Tootfinder

Worker Discretion Advised: Co-designing Risk Disclosure in Crowdsourced Responsible AI (RAI) Content Work
Alice Qian, Ziqi Yang, Ryland Shaw, Jina Suh, Laura Dabbish, Hong Shen
https://arxiv.org/abs/2509.12140

Worker Discretion Advised: Co-designing Risk Disclosure in Crowdsourced Responsible AI (RAI) Content Work
Responsible AI (RAI) content work, such as annotation, moderation, or red teaming for AI safety, often exposes crowd workers to potentially harmful content. While prior work has underscored the importance of communicating well-being risk to employed content moderators, designing effective disclosure mechanisms for crowd workers while balancing worker protection with the needs of task designers and platforms remains largely unexamined. To address this gap, we conducted co-design sessions with 29…

AI Harmonics: a human-centric and harms severity-adaptive AI risk assessment framework
Sofia Vei, Paolo Giudici, Pavlos Sermpezis, Athena Vakali, Adelaide Emma Bernardelli
https://arxiv.org/abs/2509.10104

AI Harmonics: a human-centric and harms severity-adaptive AI risk assessment framework
The absolute dominance of Artificial Intelligence (AI) introduces unprecedented societal harms and risks. Existing AI risk assessment models focus on internal compliance, often neglecting diverse stakeholder perspectives and real-world consequences. We propose a paradigm shift to a human-centric, harm-severity adaptive approach grounded in empirical incident data. We present AI Harmonics, which includes a novel AI harm assessment metric (AIH) that leverages ordinal severity data to capture rela…

Analyzing Information-Seeking Behaviors in a Hakka AI Chatbot: A Cognitive-Pragmatic Study
Chu-Hsuan Lee, Chen-Chi Chang, Hung-Shin Lee, Yun-Hsiang Hsu, Ching-Yuan Chen
https://arxiv.org/abs/2509.11591

Analyzing Information-Seeking Behaviors in a Hakka AI Chatbot: A Cognitive-Pragmatic Study
With many endangered languages at risk of disappearing, efforts to preserve them now rely more than ever on using technology alongside culturally informed teaching strategies. This study examines user behaviors in TALKA, a generative AI-powered chatbot designed for Hakka language engagement, by employing a dual-layered analytical framework grounded in Bloom's Taxonomy of cognitive processes and dialogue act categorization. We analyzed 7,077 user utterances, each carefully annotated according to…

Bengaluru-based FinBox, which provides B2B digital lending infrastructure, risk intelligence, and orchestration tools, raised a $40M Series B led by WestBridge (The Economic Times)
https://economictimes.indiati…

Digital credit infra startup FinBox raises $40 million from WestBridge Capital, others
FinBox, a digital credit infrastructure platform, secured $40 million in a funding round led by WestBridge Capital, with participation from A91 Partners and Aditya Birla Ventures. The company intends to utilise the funds to develop AI-driven solutions and expand its product offerings. Founded in 2017, FinBox enables enterprises and financial institutions to integrate financial services into their ecosystems.

Securing Agentic AI: Threat Modeling and Risk Analysis for Network Monitoring Agentic AI System
Pallavi Zambare, Venkata Nikhil Thanikella, Ying Liu
https://arxiv.org/abs/2508.10043

Securing Agentic AI: Threat Modeling and Risk Analysis for Network Monitoring Agentic AI System
When combining Large Language Models (LLMs) with autonomous agents, used in network monitoring and decision-making systems, this will create serious security issues. In this research, the MAESTRO framework consisting of the seven layers threat modeling architecture in the system was used to expose, evaluate, and eliminate vulnerabilities of agentic AI. The prototype agent system was constructed and implemented, using Python, LangChain, and telemetry in WebSockets, and deployed with inference, m…

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[11/11]:
- Deceptive Risk Minimization: Out-of-Distribution Generalization by Deceiving Distribution Shift D...
Anirudha Majumdar

“Personality vs. Personalization” in AI Systems: Responsible Design and Risk Management (Part 4)
https://fpf.org/blog/personality-vs-personalization-in-ai-systems-responsible-design-and-risk-management-part-4/

LinkedIn notifications.
(I’d rather my air carrier not be using AI but here we are.)

From Hugging Face to GitHub: Tracing License Drift in the Open-Source AI Ecosystem
James Jewitt, Hao Li, Bram Adams, Gopi Krishnan Rajbahadur, Ahmed E. Hassan
https://arxiv.org/abs/2509.09873

From Hugging Face to GitHub: Tracing License Drift in the Open-Source AI Ecosystem
Hidden license conflicts in the open-source AI ecosystem pose serious legal and ethical risks, exposing organizations to potential litigation and users to undisclosed risk. However, the field lacks a data-driven understanding of how frequently these conflicts occur, where they originate, and which communities are most affected. We present the first end-to-end audit of licenses for datasets and models on Hugging Face, as well as their downstream integration into open-source software applications…

DeltaHedge: A Multi-Agent Framework for Portfolio Options Optimization
Feliks Ba\'nka (Warsaw University of Technology, Faculty of Electronics,Information Technology), Jaros{\l}aw A. Chudziak (Warsaw University of Technology)
https://arxiv.org/abs/2509.12753

DeltaHedge: A Multi-Agent Framework for Portfolio Options Optimization
In volatile financial markets, balancing risk and return remains a significant challenge. Traditional approaches often focus solely on equity allocation, overlooking the strategic advantages of options trading for dynamic risk hedging. This work presents DeltaHedge, a multi-agent framework that integrates options trading with AI-driven portfolio management. By combining advanced reinforcement learning techniques with an ensembled options-based hedging strategy, DeltaHedge enhances risk-adjusted…

GiveDirectly plans to pilot a program using Google's AI-based Flood Hub, which provides forecast warnings, to send early aid to at-risk families in Bangladesh (Rest of World)
https://restofworld.org/2025/google-flood-hub-cash-aid/

AI flood forecasting allows aid to reach farmers before disaster strikes
Using Google’s Flood Hub, nonprofit trial program sends early warning and unconditional cash to workers.

AI and Human Oversight: A Risk-Based Framework for Alignment
Laxmiraju Kandikatla, Branislav Radeljic
https://arxiv.org/abs/2510.09090 https://arxiv.org/pd…

AI and Human Oversight: A Risk-Based Framework for Alignment
As Artificial Intelligence (AI) technologies continue to advance, protecting human autonomy and promoting ethical decision-making are essential to fostering trust and accountability. Human agency (the capacity of individuals to make informed decisions) should be actively preserved and reinforced by AI systems. This paper examines strategies for designing AI systems that uphold fundamental rights, strengthen human agency, and embed effective human oversight mechanisms. It discusses key oversight…

US embeds trackers in AI chip shipments to catch diversions to China, sources say
U.S. authorities have secretly placed location tracking devices in targeted shipments of advanced chips they see as being at high risk of illegal diversion to China, according to two people with direct knowledge of the previously unreported law enforcement tactic.
🕵️

Trading-R1: Financial Trading with LLM Reasoning via Reinforcement Learning
Yijia Xiao, Edward Sun, Tong Chen, Fang Wu, Di Luo, Wei Wang
https://arxiv.org/abs/2509.11420 https:/…

Trading-R1: Financial Trading with LLM Reasoning via Reinforcement Learning
Developing professional, structured reasoning on par with human financial analysts and traders remains a central challenge in AI for finance, where markets demand interpretability and trust. Traditional time-series models lack explainability, while LLMs face challenges in turning natural-language analysis into disciplined, executable trades. Although reasoning LLMs have advanced in step-by-step planning and verification, their application to risk-sensitive financial decisions is underexplored. …

