Tootfinder

Beware! The AI Act Can Also Apply to Your AI Research Practices
Alina Wernick, Kristof Meding
https://arxiv.org/abs/2506.03218 https://

Beware! The AI Act Can Also Apply to Your AI Research Practices
The EU has become one of the vanguards in regulating the digital age. A particularly important regulation in the Artificial Intelligence (AI) domain is the EU AI Act, which entered into force in 2024. The AI Act specifies -- due to a risk-based approach -- various obligations for providers of AI systems. These obligations, for example, include a cascade of documentation and compliance measures, which represent a potential obstacle to science. But do these obligations also apply to AI researcher…

Analysis – When your LLM calls the cops: Claude 4’s whistle-blow and the new agentic AI risk stack
🤖 https://venturebeat.com/ai/when-your-llm-calls-the-cops-claude-4s-whistle-blow-and-the-new-agentic-ai-risk-stack/…

When your LLM calls the cops: Claude 4’s whistle-blow and the new agentic AI risk stack
Claude 4’s “whistle-blow” surprise shows why agentic AI risk lives in prompts and tool access, not benchmarks. Learn the 6 controls every enterprise must adopt.

AI coding startups are at risk of being disrupted by Google, Microsoft, and OpenAI; source: Microsoft's GitHub Copilot grew to over $500M in revenue last year (Reuters)
https://www.reuters.com/business/ai-vibe-codi…

Developing a Risk Identification Framework for Foundation Model Uses
David Piorkowski, Michael Hind, John Richards, Jacquelyn Martino
https://arxiv.org/abs/2506.02066

Developing a Risk Identification Framework for Foundation Model Uses
As foundation models grow in both popularity and capability, researchers have uncovered a variety of ways that the models can pose a risk to the model's owner, user, or others. Despite the efforts of measuring these risks via benchmarks and cataloging them in AI risk taxonomies, there is little guidance for practitioners on how to determine which risks are relevant for a given foundation model use. In this paper, we address this gap and develop requirements and an initial design for a risk iden…

AI Risk-Management Standards Profile for General-Purpose AI (GPAI) and Foundation Models
Anthony M. Barrett, Jessica Newman, Brandie Nonnecke, Nada Madkour, Dan Hendrycks, Evan R. Murphy, Krystal Jackson, Deepika Raman
https://arxiv.org/abs/2506.23949

AI Risk-Management Standards Profile for General-Purpose AI (GPAI) and Foundation Models
Increasingly multi-purpose AI models, such as cutting-edge large language models or other 'general-purpose AI' (GPAI) models, 'foundation models,' generative AI models, and 'frontier models' (typically all referred to hereafter with the umbrella term 'GPAI/foundation models' except where greater specificity is needed), can provide many beneficial capabilities but also risks of adverse events with profound consequences. This document provides risk-management practices or controls for identifying…

Misalignment or misuse? The AGI alignment tradeoff
Max Hellrigel-Holderbaum, Leonard Dung
https://arxiv.org/abs/2506.03755 https://ar…

Misalignment or misuse? The AGI alignment tradeoff
Creating systems that are aligned with our goals is seen as a leading approach to create safe and beneficial AI in both leading AI companies and the academic field of AI safety. We defend the view that misaligned AGI - future, generally intelligent (robotic) AI agents - poses catastrophic risks. At the same time, we support the view that aligned AGI creates a substantial risk of catastrophic misuse by humans. While both risks are severe and stand in tension with one another, we show that - in p…

"Inside a plan to use AI to amplify doubts about the dangers of pollutants"
#AI #ArtificialIntelligence #Climate

Locating Risk: Task Designers and the Challenge of Risk Disclosure in RAI Content Work
Alice Qian Zhang, Ryland Shaw, Laura Dabbish, Jina Suh, Hong Shen
https://arxiv.org/abs/2505.24246

Locating Risk: Task Designers and the Challenge of Risk Disclosure in RAI Content Work
As AI systems are increasingly tested and deployed in open-ended and high-stakes domains, crowd workers are often tasked with responsible AI (RAI) content work. These tasks include labeling violent content, moderating disturbing text, or simulating harmful behavior for red teaming exercises to shape AI system behaviors. While prior efforts have highlighted the risks to worker well-being associated with RAI content work, far less attention has been paid to how these risks are communicated to wor…

FPF and OneTrust launch updated Conformity Assessment under the EU AI Act: guide and infographic
https://fpf.org/blog/fpf-and-onetrust-launch-updated-conformity-assessment-under-the-eu-ai-act-guide-and-infographi…

This https://arxiv.org/abs/2502.14708 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_eco…

