novel synthesis technique that first generates a programming task and then decides whether it meets certain quality criteria to be given to students. The key idea is to break this process into multiple stages performed by expert and student agents simulated using both strong and weaker generative models. Through extensive evaluation, we show that PyTaskSyn significantly improves task quality compared to baseline techniques and showcases the importance of each specialized agent type in our validation pipeline. Additionally, we conducted user studies using our publicly available web application and show that PyTaskSyn can deliver high-quality programming tasks comparable to expert-designed ones while reducing workload and costs, and being more engaging than programming tasks that are available in online resources.
A taxonomy of epistemic injustice in the context of AI and the case for generative hermeneutical erasure
Authors: Warmhold Jan Thomas Mollema
Date: 2025-04-10 00:00:00
Tasks: Philosophy

Whether related to machine learning models' epistemic opacity, algorithmic classification systems' discriminatory automation of testimonial prejudice, the distortion of human beliefs via the 'hallucinations' of generative AI, the inclusion of the global South in global AI governance, the execution of bureaucratic violence via algorithmic systems, or located in the interaction with conversational artificial agents epistemic injustice related to AI is a growing concern. Based on a proposed general taxonomy of epistemic injustice, this paper first sketches a taxonomy of the types of epistemic injustice in the context of AI, relying on the work of scholars from the fields of philosophy of technology, political philosophy and social epistemology. Secondly, an additional perspective on epistemic injustice in the context of AI: generative hermeneutical erasure. I argue that this injustice that can come about through the application of Large Language Models (LLMs) and contend that generative AI, when being deployed outside of its Western space of conception, can have effects of conceptual erasure, particularly in the epistemic domain, followed by forms of conceptual disruption caused by a mismatch between AI system and the interlocutor in terms of conceptual frameworks. AI systems' 'view from nowhere' epistemically inferiorizes non-Western epistemologies and thereby contributes to the erosion of their epistemic particulars, gradually contributing to hermeneutical erasure. This work's relevance lies in proposal of a taxonomy that allows epistemic injustices to be mapped in the AI domain and the proposal of a novel form of AI-related epistemic injustice.
Enhancements for Developing a Comprehensive AI Fairness Assessment Standard
Authors: Avinash Agarwal, Mayashankar Kumar, Manisha J. Nene
Date: 2025-04-10 00:00:00
Tasks: Decision Making, Fairness

As AI systems increasingly influence critical sectors like telecommunications, finance, healthcare, and public services, ensuring fairness in decision-making is essential to prevent biased or unjust outcomes that disproportionately affect vulnerable entities or result in adverse impacts. This need is particularly pressing as the industry approaches the 6G era, where AI will drive complex functions like autonomous network management and hyper-personalized services. The TEC Standard for Fairness Assessment and Rating of AI Systems provides guidelines for evaluating fairness in AI, focusing primarily on tabular data and supervised learning models. However, as AI applications diversify, this standard requires enhancement to strengthen its impact and broaden its applicability. This paper proposes an expansion of the TEC Standard to include fairness assessments for images, unstructured text, and generative AI, including large language models, ensuring a more comprehensive approach that keeps pace with evolving AI technologies. By incorporating these dimensions, the enhanced framework will promote responsible and trustworthy AI deployment across various sectors.
The Role of Machine Learning in Reducing Healthcare Costs: The Impact of Medication Adherence and Preventive Care on Hospitalization Expenses
Authors: Yixin Zhang, Yisong Chen
Date: 2025-04-10 00:00:00