“It is acceptable to engage a child in conversations that are romantic or sensual,” according to Meta’s “GenAI: Content Risk Standards.”
https://www.reuters.com/investigates/special-report/meta-ai-chatbot-death/

Robo-Advisors Beyond Automation: Principles and Roadmap for AI-Driven Financial Planning
Runhuan Feng, Hong Li, Ming Liu
https://arxiv.org/abs/2509.09922 https://

Robo-Advisors Beyond Automation: Principles and Roadmap for AI-Driven Financial Planning
Artificial intelligence (AI) is transforming financial planning by expanding access, lowering costs, and enabling dynamic, data-driven advice. Yet without clear safeguards, digital platforms risk reproducing longstanding market inefficiencies such as information asymmetry, misaligned incentives, and systemic fragility. This paper develops a framework for responsible AI in financial planning, anchored in five principles: fiduciary duty, adaptive personalization, technical robustness, ethical and…

Summarised by my partner as "The bank of England is saying WATCH OUT CHAPS"...
https://www.theguardian.com/business/2025/oct/08/bank-of-england-warns-of-growing-risk-that-ai-bubble-could-burst

A Dataset for Distilling Knowledge Priors from Literature for Therapeutic Design
Haydn Thomas Jones, Natalie Maus, Josh Magnus Ludan, Maggie Ziyu Huan, Jiaming Liang, Marcelo Der Torossian Torres, Jiatao Liang, Zachary Ives, Yoseph Barash, Cesar de la Fuente-Nunez, Jacob R. Gardner, Mark Yatskar
https://arxiv.org/abs/2508.10899

A Dataset for Distilling Knowledge Priors from Literature for Therapeutic Design
AI-driven discovery can greatly reduce design time and enhance new therapeutics' effectiveness. Models using simulators explore broad design spaces but risk violating implicit constraints due to a lack of experimental priors. For example, in a new analysis we performed on a diverse set of models on the GuacaMol benchmark using supervised classifiers, over 60\% of molecules proposed had high probability of being mutagenic. In this work, we introduce \ourdataset, a dataset of priors for design pr…

https://www.bbc.com/news/articles/c24zz2vdv51o
Another science institution at risk due to anti-science politicians threatening to pull funding.

Staff fear UK's Turing AI Institute at risk of collapse
A complaint raises concerns about the funding risks and a "toxic" culture at the Alan Turing Institute.

Google's New AI Health Tool: Opt in or Risk Losing Benefits - Business Insider
https://www.businessinsider.com/google-ai-health-tool-opt-in-risk-losing-benefits-2025-10

Emulating Public Opinion: A Proof-of-Concept of AI-Generated Synthetic Survey Responses for the Chilean Case
Basti\'an Gonz\'alez-Bustamante, Nando Verelst, Carla Cisternas
https://arxiv.org/abs/2509.09871

Emulating Public Opinion: A Proof-of-Concept of AI-Generated Synthetic Survey Responses for the Chilean Case
Large Language Models (LLMs) offer promising avenues for methodological and applied innovations in survey research by using synthetic respondents to emulate human answers and behaviour, potentially mitigating measurement and representation errors. However, the extent to which LLMs recover aggregate item distributions remains uncertain and downstream applications risk reproducing social stereotypes and biases inherited from training data. We evaluate the reliability of LLM-generated synthetic su…

Modeling Human Responses to Multimodal AI Content
Zhiqi Shen, Shaojing Fan, Danni Xu, Terence Sim, Mohan Kankanhalli
https://arxiv.org/abs/2508.10769 https://

Modeling Human Responses to Multimodal AI Content
As AI-generated content becomes widespread, so does the risk of misinformation. While prior research has primarily focused on identifying whether content is authentic, much less is known about how such content influences human perception and behavior. In domains like trading or the stock market, predicting how people react (e.g., whether a news post will go viral), can be more critical than verifying its factual accuracy. To address this, we take a human-centered approach and introduce the MhAI…

Based AI improves human decision-making but reduces trust
Shiyang Lai, Junsol Kim, Nadav Kunievsky, Yujin Potter, James Evans
https://arxiv.org/abs/2508.09297 https://

Based AI improves human decision-making but reduces trust
Current AI systems minimize risk by enforcing ideological neutrality, yet this may introduce automation bias by suppressing cognitive engagement in human decision-making. We conducted randomized trials with 2,500 participants to test whether culturally biased AI enhances human decision-making. Participants interacted with politically diverse GPT-4o variants on information evaluation tasks. Partisan AI assistants enhanced human performance, increased engagement, and reduced evaluative bias compa…

AI, AGI, and learning efficiency
An addendum to this: I'm someone who would accurately be called "anti-AI" in the modern age, yet I'm also an "AI researcher" in some ways (have only dabbled in neutral nets).
I don't like:
- AI systems that are the product of labor abuses towards the data workers who curate their training corpora.
- AI systems that use inordinate amounts of water and energy during an intensifying climate catastrophe.
- AI systems that are fundamentally untrustworthy and which reinforce and amplify human biases, *especially* when those systems are exposed in a way that invites harms.
- AI systems which are designed to "save" my attention or brain bandwidth but such my doing so cripple my understating of the things I might use them for when I fact that understanding was the thing I was supposed to be using my time to gain, and where the later lack of such understanding will be costly to me.
- AI systems that are designed by and whose hype fattens the purse of people who materially support genocide and the construction of concentration campus (a.k.a. fascists).
In other words, I do not like and except in very extenuating circumstances I will not use ChatGPT, Claude, Copilot, Gemini, etc.
On the other hand, I do like:
- AI research as an endeavor to discover new technologies.
- Generative AI as a research topic using a spectrum of different methods.
- Speculating about non-human intelligences, including artificial ones, and including how to behave ethically towards them.
- Large language models as a specific technique, and autoencoders and other neural networks, assuming they're used responsibly in terms of both resource costs & presentation to end users.
I write this because I think some people (especially folks without CS backgrounds) may feel that opposing AI for all the harms it's causing runs the risk of opposing technological innovation more broadly, and/or may feel there's a risk that they will be "left behind" as everyone else embraces the hype and these technologies inevitability become ubiquitous and essential (I know I feel this way sometimes). Just know that is entirely possible and logically consistent to both oppose many forms of modern AI while also embracing and even being optimistic about AI research, and that while LLMs are currently all the rage, they're not the endpoint of what AI will look like in the future, and their downsides are not inherent in AI development.

Uncertainty-Aware, Risk-Adaptive Access Control for Agentic Systems using an LLM-Judged TBAC Model
Charles Fleming, Ashish Kundu, Ramana Kompella
https://arxiv.org/abs/2510.11414

Uncertainty-Aware, Risk-Adaptive Access Control for Agentic Systems using an LLM-Judged TBAC Model
The proliferation of autonomous AI agents within enterprise environments introduces a critical security challenge: managing access control for emergent, novel tasks for which no predefined policies exist. This paper introduces an advanced security framework that extends the Task-Based Access Control (TBAC) model by using a Large Language Model (LLM) as an autonomous, risk-aware judge. This model makes access control decisions not only based on an agent's intent but also by explicitly considerin…

How some news organizations are using AI models powered by retrieval-augmented generation to surface the most newsworthy elements from very large datasets (Josh Axelrod/Nieman Lab)
https://www.niemanlab.org/2025/08/the-good

The good, the bad, and the completely made-up: Newsrooms on wrestling accurate answers out of AI
"If we sit on the sideline and observe, I think the risk is too high that we are gonna be left behind."