Human Misperception of Generative-AI Alignment: A Laboratory Experiment
We conduct an incentivized laboratory experiment to study people's perception of generative artificial intelligence (GenAI) alignment in the context of economic decision-making. Using a panel of economic problems spanning the domains of risk, time preference, social preference, and strategic interactions, we ask human subjects to make choices for themselves and to predict the choices made by GenAI on behalf of a human user. We find that people overestimate the degree of alignment between GenAI'…

Asymmetry by Design: Boosting Cyber Defenders with Differential Access to AI
Shaun Ee, Chris Covino, Cara Labrador, Christina Krawec, Jam Kraprayoon, Joe O'Brien
https://arxiv.org/abs/2506.02035

Asymmetry by Design: Boosting Cyber Defenders with Differential Access to AI
As AI-enabled cyber capabilities become more advanced, we propose "differential access" as a strategy to tilt the cybersecurity balance toward defense by shaping access to these capabilities. We introduce three possible approaches that form a continuum, becoming progressively more restrictive for higher-risk capabilities: Promote Access, Manage Access, and Deny by Default. However, a key principle across all approaches is the need to prioritize defender access, even in the most restrictive scen…

San Diego-based Clearspeed, which offers AI-driven voice-based risk assessment tech for 60 languages, raised a $60M Series D, taking its total funding to $110M (Duncan Riley/SiliconANGLE)
https://siliconangle.com/2025/06/26/cl

Clearspeed raises $60M to expand voice-based risk assessment platform - SiliconANGLE
Clearspeed raises $60M to expand voice-based risk assessment platform - SiliconANGLE

This https://arxiv.org/abs/2505.23397 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

A Unified Framework for Human AI Collaboration in Security Operations Centers with Trusted Autonomy
This article presents a structured framework for Human-AI collaboration in Security Operations Centers (SOCs), integrating AI autonomy, trust calibration, and Human-in-the-loop decision making. Existing frameworks in SOCs often focus narrowly on automation, lacking systematic structures to manage human oversight, trust calibration, and scalable autonomy with AI. Many assume static or binary autonomy settings, failing to account for the varied complexity, criticality, and risk across SOC tasks c…

Defiant peers have delivered an ultimatum to the government – calling on it to offer artists copyright protection against artificial intelligence companies or risk losing a key piece of legislation.
The government suffered a fifth defeat in the House of Lords over controversial plans to allow the AI companies to train their models using copyrighted material
https://www.theguardian.com/technology/2025/jun/04/ministers-offer-concessions-ai-copyright-avoid-fifth-lords-defeat?CMP=Share_iOSApp_Other

Replaced article(s) found for q-fin.RM. https://arxiv.org/list/q-fin.RM/new
[1/1]:
- Explainable AI for Comprehensive Risk Assessment for Financial Reports: A Lightweight Hierarchica...
Xue Wen Tan, Stanley Kok

Our urban #MobilityAnalytics project EMERALDS hosts its next webinar tomorrow:
🗓️ 27 June | ⏰ 14:30 CEST | 📍Online
How The Hague is using data to manage urban risks.
Register 👉

I've been in meetings where #ACC "experts" testified "truthpartly" - but not "truthfully" - about chemical hazards - referencing old tests of health impacts (1970s/80s knowledge) while ignoring the mass of robust data developed in the late 90s and 2000s. This is another repeat performance for #ACC

I've been in meetings where #ACC "experts" testified "truthpartly" - but not "truthfully" - about chemical hazards - referencing old tests of health impacts (1970s/80s knowledge) while ignoring the mass of robust data developed in the late 90s and 2000s. This is another repeat performance for #ACC

Systematic Hazard Analysis for Frontier AI using STPA
Simon Mylius
https://arxiv.org/abs/2506.01782 https://arxiv.org/pdf/2506.01782

Systematic Hazard Analysis for Frontier AI using STPA
All of the frontier AI companies have published safety frameworks where they define capability thresholds and risk mitigations that determine how they will safely develop and deploy their models. Adoption of systematic approaches to risk modelling, based on established practices used in safety-critical industries, has been recommended, however frontier AI companies currently do not describe in detail any structured approach to identifying and analysing hazards. STPA (Systems-Theoretic Process A…

Internal docs show Meta plans to use AI to automate up to 90% of its privacy and integrity risk assessments, including in sensitive areas like violent content (NPR)
https://www.npr.org/2025/05/31/nx-s1-5407870/meta-ai-facebook-instagram-risks…

The lethal trifecta for #AI agents: private data, untrusted content, and external communication
https://simonwillison.net/2025/Jun/16/the-lethal-trifecta/

This is quite close to a risk I spoke to someone about a couple of months ago.
Government domains might host documents containing opinions or other third-party information (consultation responses, evidence, etc) - but LLM training ingest may assume the context of the government domain and incorporate it into a government position.
> Google’s AI overview interpreted the PDF document as an official EU stance, blending the information into a response