This study reveals the important role of prevention care and medication adherence in reducing hospitalizations. By using a structured dataset of 1,171 patients, four machine learning models Logistic Regression, Gradient Boosting, Random Forest, and Artificial Neural Networks are applied to predict five-year hospitalization risk, with the Gradient Boosting model achieving the highest accuracy of 81.2%. The result demonstrated that patients with high medication adherence and consistent preventive care can reduce 38.3% and 37.7% in hospitalization risk. The finding also suggests that targeted preventive care can have positive Return on Investment (ROI), and therefore ML models can effectively direct personalized interventions and contribute to long-term medical savings.
Beyond Tools: Generative AI as Epistemic Infrastructure in Education
Authors: Bodong Chen
Date: 2025-04-09 00:00:00
Tasks: Sensitivity

As generative AI rapidly integrates into educational infrastructures worldwide, it transforms how knowledge gets created, validated, and shared, yet current discourse inadequately addresses its implications as epistemic infrastructure mediating teaching and learning. This paper investigates how AI systems function as epistemic infrastructures in education and their impact on human epistemic agency. Adopting a situated cognition perspective and following a value-sensitive design approach, the study conducts a technical investigation of two representative AI systems in educational settings, analyzing their impact on teacher practice across three dimensions: affordances for skilled epistemic actions, support for epistemic sensitivity, and implications for long-term habit formation. The analysis reveals that current AI systems inadequately support teachers' skilled epistemic actions, insufficiently foster epistemic sensitivity, and potentially cultivate problematic habits that prioritize efficiency over epistemic agency. To address these challenges, the paper recommends recognizing the infrastructural transformation occurring in education, developing AI environments that stimulate skilled actions while upholding epistemic norms, and involving educators in AI design processes -- recommendations aimed at fostering AI integration that aligns with core educational values and maintains human epistemic agency.
International Scientific Report on the Safety of Advanced AI (Interim Report)
Authors: Yoshua Bengio, Sören Mindermann, Daniel Privitera, Tamay Besiroglu, Rishi Bommasani, Stephen Casper, Yejin Choi, Danielle Goldfarb, Hoda Heidari, Leila Khalatbari, Shayne Longpre, Vasilios Mavroudis, Mantas Mazeika, Kwan Yee Ng, Chinasa T. Okolo, Deborah Raji, Theodora Skeadas, Florian Tramèr, Bayo Adekanmbi, Paul Christiano, David Dalrymple, Thomas G. Dietterich, Edward Felten, Pascale Fung, Pierre-Olivier Gourinchas, Nick Jennings, Andreas Krause, Percy Liang, Teresa Ludermir, Vidushi Marda, Helen Margetts, John A. McDermid, Arvind Narayanan, Alondra Nelson, Alice Oh, Gopal Ramchurn, Stuart Russell, Marietje Schaake, Dawn Song, Alvaro Soto, Lee Tiedrich, Gaël Varoquaux, Andrew Yao, Ya-Qin Zhang
Date: 2024-11-05 00:00:00

This is the interim publication of the first International Scientific Report on the Safety of Advanced AI. The report synthesises the scientific understanding of general-purpose AI -- AI that can perform a wide variety of tasks -- with a focus on understanding and managing its risks. A diverse group of 75 AI experts contributed to this report, including an international Expert Advisory Panel nominated by 30 countries, the EU, and the UN. Led by the Chair, these independent experts collectively had full discretion over the report's content. The final report is available at arXiv:2501.17805
Navigating the Rabbit Hole: Emergent Biases in LLM-Generated Attack Narratives Targeting Mental Health Groups
Authors: Rijul Magu, Arka Dutta, Sean Kim, Ashiqur R. KhudaBukhsh, Munmun De Choudhury
Date: 2025-04-08 00:00:00