Bias-Aware AI Chatbot for Engineering Advising at the University of Maryland A. James Clark School of Engineering
Prarthana P. Kartholy, Thandi M. Labor, Neil N. Panchal, Sean H. Wang, Hillary N. Owusu
https://arxiv.org/abs/2510.09636

Bias-Aware AI Chatbot for Engineering Advising at the University of Maryland A. James Clark School of Engineering
Selecting a college major is a difficult decision for many incoming freshmen. Traditional academic advising is often hindered by long wait times, intimidating environments, and limited personalization. AI Chatbots present an opportunity to address these challenges. However, AI systems also have the potential to generate biased responses, prejudices related to race, gender, socioeconomic status, and disability. These biases risk turning away potential students and undermining reliability of AI s…

«75% of subjects were expected to use AI at work. 22% felt pressured to use AI when it was not appropriate. So 16% of subjects just say they used the AI when they didn’t! People feel they would be putting their job at risk if they pushed back on AI directives.»
Wow, "my manager would rather believe my work came from the garbage generator" must make any bullshit job even more bullshit.
https://pivot-to-ai.com/2025/07/25/16-of-employees-pretend-to-use-ai-at-work-to-please-their-boss/

Need to talk to your students about AI ethics this fall? I've uploaded a course module for the AI IMPACT RISK framework, with interactive website, video, infographics, and quiz, that you can import into your own courseware
This imscc file can be imported directly into Canvas/Brightspace/Blackboard, or you can cherrypick resources directly from the IMPACT RISK website. Also new: SVG versions of all graphics

Wild Refitting for Model-Free Excess Risk Evaluation of Opaque ML/AI Models under Bregman Loss
Haichen Hu, David Simchi-Levi
https://arxiv.org/abs/2509.02476 https://

Wild Refitting for Model-Free Excess Risk Evaluation of Opaque ML/AI Models under Bregman Loss
We study the problem of evaluating the excess risk of classical penalized empirical risk minimization (ERM) with Bregman losses. We show that by leveraging the recently proposed wild refitting procedure (Wainwright, 2025), one can efficiently upper bound the excess risk through the so-called "wild optimism," without relying on the global structure of the underlying function class. This property makes our approach inherently model-free. Unlike conventional analyses, our framework operates with j…

Ethical AI prompt recommendations in large language models using collaborative filtering
Jordan Nelson, Almas Baimagambetov, Konstantinos Avgerinakis, Nikolaos Polatidis
https://arxiv.org/abs/2510.06924

Ethical AI prompt recommendations in large language models using collaborative filtering
As large language models (LLMs) shape AI development, ensuring ethical prompt recommendations is crucial. LLMs offer innovation but risk bias, fairness issues, and accountability concerns. Traditional oversight methods struggle with scalability, necessitating dynamic solutions. This paper proposes using collaborative filtering, a technique from recommendation systems, to enhance ethical prompt selection. By leveraging user interactions, it promotes ethical guidelines while reducing bias. Contri…

Can Risk-taking AI-Assistants suitably represent entities
Ali Mazyaki, Mohammad Naghizadeh, Samaneh Ranjkhah Zonouzaghi, Amirhossein Farshi Sotoudeh
https://arxiv.org/abs/2510.08114

Can Risk-taking AI-Assistants suitably represent entities
Responsible AI demands systems whose behavioral tendencies can be effectively measured, audited, and adjusted to prevent inadvertently nudging users toward risky decisions or embedding hidden biases in risk aversion. As language models (LMs) are increasingly incorporated into AI-driven decision support systems, understanding their risk behaviors is crucial for their responsible deployment. This study investigates the manipulability of risk aversion (MoRA) in LMs, examining their ability to repl…

Risk Analysis Techniques for Governed LLM-based Multi-Agent Systems
Alistair Reid, Simon O'Callaghan, Liam Carroll, Tiberio Caetano
https://arxiv.org/abs/2508.05687 https://…

Risk Analysis Techniques for Governed LLM-based Multi-Agent Systems
Organisations are starting to adopt LLM-based AI agents, with their deployments naturally evolving from single agents towards interconnected, multi-agent networks. Yet a collection of safe agents does not guarantee a safe collection of agents, as interactions between agents over time create emergent behaviours and induce novel failure modes. This means multi-agent systems require a fundamentally different risk analysis approach than that used for a single agent. This report addresses the earl…

Governing AI R&D: A Legal Framework for Constraining Dangerous AI
Alex Mark, Aaron Scher
https://arxiv.org/abs/2509.05361 https://arxiv.org/pdf/2509.05…

Governing AI R&D: A Legal Framework for Constraining Dangerous AI
As AI advances, governing its development may become paramount to public safety. Lawmakers may seek to restrict the development and release of AI models or of AI research itself. These governance actions could trigger legal challenges that invalidate the actions, so lawmakers should consider these challenges ahead of time. We investigate three classes of potential litigation risk for AI regulation in the U.S.: the First Amendment, administrative law, and the Fourteenth Amendment. We discuss exi…

Towards Early Detection: AI-Based Five-Year Forecasting of Breast Cancer Risk Using Digital Breast Tomosynthesis Imaging
Manon A. Dorster, Felix J. Dorfner, Mason C. Cleveland, Melisa S. Guelen, Jay Patel, Dania Daye, Jean-Philippe Thiran, Albert E. Kim, Christopher P. Bridge
https://arxiv.org/abs/2509.00900

Towards Early Detection: AI-Based Five-Year Forecasting of Breast Cancer Risk Using Digital Breast Tomosynthesis Imaging
As early detection of breast cancer strongly favors successful therapeutic outcomes, there is major commercial interest in optimizing breast cancer screening. However, current risk prediction models achieve modest performance and do not incorporate digital breast tomosynthesis (DBT) imaging, which was FDA-approved for breast cancer screening in 2011. To address this unmet need, we present a deep learning (DL)-based framework capable of forecasting an individual patient's 5-year breast cancer ri…

This is of course very bad, but I also found it funny. AI evangelists are suddenly privacy-aware and repeating this as a security risk. And it is. But the idea that using ChatGPT itself hasn’t always been a security risk is ridiculous to me.

TLDR: If you’ve ever used the share function on a ChatGPT chat, that full chat can be found via Google, viewable for anyone in the world.

https:…

Investors are increasingly divesting from companies they fear are at risk of AI disruption, like Wix and Shutterstock, which are down at least 30% in 2025 (Bloomberg)
https://www.bloomberg.com/news/articles/2025-08-09/traders-ar…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[4/4]:
- Beyond the Pre-Service Horizon: Infusing In-Service Behavior for Improved Financial Risk Forecasting
Senhao Liu, Zhiyu Guo, Zhiyuan Ji, Yueguo Chen, Yateng Tang, Yunhai Wang, Xuehao Zheng, Xiang Ao

Effect of AI Performance, Risk Perception, and Trust on Human Dependence in Deepfake Detection AI system
Yingfan Zhou, Ester Chen, Manasa Pisipati, Aiping Xiong, Sarah Rajtmajer
https://arxiv.org/abs/2508.01906

Effect of AI Performance, Risk Perception, and Trust on Human Dependence in Deepfake Detection AI system
Synthetic images, audio, and video can now be generated and edited by Artificial Intelligence (AI). In particular, the malicious use of synthetic data has raised concerns about potential harms to cybersecurity, personal privacy, and public trust. Although AI-based detection tools exist to help identify synthetic content, their limitations often lead to user mistrust and confusion between real and fake content. This study examines the role of AI performance in influencing human trust and decisio…

EU Digital Regulation and Guatemala: AI, 5G, and Cybersecurity
Victor Lopez Juarez
https://arxiv.org/abs/2508.08315 https://arxiv.org/pdf/2508.08315…

EU Digital Regulation and Guatemala: AI, 5G, and Cybersecurity
The paper examines how EU rules in AI, 5G, and cybersecurity operate as transnational governance and shape policy in Guatemala. It outlines the AI Act's risk approach, the 5G Action Plan and Security Toolbox, and the cybersecurity regime built on ENISA, NIS2, the Cybersecurity Act, and the Cyber Resilience Act. It traces extraterritorial channels such as the Brussels effect, private standards, supply chain clauses, and data transfer controls. Guatemala specific impacts include SME compliance co…

Replaced article(s) found for q-fin.PM. https://arxiv.org/list/q-fin.PM/new
[1/1]:
- Increase Alpha: Performance and Risk of an AI-Driven Trading Framework
Sid Ghatak, Arman Khaledian, Navid Parvini, Nariman Khaledian

Google DeepMind updates its Frontier Safety Framework to account for new risks, including the potential for models to resist shutdown or modification by humans (Ina Fried/Axios)
https://www.axios.com/2025/09/22/google-ai-risk-models-resist-shutdown

Google AI risk document spotlights risk of models resisting shutdown
Some advanced AI models have plotted or deceived users in order to achieve their goals.