This essay by @… on why individual experiments on the usefulness of "AI" (or similar stuff) don't teach us anything useful and might actually harm us is brilliant.
Go read it. Too many insights to pull a quote TBH:

Trusting your own judgement on 'AI' is a huge risk
Web dev at the end of the world, from Hveragerði, Iceland

Explainable AI for Comprehensive Risk Assessment for Financial Reports: A Lightweight Hierarchical Transformer Network Approach
Xue Wen Tan, Stanley Kok
https://arxiv.org/abs/2506.23767

Explainable AI for Comprehensive Risk Assessment for Financial Reports: A Lightweight Hierarchical Transformer Network Approach
Every publicly traded U.S. company files an annual 10-K report containing critical insights into financial health and risk. We propose Tiny eXplainable Risk Assessor (TinyXRA), a lightweight and explainable transformer-based model that automatically assesses company risk from these reports. Unlike prior work that relies solely on the standard deviation of excess returns (adjusted for the Fama-French model), which indiscriminately penalizes both upside and downside risk, TinyXRA incorporates ske…

Agentic AI as the enemy's agent.
It is a bad idea to allow an LLM access to internal data and external communication (web pages, APIs, email, …) at the same time.
#AgenticAI #DataLeak #LLM

Concerning the Responsible Use of AI in the US Criminal Justice System
Cristopher Moore, Catherine Gill, Nadya Bliss, Kevin Butler, Stephanie Forrest, Daniel Lopresti, Mary Lou Maher, Helena Mentis, Shashi Shekhar, Amanda Stent, Matthew Turk
https://arxiv.org/abs/2506.00212

Concerning the Responsible Use of AI in the US Criminal Justice System
Artificial intelligence (AI) is increasingly being adopted in most industries, and for applications such as note taking and checking grammar, there is typically not a cause for concern. However, when constitutional rights are involved, as in the justice system, transparency is paramount. While AI can assist in areas such as risk assessment and forensic evidence generation, its "black box" nature raises significant questions about how decisions are made and whether they can be contested. This pa…

«The singularity is near, not because machine intelligence is suddenly surging, but because we are content to risk extinguishing the spark of human consciousness by exposing ourselves to endless streams of artificially generated bullshit.»
Nice post by @…! I've definitely noticed this when interacting with people who've been too deep into using "AI".
https://ideophone.org/bringing-about-the-singularity-by-giving-up-thinking/

Silence is Golden: Leveraging Adversarial Examples to Nullify Audio Control in LDM-based Talking-Head Generation
Yuan Gan, Jiaxu Miao, Yunze Wang, Yi Yang
https://arxiv.org/abs/2506.01591

Silence is Golden: Leveraging Adversarial Examples to Nullify Audio Control in LDM-based Talking-Head Generation
Advances in talking-head animation based on Latent Diffusion Models (LDM) enable the creation of highly realistic, synchronized videos. These fabricated videos are indistinguishable from real ones, increasing the risk of potential misuse for scams, political manipulation, and misinformation. Hence, addressing these ethical concerns has become a pressing issue in AI security. Recent proactive defense studies focused on countering LDM-based models by adding perturbations to portraits. However, th…

Here’s a person, Baldur Bjarnason, @…, who thinks I’m really wrong about LLMs and coding. I mostly don’t agree but the argument is well-presented: https://www.baldurbjarna…

Trusting your own judgement on 'AI' is a huge risk
Web dev at the end of the world, from Hveragerði, Iceland

Risk-Guided Diffusion: Toward Deploying Robot Foundation Models in Space, Where Failure Is Not An Option
Rohan Thakker, Adarsh Patnaik, Vince Kurtz, Jonas Frey, Jonathan Becktor, Sangwoo Moon, Rob Royce, Marcel Kaufmann, Georgios Georgakis, Pascal Roth, Joel Burdick, Marco Hutter, Shehryar Khattak
https://arxiv.org/abs/2506.1760…

Risk-Guided Diffusion: Toward Deploying Robot Foundation Models in Space, Where Failure Is Not An Option
Safe, reliable navigation in extreme, unfamiliar terrain is required for future robotic space exploration missions. Recent generative-AI methods learn semantically aware navigation policies from large, cross-embodiment datasets, but offer limited safety guarantees. Inspired by human cognitive science, we propose a risk-guided diffusion framework that fuses a fast, learned "System-1" with a slow, physics-based "System-2", sharing computation at both training and inference to couple adaptability …

Watermarking Without Standards Is Not AI Governance
Alexander Nemecek, Yuzhou Jiang, Erman Ayday
https://arxiv.org/abs/2505.23814 https://

