Tootfinder

The AI Ethical Resonance Hypothesis: The Possibility of Discovering Moral Meta-Patterns in AI Systems
Tomasz Zgliczy\'nski-Cuber
https://arxiv.org/abs/2507.11552

The AI Ethical Resonance Hypothesis: The Possibility of Discovering Moral Meta-Patterns in AI Systems
This paper presents a theoretical framework for the AI ethical resonance hypothesis, which proposes that advanced AI systems with purposefully designed cognitive structures ("ethical resonators") may emerge with the ability to identify subtle moral patterns that are invisible to the human mind. The paper explores the possibility that by processing and synthesizing large amounts of ethical contexts, AI systems may discover moral meta-patterns that transcend cultural, historical, and individual b…

Enterprise Security Incident Analysis and Countermeasures Based on the T-Mobile Data Breach
Zhuohan Cui, Zikun Song
https://arxiv.org/abs/2507.12937 https:…

Enterprise Security Incident Analysis and Countermeasures Based on the T-Mobile Data Breach
This paper presents a comprehensive analysis of T-Mobile's critical data breaches in 2021 and 2023, alongside a full-spectrum security audit targeting its systems, infrastructure, and publicly exposed endpoints. By combining case-based vulnerability assessments with active ethical hacking techniques--including Shodan reconnaissance, API misuse simulations, VNC brute-forcing, firmware reverse engineering, and web application scans--we uncover structural weaknesses persisting beyond the initial b…

Structured Moral Reasoning in Language Models: A Value-Grounded Evaluation Framework
Mohna Chakraborty, Lu Wang, David Jurgens
https://arxiv.org/abs/2506.14948

Structured Moral Reasoning in Language Models: A Value-Grounded Evaluation Framework
Large language models (LLMs) are increasingly deployed in domains requiring moral understanding, yet their reasoning often remains shallow, and misaligned with human reasoning. Unlike humans, whose moral reasoning integrates contextual trade-offs, value systems, and ethical theories, LLMs often rely on surface patterns, leading to biased decisions in morally and ethically complex scenarios. To address this gap, we present a value-grounded framework for evaluating and distilling structured moral…

"Mitigating Bias and Advocating for Data Sovereignty: The Role of Metadata and Paradata in Ethical #AI-Driven Information Systems" https://doi.org/10.1080/19386389.2025.2515766

Feeling Machines: Ethics, Culture, and the Rise of Emotional AI
Vivek Chavan, Arsen Cenaj, Shuyuan Shen, Ariane Bar, Srishti Binwani, Tommaso Del Becaro, Marius Funk, Lynn Greschner, Roberto Hung, Stina Klein, Romina Kleiner, Stefanie Krause, Sylwia Olbrych, Vishvapalsinhji Parmar, Jaleh Sarafraz, Daria Soroko, Daksitha Withanage Don, Chang Zhou, Hoang Thuy Duong Vu, Parastoo Semnani, Daniel Weinhardt, Elisabeth Andre, J\"org Kr\"uger, Xavier Fresquet

Feeling Machines: Ethics, Culture, and the Rise of Emotional AI
This paper explores the growing presence of emotionally responsive artificial intelligence through a critical and interdisciplinary lens. Bringing together the voices of early-career researchers from multiple fields, it explores how AI systems that simulate or interpret human emotions are reshaping our interactions in areas such as education, healthcare, mental health, caregiving, and digital life. The analysis is structured around four central themes: the ethical implications of emotional AI, …

Ethical AI: Towards Defining a Collective Evaluation Framework
Aasish Kumar Sharma, Dimitar Kyosev, Julian Kunkel
https://arxiv.org/abs/2506.00233 https://…

Ethical AI: Towards Defining a Collective Evaluation Framework
Artificial Intelligence (AI) is transforming sectors such as healthcare, finance, and autonomous systems, offering powerful tools for innovation. Yet its rapid integration raises urgent ethical concerns related to data ownership, privacy, and systemic bias. Issues like opaque decision-making, misleading outputs, and unfair treatment in high-stakes domains underscore the need for transparent and accountable AI systems. This article addresses these challenges by proposing a modular ethical assess…

Human-Machine Collaboration and Ethical Considerations in Adaptive Cyber-Physical Systems
Zoe Pfister
https://arxiv.org/abs/2507.02578 https://

Human-Machine Collaboration and Ethical Considerations in Adaptive Cyber-Physical Systems
Adaptive Cyber-Physical Systems (CPS) are systems that integrate both physical and computational capabilities, which can adjust in response to changing parameters. Furthermore, they increasingly incorporate human-machine collaboration, allowing them to benefit from the individual strengths of humans and machines. Human-Machine Teaming (HMT) represents the most advanced paradigm of human-machine collaboration, envisioning seamless teamwork between humans and machines. However, achieving effectiv…

