Tootfinder

Principles and Reasons Behind Automated Vehicle Decisions in Ethically Ambiguous Everyday Scenarios
Lucas Elbert Suryana, Simeon Calvert, Arkady Zgonnikov, Bart van Arem
https://arxiv.org/abs/2507.13837

Principles and Reasons Behind Automated Vehicle Decisions in Ethically Ambiguous Everyday Scenarios
Automated vehicles (AVs) increasingly encounter ethically ambiguous situations in everyday driving--scenarios involving conflicting human interests and lacking clearly optimal courses of action. While existing ethical models often focus on rare, high-stakes dilemmas (e.g., crash avoidance or trolley problems), routine decisions such as overtaking cyclists or navigating social interactions remain underexplored. This study addresses that gap by applying the tracking condition of Meaningful Human …

EMNLP: Educator-role Moral and Normative Large Language Models Profiling
Yilin Jiang, Mingzi Zhang, Sheng Jin, Zengyi Yu, Xiangjie Kong, Binghao Tu
https://arxiv.org/abs/2508.15250

EMNLP: Educator-role Moral and Normative Large Language Models Profiling
Simulating Professions (SP) enables Large Language Models (LLMs) to emulate professional roles. However, comprehensive psychological and ethical evaluation in these contexts remains lacking. This paper introduces EMNLP, an Educator-role Moral and Normative LLMs Profiling framework for personality profiling, moral development stage measurement, and ethical risk under soft prompt injection. EMNLP extends existing scales and constructs 88 teacher-specific moral dilemmas, enabling profession-orient…

Correct-By-Construction: Certified Individual Fairness through Neural Network Training
Ruihan Zhang, Jun Sun
https://arxiv.org/abs/2508.15642 https://arxiv…

Correct-By-Construction: Certified Individual Fairness through Neural Network Training
Fairness in machine learning is more important than ever as ethical concerns continue to grow. Individual fairness demands that individuals differing only in sensitive attributes receive the same outcomes. However, commonly used machine learning algorithms often fail to achieve such fairness. To improve individual fairness, various training methods have been developed, such as incorporating fairness constraints as optimisation objectives. While these methods have demonstrated empirical effectiv…

BadFU: Backdoor Federated Learning through Adversarial Machine Unlearning
Bingguang Lu, Hongsheng Hu, Yuantian Miao, Shaleeza Sohail, Chaoxiang He, Shuo Wang, Xiao Chen
https://arxiv.org/abs/2508.15541

BadFU: Backdoor Federated Learning through Adversarial Machine Unlearning
Federated learning (FL) has been widely adopted as a decentralized training paradigm that enables multiple clients to collaboratively learn a shared model without exposing their local data. As concerns over data privacy and regulatory compliance grow, machine unlearning, which aims to remove the influence of specific data from trained models, has become increasingly important in the federated setting to meet legal, ethical, or user-driven demands. However, integrating unlearning into FL introdu…

Beyond Ethical Alignment: Evaluating LLMs as Artificial Moral Assistants
Alessio Galatolo, Luca Alberto Rappuoli, Katie Winkle, Meriem Beloucif
https://arxiv.org/abs/2508.12754 …

Beyond Ethical Alignment: Evaluating LLMs as Artificial Moral Assistants
The recent rise in popularity of large language models (LLMs) has prompted considerable concerns about their moral capabilities. Although considerable effort has been dedicated to aligning LLMs with human moral values, existing benchmarks and evaluations remain largely superficial, typically measuring alignment based on final ethical verdicts rather than explicit moral reasoning. In response, this paper aims to advance the investigation of LLMs' moral capabilities by examining their capacity to…

An Integrated Target Study and Target Trial Framework to Evaluate Intervention Effects on Disparities
Xinyi Sun, Theodore J. Iwashyna, Emmanuel F. Drabo, Deidra C. Crews, Kadija Ferryman, John W. Jackson
https://arxiv.org/abs/2508.14690

An Integrated Target Study and Target Trial Framework to Evaluate Intervention Effects on Disparities
We present a novel framework -- the integrated Target Study + Target Trial (TS+TT) -- to evaluate the effects of interventions on disparities. This framework combines the ethical clarity of the Target Study, which balances allowable covariates across social groups to define meaningful disparity measures, with the causal rigor of the Target Trial, which emulates randomized trials to estimate intervention effects. TS+TT achieves two forms of balance: (1) stratified sampling ensures that allowable…

Culling Misinformation from Gen AI: Toward Ethical Curation and Refinement
Prerana Khatiwada, Grace Donaher, Jasymyn Navarro, Lokesh Bhatta
https://arxiv.org/abs/2507.14242

Culling Misinformation from Gen AI: Toward Ethical Curation and Refinement
While Artificial Intelligence (AI) is not a new field, recent developments, especially with the release of generative tools like ChatGPT, have brought it to the forefront of the minds of industry workers and academic folk alike. There is currently much talk about AI and its ability to reshape many everyday processes as we know them through automation. It also allows users to expand their ideas by suggesting things they may not have thought of on their own and provides easier access to informati…

Mind the Ethics! The Overlooked Ethical Dimensions of GenAI in Software Modeling Education
Shalini Chakraborty, Lola Burgue\~no, Nathalie Moreno, Javier Troya, Paula Mu\~noz
https://arxiv.org/abs/2509.13896

Mind the Ethics! The Overlooked Ethical Dimensions of GenAI in Software Modeling Education
Generative Artificial Intelligence (GenAI) is rapidly gaining momentum in software modeling education, embraced by both students and educators. As GenAI assists with interpreting requirements, formalizing models, and translating students' mental models into structured notations, it increasingly shapes core learning outcomes such as domain comprehension, diagrammatic thinking, and modeling fluency without clear ethical oversight or pedagogical guidelines. Yet, the ethical implications of this in…

The User-first Approach to AI Ethics: Preferences for Ethical Principles in AI Systems across Cultures and Contexts
Benjamin J. Carroll, Jianlong Zhou, Paul F. Burke, Sabine Ammon
https://arxiv.org/abs/2508.11327

The User-first Approach to AI Ethics: Preferences for Ethical Principles in AI Systems across Cultures and Contexts
As AI systems increasingly permeate everyday life, designers and developers face mounting pressure to balance innovation with ethical design choices. To date, the operationalisation of AI ethics has predominantly depended on frameworks that prescribe which ethical principles should be embedded within AI systems. However, the extent to which users value these principles remains largely unexplored in the existing literature. In a discrete choice experiment conducted in four countries, we quantify…

When AI Writes Back: Ethical Considerations by Physicians on AI-Drafted Patient Message Replies
Di Hu (University of California, Irvine, Irvine, CA, USA), Yawen Guo (University of California, Irvine, Irvine, CA, USA), Ha Na Cho (University of California, Irvine, Irvine, CA, USA), Emilie Chow (University of California, Irvine, Irvine, CA, USA), Dana B. Mukamel (University of California, Irvine, Irvine, CA, USA), Dara Sorkin (University of California, Irvine, Irvine, CA, USA), Andrew Rei…

When AI Writes Back: Ethical Considerations by Physicians on AI-Drafted Patient Message Replies
The increasing burden of responding to large volumes of patient messages has become a key factor contributing to physician burnout. Generative AI (GenAI) shows great promise to alleviate this burden by automatically drafting patient message replies. The ethical implications of this use have however not been fully explored. To address this knowledge gap, we conducted a semi-structured interview study with 21 physicians who participated in a GenAI pilot program. We found that notable ethical cons…