We don't need AI to produce nonsensical gibberish. Humans are exceedingly good at that all on their own.

Evaluating Generative AI Tools for Personalized Offline Recommendations: A Comparative Study
Rafael Salinas-Buestan, Otto Parra, Nelly Condori-Fernandez, Maria Fernanda Granda
https://arxiv.org/abs/2508.03710

Evaluating Generative AI Tools for Personalized Offline Recommendations: A Comparative Study
Background: Generative AI tools have become increasingly relevant in supporting personalized recommendations across various domains. However, their effectiveness in health-related behavioral interventions, especially those aiming to reduce the use of technology, remains underexplored. Aims: This study evaluates the performance and user satisfaction of the five most widely used generative AI tools when recommending non-digital activities tailored to individuals at risk of repetitive strain injur…

Incorporating AI Incident Reporting into Telecommunications Law and Policy: Insights from India
Avinash Agarwal, Manisha J. Nene
https://arxiv.org/abs/2509.09508 https://…

Incorporating AI Incident Reporting into Telecommunications Law and Policy: Insights from India
The integration of artificial intelligence (AI) into telecommunications infrastructure introduces novel risks, such as algorithmic bias and unpredictable system behavior, that fall outside the scope of traditional cybersecurity and data protection frameworks. This paper introduces a precise definition and a detailed typology of telecommunications AI incidents, establishing them as a distinct category of risk that extends beyond conventional cybersecurity and data protection breaches. It argues …

Responsible AI Technical Report
KT, :, Soonmin Bae, Wanjin Park, Jeongyeop Kim, Yunjin Park, Jungwon Yoon, Junhyung Moon, Myunggyo Oh, Wonhyuk Lee, Junseo Jang, Dongyoung Jung, Minwook Ju, Eunmi Kim, Sujin Kim, Youngchol Kim, Somin Lee, Wonyoung Lee, Minsung Noh, Hyoungjun Park, Eunyoung Shin
https://arxiv.org/abs/2509.20057

Responsible AI Technical Report
KT developed a Responsible AI (RAI) assessment methodology and risk mitigation technologies to ensure the safety and reliability of AI services. By analyzing the Basic Act on AI implementation and global AI governance trends, we established a unique approach for regulatory compliance and systematically identify and manage all potential risk factors from AI development to operation. We present a reliable assessment methodology that systematically verifies model safety and robustness based on KT'…

CORTEX: Composite Overlay for Risk Tiering and Exposure in Operational AI Systems
Aoun E Muhammad, Kin Choong Yow, Jamel Baili, Yongwon Cho, Yunyoung Nam
https://arxiv.org/abs/2508.19281

CORTEX: Composite Overlay for Risk Tiering and Exposure in Operational AI Systems
As the deployment of Artificial Intelligence (AI) systems in high-stakes sectors - like healthcare, finance, education, justice, and infrastructure has increased - the possibility and impact of failures of these systems have significantly evolved from being a theoretical possibility to practical recurring, systemic risk. This paper introduces CORTEX (Composite Overlay for Risk Tiering and Exposure), a multi-layered risk scoring framework proposed to assess and score AI system vulnerabilities, d…

Dimensional Characterization and Pathway Modeling for Catastrophic AI Risks
Ze Shen Chin
https://arxiv.org/abs/2508.06411 https://arxiv.org/pdf/2508.06411

Dimensional Characterization and Pathway Modeling for Catastrophic AI Risks
Although discourse around the risks of Artificial Intelligence (AI) has grown, it often lacks a comprehensive, multidimensional framework, and concrete causal pathways mapping hazard to harm. This paper aims to bridge this gap by examining six commonly discussed AI catastrophic risks: CBRN, cyber offense, sudden loss of control, gradual loss of control, environmental risk, and geopolitical risk. First, we characterize these risks across seven key dimensions, namely intent, competency, entity, p…

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[3/8]:
- Leveraging Large Language Models for Cybersecurity Risk Assessment -- A Case from Forestry Cyber-...
Fikret Mert G\"ultekin, Oscar Lilja, Ranim Khojah, Rebekka Wohlrab, Marvin Damschen, Mazen Mohamad

YouthSafe: A Youth-Centric Safety Benchmark and Safeguard Model for Large Language Models
Yaman Yu, Yiren Liu, Jacky Zhang, Yun Huang, Yang Wang
https://arxiv.org/abs/2509.08997

YouthSafe: A Youth-Centric Safety Benchmark and Safeguard Model for Large Language Models
Large Language Models (LLMs) are increasingly used by teenagers and young adults in everyday life, ranging from emotional support and creative expression to educational assistance. However, their unique vulnerabilities and risk profiles remain under-examined in current safety benchmarks and moderation systems, leaving this population disproportionately exposed to harm. In this work, we present Youth AI Risk (YAIR), the first benchmark dataset designed to evaluate and improve the safety of youth…

Non-Invasive Detection of PROState Cancer with Novel Time-Dependent Diffusion MRI and AI-Enhanced Quantitative Radiological Interpretation: PROS-TD-AI
Baltasar Ramos, Cristian Garrido, Paulette Narv'aez, Santiago Gelerstein Claro, Haotian Li, Rafael Salvador, Constanza V'asquez-Venegas, Iv'an Gallegos, Yi Zhang, V'ictor Casta~neda, Cristian Acevedo, Dan Wu, Gonzalo C'ardenas, Camilo G. Sotomayor

Non-Invasive Detection of PROState Cancer with Novel Time-Dependent Diffusion MRI and AI-Enhanced Quantitative Radiological Interpretation: PROS-TD-AI
Prostate cancer (PCa) is the most frequently diagnosed malignancy in men and the eighth leading cause of cancer death worldwide. Multiparametric MRI (mpMRI) has become central to the diagnostic pathway for men at intermediate risk, improving de-tection of clinically significant PCa (csPCa) while reducing unnecessary biopsies and over-diagnosis. However, mpMRI remains limited by false positives, false negatives, and moderate to substantial interobserver agreement. Time-dependent diffusion (TDD) …

A look at CoreWeave, whose financing strategy involves using GPUs as collateral for large loans, which enabled rapid expansion but resulted in $11.2B in debt (Rashi Shrivastava/Forbes)
https://www.forbes.com/sites/rashishrivast

How CoreWeave’s GPU Bet Made Four Risk-Loving Cryptominers AI Billionaires
Creative financing and an enormous appetite for risk helped CoreWeave’s Michael Intrator ride the AI data center boom to a $6.7 billion personal fortune. But as bubble fears mount, his reliance on billions in debt to supply advanced chips to major AI players puts him in a powerful position.