MAMBO: High-Resolution Generative Approach for Mammography Images
Milica \v{S}kipina, Nikola Jovi\v{s}i\'c, Nicola Dall'Asen, Vanja \v{S}venda, Anil Osman Tur, Slobodan Ili\'c, Elisa Ricci, Dubravko \'Culibrk
https://arxiv.org/abs/2506.08677

MAMBO: High-Resolution Generative Approach for Mammography Images
Mammography is the gold standard for the detection and diagnosis of breast cancer. This procedure can be significantly enhanced with Artificial Intelligence (AI)-based software, which assists radiologists in identifying abnormalities. However, training AI systems requires large and diverse datasets, which are often difficult to obtain due to privacy and ethical constraints. To address this issue, the paper introduces MAMmography ensemBle mOdel (MAMBO), a novel patch-based diffusion approach des…

Model Cards Revisited: Bridging the Gap Between Theory and Practice for Ethical AI Requirements
Tim Puhlf\"ur{\ss}, Julia Butzke, Walid Maalej
https://arxiv.org/abs/2507.06014

Model Cards Revisited: Bridging the Gap Between Theory and Practice for Ethical AI Requirements
Model cards are the primary documentation framework for developers of artificial intelligence (AI) models to communicate critical information to their users. Those users are often developers themselves looking for relevant documentation to ensure that their AI systems comply with the ethical requirements of existing laws, guidelines, and standards. Recent studies indicate inadequate model documentation practices, suggesting a gap between AI requirements and current practices in model documentat…

Bridging Ethical Principles and Algorithmic Methods: An Alternative Approach for Assessing Trustworthiness in AI Systems
Michael Papademas, Xenia Ziouvelou, Antonis Troumpoukis, Vangelis Karkaletsis
https://arxiv.org/abs/2506.22774

Bridging Ethical Principles and Algorithmic Methods: An Alternative Approach for Assessing Trustworthiness in AI Systems
Artificial Intelligence (AI) technology epitomizes the complex challenges posed by human-made artifacts, particularly those widely integrated into society and exert significant influence, highlighting potential benefits and their negative consequences. While other technologies may also pose substantial risks, AI's pervasive reach makes its societal effects especially profound. The complexity of AI systems, coupled with their remarkable capabilities, can lead to a reliance on technologies that o…

Ethics by Design: A Lifecycle Framework for Trustworthy AI in Medical Imaging From Transparent Data Governance to Clinically Validated Deployment
Umer Sadiq Khan, Saif Ur Rehman Khan
https://arxiv.org/abs/2507.04249

Ethics by Design: A Lifecycle Framework for Trustworthy AI in Medical Imaging From Transparent Data Governance to Clinically Validated Deployment
The integration of artificial intelligence (AI) in medical imaging raises crucial ethical concerns at every stage of its development, from data collection to deployment. Addressing these concerns is essential for ensuring that AI systems are developed and implemented in a manner that respects patient rights and promotes fairness. This study aims to explore the ethical implications of AI in medical imaging, focusing on five key stages: data collection, data processing, model training, model eval…

AI Should Sense Better, Not Just Scale Bigger: Adaptive Sensing as a Paradigm Shift
Eunsu Baek, Keondo Park, Jeonggil Ko, Min-hwan Oh, Taesik Gong, Hyung-Sin Kim
https://arxiv.org/abs/2507.07820

AI Should Sense Better, Not Just Scale Bigger: Adaptive Sensing as a Paradigm Shift
Current AI advances largely rely on scaling neural models and expanding training datasets to achieve generalization and robustness. Despite notable successes, this paradigm incurs significant environmental, economic, and ethical costs, limiting sustainability and equitable access. Inspired by biological sensory systems, where adaptation occurs dynamically at the input (e.g., adjusting pupil size, refocusing vision)--we advocate for adaptive sensing as a necessary and foundational shift. Adaptiv…

Testing, Evaluation, Verification and Validation (TEVV) of Digital Twins: A Comprehensive Framework
Gabriella Waters
https://arxiv.org/abs/2507.04555 https…

Testing, Evaluation, Verification and Validation (TEVV) of Digital Twins: A Comprehensive Framework
Digital twins have emerged as a powerful technology for modeling and simulating complex systems across various domains (Fuller et al., 2020; Tao et al., 2019). As virtual representations of physical assets, processes, or systems, digital twins enable real-time monitoring, predictive analysis, and optimization. However, as digital twins become more sophisticated and integral to decision-making processes, ensuring their accuracy, reliability, and ethical implementation is essential. This paper pr…