Large Language Models (LLMs) have been shown to demonstrate imbalanced biases against certain groups. However, the study of unprovoked targeted attacks by LLMs towards at-risk populations remains underexplored. Our paper presents three novel contributions: (1) the explicit evaluation of LLM-generated attacks on highly vulnerable mental health groups; (2) a network-based framework to study the propagation of relative biases; and (3) an assessment of the relative degree of stigmatization that emerges from these attacks. Our analysis of a recently released large-scale bias audit dataset reveals that mental health entities occupy central positions within attack narrative networks, as revealed by a significantly higher mean centrality of closeness (p-value = 4.06e-10) and dense clustering (Gini coefficient = 0.7). Drawing from sociological foundations of stigmatization theory, our stigmatization analysis indicates increased labeling components for mental health disorder-related targets relative to initial targets in generation chains. Taken together, these insights shed light on the structural predilections of large language models to heighten harmful discourse and highlight the need for suitable approaches for mitigation.
Societal Impacts Research Requires Benchmarks for Creative Composition Tasks
Authors: Judy Hanwen Shen, Carlos Guestrin
Date: 2025-04-09 00:00:00
Tasks: Language Modeling, Language Modelling

Foundation models that are capable of automating cognitive tasks represent a pivotal technological shift, yet their societal implications remain unclear. These systems promise exciting advances, yet they also risk flooding our information ecosystem with formulaic, homogeneous, and potentially misleading synthetic content. Developing benchmarks grounded in real use cases where these risks are most significant is therefore critical. Through a thematic analysis using 2 million language model user prompts, we identify creative composition tasks as a prevalent usage category where users seek help with personal tasks that require everyday creativity. Our fine-grained analysis identifies mismatches between current benchmarks and usage patterns among these tasks. Crucially, we argue that the same use cases that currently lack thorough evaluations can lead to negative downstream impacts. This position paper argues that benchmarks focused on creative composition tasks is a necessary step towards understanding the societal harms of AI-generated content. We call for greater transparency in usage patterns to inform the development of new benchmarks that can effectively measure both the progress and the impacts of models with creative capabilities.
Assessing employment and labour issues implicated by using AI
Authors: Thijs Willems, Darion Jin Hotan, Jiawen Cheryl Tang, Norakmal Hakim bin Norhashim, King Wang Poon, Zi An Galvyn Goh, Radha Vinod
Date: 2025-04-08 00:00:00

This chapter critiques the dominant reductionist approach in AI and work studies, which isolates tasks and skills as replaceable components. Instead, it advocates for a systemic perspective that emphasizes the interdependence of tasks, roles, and workplace contexts. Two complementary approaches are proposed: an ethnographic, context-rich method that highlights how AI reconfigures work environments and expertise; and a relational task-based analysis that bridges micro-level work descriptions with macro-level labor trends. The authors argue that effective AI impact assessments must go beyond predicting automation rates to include ethical, well-being, and expertise-related questions. Drawing on empirical case studies, they demonstrate how AI reshapes human-technology relations, professional roles, and tacit knowledge practices. The chapter concludes by calling for a human-centric, holistic framework that guides organizational and policy decisions, balancing technological possibilities with social desirability and sustainability of work.
Text-to-Image Models and Their Representation of People from Different Nationalities Engaging in Activities
Authors: Abdulkareem Alsudais
Date: 2025-04-08 00:00:00

This paper investigates how a popular Text-to-Image (T2I) model represents people from 208 different nationalities when prompted to generate images of individuals engaging in typical activities. Two scenarios were developed, and 644 images were generated based on input prompts that specified nationalities. The results show that in one scenario, 52.88% of images, and in the other, 27.4%, depict individuals wearing traditional attire. A statistically significant relationship was observed between this representation pattern and regions. This indicates that the issue disproportionately affects certain areas, particularly the Middle East & North Africa and Sub-Saharan Africa. A notable association with income groups was also found. CLIP, ALIGN, and GPT-4.1 mini were used to measure alignment scores between generated images and 3320 prompts and captions, with findings indicating statistically significant higher scores for images featuring individuals in traditional attire in one scenario. The study also examined revised prompts, finding that the word "traditional" was added by the model to 88.46% of prompts for one scenario. These findings provide valuable insights into T2I models' representation of individuals across different countries, demonstrating how the examined model prioritizes traditional characteristics despite their impracticality for the given activities.