Governance-as-a-Service: A Multi-Agent Framework for AI System Compliance and Policy Enforcement
Helen Pervez, Suyash Gaurav, Jukka Heikkonen, Jatin Chaudhary
https://arxiv.org/abs/2508.18765

Governance-as-a-Service: A Multi-Agent Framework for AI System Compliance and Policy Enforcement
As AI systems evolve into distributed ecosystems with autonomous execution, asynchronous reasoning, and multi-agent coordination, the absence of scalable, decoupled governance poses a structural risk. Existing oversight mechanisms are reactive, brittle, and embedded within agent architectures, making them non-auditable and hard to generalize across heterogeneous deployments. We introduce Governance-as-a-Service (GaaS): a modular, policy-driven enforcement layer that regulates agent outputs at…

Beyond risk: A proto-framework for assessing the societal impact of AI systems
Willem Fourie
https://arxiv.org/abs/2508.03666 https://arxiv.org/pdf/2508.03…

Beyond risk: A proto-framework for assessing the societal impact of AI systems
In the discourse on AI regulation, 'responsible AI' is the dominant paradigm, with the focus on mitigating the risks related to AI systems. While this focus is important and necessary, it has limited use for a systematic consideration of AI's societal impact. This paper proposes a proto-framework for assessing the societal impact of AI systems by operationalising the concept of freedom. This proto-framework is intended as a step towards a fully operationalised framework to be used in policymaki…

Charting Uncertain Waters: A Socio-Technical Framework for Navigating GenAI's Impact on Open Source Communities
Zixuan Feng, Reed Milewicz, Emerson Murphy-Hill, Tyler Menezes, Alexander Serebrenik, Igor Steinmacher, Anita Sarma
https://arxiv.org/abs/2508.04921

Charting Uncertain Waters: A Socio-Technical Framework for Navigating GenAI's Impact on Open Source Communities
Open Source Software communities face a wave of uncertainty as Generative AI rapidly transforms how software is created, maintained, and governed. Without clear frameworks, communities risk being overwhelmed by the complexity and ambiguity introduced by GenAI, threatening the collaborative ethos that underpins OSS. We conduct a scenario-driven, conceptual exploration using a socio-technical framework inspired by McLuhan's Tetrad to surface both risks and opportunities for community resilience a…

"We are not Future-ready": Understanding AI Privacy Risks and Existing Mitigation Strategies from the Perspective of AI Developers in Europe
Alexandra Klymenko, Stephen Meisenbacher, Patrick Gage Kelley, Sai Teja Peddinti, Kurt Thomas, Florian Matthes
https://arxiv.org/abs/2510.00909

"We are not Future-ready": Understanding AI Privacy Risks and Existing Mitigation Strategies from the Perspective of AI Developers in Europe
The proliferation of AI has sparked privacy concerns related to training data, model interfaces, downstream applications, and more. We interviewed 25 AI developers based in Europe to understand which privacy threats they believe pose the greatest risk to users, developers, and businesses and what protective strategies, if any, would help to mitigate them. We find that there is little consensus among AI developers on the relative ranking of privacy risks. These differences stem from salient reas…

Bias Mitigation for AI-Feedback Loops in Recommender Systems: A Systematic Literature Review and Taxonomy
Theodor Stoecker, Samed Bayer, Ingo Weber
https://arxiv.org/abs/2509.00109

Bias Mitigation for AI-Feedback Loops in Recommender Systems: A Systematic Literature Review and Taxonomy
Recommender systems continually retrain on user reactions to their own predictions, creating AI feedback loops that amplify biases and diminish fairness over time. Despite this well-known risk, most bias mitigation techniques are tested only on static splits, so their long-term fairness across multiple retraining rounds remains unclear. We therefore present a systematic literature review of bias mitigation methods that explicitly consider AI feedback loops and are validated in multi-round simul…

The More You Automate, the Less You See: Hidden Pitfalls of AI Scientist Systems
Ziming Luo, Atoosa Kasirzadeh, Nihar B. Shah
https://arxiv.org/abs/2509.08713 https://

The More You Automate, the Less You See: Hidden Pitfalls of AI Scientist Systems
AI scientist systems, capable of autonomously executing the full research workflow from hypothesis generation and experimentation to paper writing, hold significant potential for accelerating scientific discovery. However, the internal workflow of these systems have not been closely examined. This lack of scrutiny poses a risk of introducing flaws that could undermine the integrity, reliability, and trustworthiness of their research outputs. In this paper, we identify four potential failure mod…

The Memory Wars: AI Memory, Network Effects, and the Geopolitics of Cognitive Sovereignty
Mario Brcic
https://arxiv.org/abs/2508.05867 https://arxiv.org/pd…

The Memory Wars: AI Memory, Network Effects, and the Geopolitics of Cognitive Sovereignty
The advent of continuously learning Artificial Intelligence (AI) assistants marks a paradigm shift from episodic interactions to persistent, memory-driven relationships. This paper introduces the concept of "Cognitive Sovereignty", the ability of individuals, groups, and nations to maintain autonomous thought and preserve identity in the age of powerful AI systems, especially those that hold their deep personal memory. It argues that the primary risk of these technologies transcends traditional…

Cyst-X: AI-Powered Pancreatic Cancer Risk Prediction from Multicenter MRI in Centralized and Federated Learning
Hongyi Pan, Gorkem Durak, Elif Keles, Deniz Seyithanoglu, Zheyuan Zhang, Alpay Medetalibeyoglu, Halil Ertugrul Aktas, Andrea Mia Bejar, Ziliang Hong, Yavuz Taktak, Gulbiz Dagoglu Kartal, Mehmet Sukru Erturk, Timurhan Cebeci, Maria Jaramillo Gonzalez, Yury Velichko, Lili Zhao, Emil Agarunov, Federica Proietto Salanitri, Concetto Spampinato, Pallavi Tiwari, Ziyue Xu, Sachin Jam…

Cyst-X: AI-Powered Pancreatic Cancer Risk Prediction from Multicenter MRI in Centralized and Federated Learning
Pancreatic cancer is projected to become the second-deadliest malignancy in Western countries by 2030, highlighting the urgent need for better early detection. Intraductal papillary mucinous neoplasms (IPMNs), key precursors to pancreatic cancer, are challenging to assess with current guidelines, often leading to unnecessary surgeries or missed malignancies. We present Cyst-X, an AI framework that predicts IPMN malignancy using multicenter MRI data, leveraging MRI's superior soft tissue contras…

HumanAgencyBench: Scalable Evaluation of Human Agency Support in AI Assistants
Benjamin Sturgeon, Daniel Samuelson, Jacob Haimes, Jacy Reese Anthis
https://arxiv.org/abs/2509.08494

HumanAgencyBench: Scalable Evaluation of Human Agency Support in AI Assistants
As humans delegate more tasks and decisions to artificial intelligence (AI), we risk losing control of our individual and collective futures. Relatively simple algorithmic systems already steer human decision-making, such as social media feed algorithms that lead people to unintentionally and absent-mindedly scroll through engagement-optimized content. In this paper, we develop the idea of human agency by integrating philosophical and scientific theories of agency with AI-assisted evaluation me…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[8/13]:
- Improved Sample Complexity For Diffusion Model Training Without Empirical Risk Minimizer Access
Mudit Gaur, Prashant Trivedi, Sasidhar Kunapuli, Amrit Singh Bedi, Vaneet Aggarwal

CIA TA Risk Assessment for AI Reasoning Vulnerabilities
Yuksel Aydin
https://arxiv.org/abs/2508.15839 https://arxiv.org/pdf/2508.15839

CIA+TA Risk Assessment for AI Reasoning Vulnerabilities
As AI systems increasingly influence critical decisions, they face threats that exploit reasoning mechanisms rather than technical infrastructure. We present a framework for cognitive cybersecurity, a systematic protection of AI reasoning processes from adversarial manipulation. Our contributions are threefold. First, we establish cognitive cybersecurity as a discipline complementing traditional cybersecurity and AI safety, addressing vulnerabilities where legitimate inputs corrupt reasoning wh…