A Fourier Explanation of AI-music Artifacts
Darius Afchar, Gabriel Meseguer-Brocal, Kamil Akesbi, Romain Hennequin
https://arxiv.org/abs/2506.19108 https:/…

A Fourier Explanation of AI-music Artifacts
The rapid rise of generative AI has transformed music creation, with millions of users engaging in AI-generated music. Despite its popularity, concerns regarding copyright infringement, job displacement, and ethical implications have led to growing scrutiny and legal challenges. In parallel, AI-detection services have emerged, yet these systems remain largely opaque and privately controlled, mirroring the very issues they aim to address. This paper explores the fundamental properties of synthet…

Can AI be Consentful?
Giada Pistilli, Bruna Trevelin
https://arxiv.org/abs/2507.01051 https://arxiv.org/pdf/2507.01051

Can AI be Consentful?
The evolution of generative AI systems exposes the challenges of traditional legal and ethical frameworks built around consent. This chapter examines how the conventional notion of consent, while fundamental to data protection and privacy rights, proves insufficient in addressing the implications of AI-generated content derived from personal data. Through legal and ethical analysis, we show that while individuals can consent to the initial use of their data for AI training, they cannot meaningf…

AI based Content Creation and Product Recommendation Applications in E-commerce: An Ethical overview
Aditi Madhusudan Jain, Ayush Jain
https://arxiv.org/abs/2506.17370

AI based Content Creation and Product Recommendation Applications in E-commerce: An Ethical overview
As e-commerce rapidly integrates artificial intelligence for content creation and product recommendations, these technologies offer significant benefits in personalization and efficiency. AI-driven systems automate product descriptions, generate dynamic advertisements, and deliver tailored recommendations based on consumer behavior, as seen in major platforms like Amazon and Shopify. However, the widespread use of AI in e-commerce raises crucial ethical challenges, particularly around data priv…

The Confidence Paradox: Can LLM Know When It's Wrong
Sahil Tripathi, Md Tabrez Nafis, Imran Hussain, Jiechao Gao
https://arxiv.org/abs/2506.23464 https…

The Confidence Paradox: Can LLM Know When It's Wrong
Document Visual Question Answering (DocVQA) systems are increasingly deployed in real world applications, yet they remain ethically opaque-often producing overconfident answers to ambiguous questions or failing to communicate uncertainty in a trustworthy manner. This misalignment between model confidence and actual knowledge poses significant risks, particularly in domains requiring ethical accountability. Existing approaches such as LayoutLMv3, UDOP, and DONUT have advanced SOTA performance by…

Will Agents Replace Us? Perceptions of Autonomous Multi-Agent AI
Nikola Balic
https://arxiv.org/abs/2506.02055 https://arxiv.org/pdf/…

Will Agents Replace Us? Perceptions of Autonomous Multi-Agent AI
Autonomous multi-agent AI systems are poised to transform various industries, particularly software development and knowledge work. Understanding current perceptions among professionals is crucial for anticipating adoption challenges, ethical considerations, and future workforce development. This study analyzes responses from 130 participants to a survey on the capabilities, impact, and governance of AI agents. We explore expected timelines for AI replacing programmers, identify perceived barri…

Toward Effective AI Governance: A Review of Principles
Danilo Ribeiro, Thayssa Rocha, Gustavo Pinto, Bruno Cartaxo, Marcelo Amaral, Nicole Davila, Ana Camargo
https://arxiv.org/abs/2505.23417

Toward Effective AI Governance: A Review of Principles
Artificial Intelligence (AI) governance is the practice of establishing frameworks, policies, and procedures to ensure the responsible, ethical, and safe development and deployment of AI systems. Although AI governance is a core pillar of Responsible AI, current literature still lacks synthesis across such governance frameworks and practices. Objective: To identify which frameworks, principles, mechanisms, and stakeholder roles are emphasized in secondary literature on AI governance. Method: We…

Software Fairness Testing in Practice
Ronnie de Souza Santos, Matheus de Morais Leca, Reydne Santos, Cleyton Magalhaes
https://arxiv.org/abs/2506.17095 htt…

Software Fairness Testing in Practice
Software testing ensures that a system functions correctly, meets specified requirements, and maintains high quality. As artificial intelligence and machine learning (ML) technologies become integral to software systems, testing has evolved to address their unique complexities. A critical advancement in this space is fairness testing, which identifies and mitigates biases in AI applications to promote ethical and equitable outcomes. Despite extensive academic research on fairness testing, inclu…