Student Development Agent: Risk-free Simulation for Evaluating AIED Innovations
Jianxiao Jiang, Yu Zhang
https://arxiv.org/abs/2510.09183 https://arxiv.org…

Student Development Agent: Risk-free Simulation for Evaluating AIED Innovations
In the age of AI-powered educational (AIED) innovation, evaluating the developmental consequences of novel designs before they are exposed to students has become both essential and challenging. Since such interventions may carry irreversible effects, it is critical to anticipate not only potential benefits but also possible harms. This study proposes a student development agent framework based on large language models (LLMs), designed to simulate how students with diverse characteristics may ev…

Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs "In the Wild"
Lingyao Li, Renkai Ma, Zhaoqian Xue, Junjie Xiong
https://arxiv.org/abs/2509.08912

Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs "In the Wild"
While Large Language Models (LLMs) are rapidly integrating into daily life, research on their risks often remains lab-based and disconnected from the problems users encounter "in the wild." While recent HCI research has begun to explore these user-facing risks, it typically concentrates on a singular LLM chatbot like ChatGPT or an isolated risk like privacy. To gain a holistic understanding of multi-risk across LLM chatbots, we analyze online discussions on Reddit around seven major LLM chatbot…

Multi-Label Classification with Generative AI Models in Healthcare: A Case Study of Suicidality and Risk Factors
Ming Huang, Zehan Li, Yan Hu, Wanjing Wang, Andrew Wen, Scott Lane, Salih Selek, Lokesh Shahani, Rodrigo Machado-Vieira, Jair Soares, Hua Xu, Hongfang Liu
https://arxiv.org/abs/2507.17009

Multi-Label Classification with Generative AI Models in Healthcare: A Case Study of Suicidality and Risk Factors
Suicide remains a pressing global health crisis, with over 720,000 deaths annually and millions more affected by suicide ideation (SI) and suicide attempts (SA). Early identification of suicidality-related factors (SrFs), including SI, SA, exposure to suicide (ES), and non-suicidal self-injury (NSSI), is critical for timely intervention. While prior studies have applied AI to detect SrFs in clinical notes, most treat suicidality as a binary classification task, overlooking the complexity of coo…

Informing AI Risk Assessment with News Media: Analyzing National and Political Variation in the Coverage of AI Risks
Mowafak Allaham, Kimon Kieslich, Nicholas Diakopoulos
https://arxiv.org/abs/2507.23718

Informing AI Risk Assessment with News Media: Analyzing National and Political Variation in the Coverage of AI Risks
Risk-based approaches to AI governance often center the technological artifact as the primary focus of risk assessments, overlooking systemic risks that emerge from the complex interaction between AI systems and society. One potential source to incorporate more societal context into these approaches is the news media, as it embeds and reflects complex interactions between AI systems, human stakeholders, and the larger society. News media is influential in terms of which AI risks are emphasized …

AI-Driven Collaborative Satellite Object Detection for Space Sustainability
Peng Hu, Wenxuan Zhang
https://arxiv.org/abs/2508.00755 https://arxiv.org/pdf/2…

AI-Driven Collaborative Satellite Object Detection for Space Sustainability
The growing density of satellites in low-Earth orbit (LEO) presents serious challenges to space sustainability, primarily due to the increased risk of in-orbit collisions. Traditional ground-based tracking systems are constrained by latency and coverage limitations, underscoring the need for onboard, vision-based space object detection (SOD) capabilities. In this paper, we propose a novel satellite clustering framework that enables the collaborative execution of deep learning (DL)-based SOD tas…

Frontier AI Risk Management Framework in Practice: A Risk Analysis Technical Report
Shanghai AI Lab, :, Xiaoyang Chen, Yunhao Chen, Zeren Chen, Zhiyun Chen, Hanyun Cui, Yawen Duan, Jiaxuan Guo, Qi Guo, Xuhao Hu, Hong Huang, Lige Huang, Chunxiao Li, Juncheng Li, Qihao Lin, Dongrui Liu, Xinmin Liu, Zicheng Liu, Chaochao Lu, Xiaoya Lu, Jingjing Qu, Qibing Ren, Jing Shao, Jingwei Shi, Jingwei Sun, Peng Wang, Weibing Wang, Jia Xu, Lewen Yan, Xiao Yu, Yi Yu, Boxuan Zhang, Jie Zhang, Weichen…

Frontier AI Risk Management Framework in Practice: A Risk Analysis Technical Report
To understand and identify the unprecedented risks posed by rapidly advancing artificial intelligence (AI) models, this report presents a comprehensive assessment of their frontier risks. Drawing on the E-T-C analysis (deployment environment, threat source, enabling capability) from the Frontier AI Risk Management Framework (v1.0) (SafeWork-F1-Framework), we identify critical risks in seven areas: cyber offense, biological and chemical risks, persuasion and manipulation, uncontrolled autonomous…

The UK's Cabinet Office says its Fraud Risk Assessment Accelerator AI tool helped the government recover £480M from fraud in the 12 months from April 2024 (Jack Fenwick/BBC)
https://www.bbc.com/news/articles/cpd92gpld0go

AI tool used to recover £500m lost to fraud, government says
Ministers say software developed by the Cabinet Office has helped to ramp up fraud detection.

The 2025 OpenAI Preparedness Framework does not guarantee any AI risk mitigation practices: a proof-of-concept for affordance analyses of AI safety policies
Sam Coggins, Alex Saeri, Katherine A. Daniell, Lorenn P. Ruster, Jessie Liu, Jenny L. Davis
https://arxiv.org/abs/2509.24394

The 2025 OpenAI Preparedness Framework does not guarantee any AI risk mitigation practices: a proof-of-concept for affordance analyses of AI safety policies
Prominent AI companies are producing 'safety frameworks' as a type of voluntary self-governance. These statements purport to establish risk thresholds and safety procedures for the development and deployment of highly capable AI. Understanding which AI risks are covered and what actions are allowed, refused, demanded, encouraged, or discouraged by these statements is vital for assessing how these frameworks actually govern AI development and deployment. We draw on affordance theory to analyse t…

Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[5/5]:
- Frontier AI Risk Management Framework in Practice: A Risk Analysis Technical Report
Shanghai AI Lab, et al.

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[10/17]:
- Improved Sample Complexity For Diffusion Model Training Without Empirical Risk Minimizer Access
Mudit Gaur, Prashant Trivedi, Sasidhar Kunapuli, Amrit Singh Bedi, Vaneet Aggarwal

The AI Model Risk Catalog: What Developers and Researchers Miss About Real-World AI Harms
Pooja S. B. Rao, Sanja \v{S}\'cepanovi\'c, Dinesh Babu Jayagopi, Mauro Cherubini, Daniele Quercia
https://arxiv.org/abs/2508.16672

The AI Model Risk Catalog: What Developers and Researchers Miss About Real-World AI Harms
We analyzed nearly 460,000 AI model cards from Hugging Face to examine how developers report risks. From these, we extracted around 3,000 unique risk mentions and built the \emph{AI Model Risk Catalog}. We compared these with risks identified by researchers in the MIT Risk Repository and with real-world incidents from the AI Incident Database. Developers focused on technical issues like bias and safety, while researchers emphasized broader social impacts. Both groups paid little attention to fr…

Even More Kawaii than Real-Person-Driven VTubers? Understanding How Viewers Perceive AI-Driven VTubers
Yiluo Wei, Yupeng He, Gareth Tyson
https://arxiv.org/abs/2509.20817 https:…

Even More Kawaii than Real-Person-Driven VTubers? Understanding How Viewers Perceive AI-Driven VTubers
VTubers, digital personas represented by animated avatars, have gained massive popularity. Traditionally, VTubers are operated and voiced by human controllers known as Nakanohito. The reliance on Nakanohito, however, poses risks due to potential personal controversies and operational disruptions. The emergence of AI-driven VTubers offers a new model free from these human constraints. While AI-driven VTubers present benefits such as continuous operation and reduced scandal risk, they also raise …

Dallas-based iCounter, which uses AI to combat cyber risks, launches out of stealth from Apollo Information Systems with a $30M Series A led by SYN Ventures (Chris Metinko/Axios)
https://www.axios.com/pro/enterprise-software-deals/2025/07/16/icou…

Exclusive: iCounter raises $30M Series A
SYN Ventures leads the round.

Assessing Human Rights Risks in AI: A Framework for Model Evaluation
Vyoma Raman, Camille Chabot, Betsy Popken
https://arxiv.org/abs/2510.05519 https://arx…

Assessing Human Rights Risks in AI: A Framework for Model Evaluation
The Universal Declaration of Human Rights and other international agreements outline numerous inalienable rights that apply across geopolitical boundaries. As generative AI becomes increasingly prevalent, it poses risks to human rights such as non-discrimination, health, and security, which are also central concerns for AI researchers focused on fairness and safety. We contribute to the field of algorithmic auditing by presenting a framework to computationally assess human rights risk. Drawing …

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[5/5]:
- Machine Learning Risk Intelligence for Green Hydrogen Investment: Insights for Duqm R3 Auction
Obumneme Nwafor, Mohammed Abdul Majeed Al Hooti

Societal Capacity Assessment Framework: Measuring Resilience to Inform Advanced AI Risk Management
Milan Gandhi, Peter Cihon, Owen Larter, Rebecca Anselmetti
https://arxiv.org/abs/2509.22742

Societal Capacity Assessment Framework: Measuring Resilience to Inform Advanced AI Risk Management
Risk assessments for advanced AI systems require evaluating both the models themselves and their deployment contexts. We introduce the Societal Capacity Assessment Framework (SCAF), an indicators-based approach to measuring a society's vulnerability, coping capacity, and adaptive capacity in response to AI-related risks. SCAF adapts established resilience analysis methodologies to AI, enabling organisations to ground risk management in insights about country-level deployment conditions. It can …

Education software company EdSights, which uses SMS texting and AI to reach students and identify those at risk of dropping out, raised $80M from JMI Equity (Chris Metinko/Axios)
https://www.axios.com/pro/enterprise-software-deals/…

Exclusive: EdSights raises $80M from JMI Equity
The company is growing revenue 70% year to year.

AnchoredAI: Contextual Anchoring of AI Comments Improves Writer Agency and Ownership
Martin Lou, Jackie Crowley, Samuel Dodson, Dongwook Yoon
https://arxiv.org/abs/2509.16128 ht…

AnchoredAI: Contextual Anchoring of AI Comments Improves Writer Agency and Ownership
Generative AI is increasingly integrated into writing support, yet current chat-based interfaces often obscure referential context and risk amplifying automation bias and overreliance. We introduce AnchoredAI, a novel system that anchors AI feedback directly to relevant text spans. AnchoredAI implements two key mechanisms: (1) an Anchoring Context Window (ACW) that maintains unique, context-rich references, and (2) an update-aware context retrieval method that preserves the intent of prior comm…

Measuring and mitigating overreliance is necessary for building human-compatible AI
Lujain Ibrahim, Katherine M. Collins, Sunnie S. Y. Kim, Anka Reuel, Max Lamparth, Kevin Feng, Lama Ahmad, Prajna Soni, Alia El Kattan, Merlin Stein, Siddharth Swaroop, Ilia Sucholutsky, Andrew Strait, Q. Vera Liao, Umang Bhatt
https://arxiv.org/abs/2509.080…

Measuring and mitigating overreliance is necessary for building human-compatible AI
Large language models (LLMs) distinguish themselves from previous technologies by functioning as collaborative "thought partners," capable of engaging more fluidly in natural language. As LLMs increasingly influence consequential decisions across diverse domains from healthcare to personal advice, the risk of overreliance - relying on LLMs beyond their capabilities - grows. This position paper argues that measuring and mitigating overreliance must become central to LLM research and deployment. …

Automating AI Failure Tracking: Semantic Association of Reports in AI Incident Database
Diego Russo, Gian Marco Orlando, Valerio La Gatta, Vincenzo Moscato
https://arxiv.org/abs/2507.23669

Automating AI Failure Tracking: Semantic Association of Reports in AI Incident Database
Artificial Intelligence (AI) systems are transforming critical sectors such as healthcare, finance, and transportation, enhancing operational efficiency and decision-making processes. However, their deployment in high-stakes domains has exposed vulnerabilities that can result in significant societal harm. To systematically study and mitigate these risk, initiatives like the AI Incident Database (AIID) have emerged, cataloging over 3,000 real-world AI failure reports. Currently, associating a ne…

Mark Zuckerberg says "We'll need to be rigorous about mitigating" new risks posed by superintelligence and "careful about what we choose to open source" (Michael Kan/PCMag)
https://www.pcmag.com/news/zuckerberg-walks-back-open…

Who pays the RENT? Implications of Spatial Inequality for Prediction-Based Allocation Policies
Tasfia Mashiat, Patrick J. Fowler, Sanmay Das
https://arxiv.org/abs/2508.08573 htt…

Who pays the RENT? Implications of Spatial Inequality for Prediction-Based Allocation Policies
AI-powered scarce resource allocation policies rely on predictions to target either specific individuals (e.g., high-risk) or settings (e.g., neighborhoods). Recent research on individual-level targeting demonstrates conflicting results; some models show that targeting is not useful when inequality is high, while other work demonstrates potential benefits. To study and reconcile this apparent discrepancy, we develop a stylized framework based on the Mallows model to understand how the spatial d…

A Human-Centered Approach to Identifying Promises, Risks, & Challenges of Text-to-Image Generative AI in Radiology
Katelyn Morrison, Arpit Mathur, Aidan Bradshaw, Tom Wartmann, Steven Lundi, Afrooz Zandifar, Weichang Dai, Kayhan Batmanghelich, Motahhare Eslami, Adam Perer
https://arxiv.org/abs/2507.16207

A Human-Centered Approach to Identifying Promises, Risks, & Challenges of Text-to-Image Generative AI in Radiology
As text-to-image generative models rapidly improve, AI researchers are making significant advances in developing domain-specific models capable of generating complex medical imagery from text prompts. Despite this, these technical advancements have overlooked whether and how medical professionals would benefit from and use text-to-image generative AI (GenAI) in practice. By developing domain-specific GenAI without involving stakeholders, we risk the potential of building models that are either …

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[6/6]:
- On the Security of Tool-Invocation Prompts for LLM-Based Agentic Systems: An Empirical Risk Asses...
Xie, Luo, Liu, Zhang, Zhang, Liu, Li, Chen, Wang, She

Are Companies Taking AI Risks Seriously? A Systematic Analysis of Companies' AI Risk Disclosures in SEC 10-K forms
Lucas G. Uberti-Bona Marin, Bram Rijsbosch, Gerasimos Spanakis, Konrad Kollnig
https://arxiv.org/abs/2508.19313

Are Companies Taking AI Risks Seriously? A Systematic Analysis of Companies' AI Risk Disclosures in SEC 10-K forms
As Artificial Intelligence becomes increasingly central to corporate strategies, concerns over its risks are growing too. In response, regulators are pushing for greater transparency in how companies identify, report and mitigate AI-related risks. In the US, the Securities and Exchange Commission (SEC) repeatedly warned companies to provide their investors with more accurate disclosures of AI-related risks; recent enforcement and litigation against companies' misleading AI claims reinforce thes…

SePA: A Search-enhanced Predictive Agent for Personalized Health Coaching
Melik Ozolcer, Sang Won Bae
https://arxiv.org/abs/2509.04752 https://arxiv.org/pd…

SePA: A Search-enhanced Predictive Agent for Personalized Health Coaching
This paper introduces SePA (Search-enhanced Predictive AI Agent), a novel LLM health coaching system that integrates personalized machine learning and retrieval-augmented generation to deliver adaptive, evidence-based guidance. SePA combines: (1) Individualized models predicting daily stress, soreness, and injury risk from wearable sensor data (28 users, 1260 data points); and (2) A retrieval module that grounds LLM-generated feedback in expert-vetted web content to ensure contextual relevance …

London-based media monitoring and risk management company Signal AI raised $165M led by Battery Ventures, which will hold a majority stake after the investment (Daniel Thomas/Financial Times)
https://www.ft.com/content/d2913fba-a867-4b63-9fed-1dd2e1c65453

Can AI be Auditable?
Himanshu Verma, Kirtan Path, Eva Thelisson
https://arxiv.org/abs/2509.00575 https://arxiv.org/pdf/2509.00575

Can AI be Auditable?
Auditability is defined as the capacity of AI systems to be independently assessed for compliance with ethical, legal, and technical standards throughout their lifecycle. The chapter explores how auditability is being formalized through emerging regulatory frameworks, such as the EU AI Act, which mandate documentation, risk assessments, and governance structures. It analyzes the diverse challenges facing AI auditability, including technical opacity, inconsistent documentation practices, lack of…

An Overview of the Risk-based Model of AI Governance
Veve Fry
https://arxiv.org/abs/2507.15299 https://arxiv.org/pdf/2507.15299

An Overview of the Risk-based Model of AI Governance
This paper provides an overview and critique of the risk based model of artificial intelligence (AI) governance that has become a popular approach to AI regulation across multiple jurisdictions. The 'AI Policy Landscape in Europe, North America and Australia' section summarises the existing AI policy efforts across these jurisdictions, with a focus of the EU AI Act and the Australian Department of Industry, Science and Regulation's (DISR) safe and responsible AI consultation. The 'Analysis' sec…

The Ethical Compass of the Machine: Evaluating Large Language Models for Decision Support in Construction Project Management
Somtochukwu Azie, Yiping Meng
https://arxiv.org/abs/2509.04505

The Ethical Compass of the Machine: Evaluating Large Language Models for Decision Support in Construction Project Management
The integration of Artificial Intelligence (AI) into construction project management (CPM) is accelerating, with Large Language Models (LLMs) emerging as accessible decision-support tools. This study aims to critically evaluate the ethical viability and reliability of LLMs when applied to the ethically sensitive, high-risk decision-making contexts inherent in CPM. A mixed-methods research design was employed, involving the quantitative performance testing of two leading LLMs against twelve real…

An Analysis of the New EU AI Act and A Proposed Standardization Framework for Machine Learning Fairness
Mike Teodorescu, Yongxu Sun, Haren N. Bhatia, Christos Makridis
https://arxiv.org/abs/2510.01281 …

An Analysis of the New EU AI Act and A Proposed Standardization Framework for Machine Learning Fairness
The European Union's AI Act represents a crucial step towards regulating ethical and responsible AI systems. However, we find an absence of quantifiable fairness metrics and the ambiguity in terminology, particularly the interchangeable use of the keywords transparency, explainability, and interpretability in the new EU AI Act and no reference of transparency of ethical compliance. We argue that this ambiguity creates substantial liability risk that would deter investment. Fairness transparency…

The Stories We Govern By: AI, Risk, and the Power of Imaginaries
Ninell Oldenburg, Gleb Papyshev
https://arxiv.org/abs/2508.11729 https://arxiv.org/pdf/250…

The Stories We Govern By: AI, Risk, and the Power of Imaginaries
This paper examines how competing sociotechnical imaginaries of artificial intelligence (AI) risk shape governance decisions and regulatory constraints. Drawing on concepts from science and technology studies, we analyse three dominant narrative groups: existential risk proponents, who emphasise catastrophic AGI scenarios; accelerationists, who portray AI as a transformative force to be unleashed; and critical AI scholars, who foreground present-day harms rooted in systemic inequality. Through …

The AI Risk Spectrum: From Dangerous Capabilities to Existential Threats
Markov Grey, Charbel-Rapha\"el Segerie
https://arxiv.org/abs/2508.13700 https://

The AI Risk Spectrum: From Dangerous Capabilities to Existential Threats
As AI systems become more capable, integrated, and widespread, understanding the associated risks becomes increasingly important. This paper maps the full spectrum of AI risks, from current harms affecting individual users to existential threats that could endanger humanity's survival. We organize these risks into three main causal categories. Misuse risks, which occur when people deliberately use AI for harmful purposes - creating bioweapons, launching cyberattacks, adversarial AI attacks or d…

Navigating the EU AI Act: Foreseeable Challenges in Qualifying Deep Learning-Based Automated Inspections of Class III Medical Devices
Julio Zanon Diaz, Tommy Brennan, Peter Corcoran
https://arxiv.org/abs/2508.20144

Navigating the EU AI Act: Foreseeable Challenges in Qualifying Deep Learning-Based Automated Inspections of Class III Medical Devices
As deep learning (DL) technologies advance, their application in automated visual inspection for Class III medical devices offers significant potential to enhance quality assurance and reduce human error. However, the adoption of such AI-based systems introduces new regulatory complexities--particularly under the EU Artificial Intelligence (AI) Act, which imposes high-risk system obligations that differ in scope and depth from established regulatory frameworks such as the Medical Device Regulat…

Toward an African Agenda for AI Safety
Samuel T. Segun, Rachel Adams, Ana Florido, Scott Timcke, Jonathan Shock, Leah Junck, Fola Adeleke, Nicolas Grossman, Ayantola Alayande, Jerry John Kponyo, Matthew Smith, Dickson Marfo Fosu, Prince Dawson Tetteh, Juliet Arthur, Stephanie Kasaon, Odilile Ayodele, Laetitia Badolo, Paul Plantinga, Michael Gastrow, Sumaya Nur Adan, Joanna Wiaterek, Cecil Abungu, Kojo Apeagyei, Luise Eder, Tegawende Bissyande

Toward an African Agenda for AI Safety
This paper maps Africa's distinctive AI risk profile, from deepfake fuelled electoral interference and data colonial dependency to compute scarcity, labour disruption and disproportionate exposure to climate driven environmental costs. While major benefits are promised to accrue, the availability, development and adoption of AI also mean that African people and countries face particular AI safety risks, from large scale labour market disruptions to the nefarious use of AI to manipulate public o…

Longitudinal and Multimodal Recording System to Capture Real-World Patient-Clinician Conversations for AI and Encounter Research: Protocol
Misk Al Zahidy, Kerly Guevara Maldonado, Luis Vilatuna Andrango, Ana Cristina Proano, Ana Gabriela Claros, Maria Lizarazo Jimenez, David Toro-Tobon, Oscar J. Ponce-Ponce, Juan P. Brito
https://arxiv.org…

Longitudinal and Multimodal Recording System to Capture Real-World Patient-Clinician Conversations for AI and Encounter Research: Protocol
The promise of AI in medicine depends on learning from data that reflect what matters to patients and clinicians. Most existing models are trained on electronic health records (EHRs), which capture biological measures but rarely patient-clinician interactions. These relationships, central to care, unfold across voice, text, and video, yet remain absent from datasets. As a result, AI systems trained solely on EHRs risk perpetuating a narrow biomedical view of medicine and overlooking the lived e…