Watermarking Without Standards Is Not AI Governance
Watermarking has emerged as a leading technical proposal for attributing generative AI content and is increasingly cited in global governance frameworks. This paper argues that current implementations risk serving as symbolic compliance rather than delivering effective oversight. We identify a growing gap between regulatory expectations and the technical limitations of existing watermarking schemes. Through analysis of policy proposals and industry practices, we show how incentive structures di…

The fundamental problem of risk prediction for individuals: health AI, uncertainty, and personalized medicine
Lasai Barre\~nada, Ewout W Steyerberg, Dirk Timmerman, Doranne Thomassen, Laure Wynants, Ben Van Calster
https://arxiv.org/abs/2506.17141

The fundamental problem of risk prediction for individuals: health AI, uncertainty, and personalized medicine
Background: Clinical prediction models for a health condition are commonly evaluated regarding performance for a population, although decisions are made for individuals. The classic view relates uncertainty in risk estimates for individuals to sample size (estimation uncertainty) but uncertainty can also be caused by model uncertainty (variability in modeling choices) and applicability uncertainty (variability in measurement procedures and between populations). Methods: We used real and synthet…

#GISjob at Uni Twente: https://utwentecareers.nl/en/vacancies/2140/phd-candidate-in-explainable-ai-for-earth-observation/

PhD Candidate in Explainable AI for Earth Observation - Looking for a job that matters?
With the growing integration of Artificial Intelligence (AI) in geospatial decision-making processes, ensuring transparency, accountability, and trust in model outputs has become imperative, especially in critical domains such as disaster risk management,…

This https://arxiv.org/abs/2505.23436 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

Emergent Risk Awareness in Rational Agents under Resource Constraints
Advanced reasoning models with agentic capabilities (AI agents) are deployed to interact with humans and to solve sequential decision-making problems under (approximate) utility functions and internal models. When such problems have resource or failure constraints where action sequences may be forcibly terminated once resources are exhausted, agents face implicit trade-offs that reshape their utility-driven (rational) behaviour. Additionally, since these agents are typically commissioned by a h…

Mitigating Gambling-Like Risk-Taking Behaviors in Large Language Models: A Behavioral Economics Approach to AI Safety
Y. Du
https://arxiv.org/abs/2506.22496

Mitigating Gambling-Like Risk-Taking Behaviors in Large Language Models: A Behavioral Economics Approach to AI Safety
Large Language Models (LLMs) exhibit systematic risk-taking behaviors analogous to those observed in gambling psychology, including overconfidence bias, loss-chasing tendencies, and probability misjudgment. Drawing from behavioral economics and prospect theory, we identify and formalize these "gambling-like" patterns where models sacrifice accuracy for high-reward outputs, exhibit escalating risk-taking after errors, and systematically miscalibrate uncertainty. We propose the Risk-Aware Respons…

A look at California's 52-page California Report on Frontier Policy, which proposes an AI regulatory framework based on transparency, risk assessments, and more (Hayden Field/The Verge)
https://www.theverge.com/ai-artificial-int

California is trying to regulate its AI giants — again
California has released a safety report proposing new legislation after Governor Gavin Newsom vetoed SB 1047.

VisText-Mosquito: A Multimodal Dataset and Benchmark for AI-Based Mosquito Breeding Site Detection and Reasoning
Md. Adnanul Islam, Md. Faiyaz Abdullah Sayeedi, Md. Asaduzzaman Shuvo, Muhammad Ziaur Rahman, Shahanur Rahman Bappy, Raiyan Rahman, Swakkhar Shatabda
https://arxiv.org/abs/2506.14629

VisText-Mosquito: A Multimodal Dataset and Benchmark for AI-Based Mosquito Breeding Site Detection and Reasoning
Mosquito-borne diseases pose a major global health risk, requiring early detection and proactive control of breeding sites to prevent outbreaks. In this paper, we present VisText-Mosquito, a multimodal dataset that integrates visual and textual data to support automated detection, segmentation, and reasoning for mosquito breeding site analysis. The dataset includes 1,828 annotated images for object detection, 142 images for water surface segmentation, and natural language reasoning texts linked…

A Question Bank to Assess AI Inclusivity: Mapping out the Journey from Diversity Errors to Inclusion Excellence
Rifat Ara Shams, Didar Zowghi, Muneera Bano
https://arxiv.org/abs/2506.18538

A Question Bank to Assess AI Inclusivity: Mapping out the Journey from Diversity Errors to Inclusion Excellence
Ensuring diversity and inclusion (D&I) in artificial intelligence (AI) is crucial for mitigating biases and promoting equitable decision-making. However, existing AI risk assessment frameworks often overlook inclusivity, lacking standardized tools to measure an AI system's alignment with D&I principles. This paper introduces a structured AI inclusivity question bank, a comprehensive set of 253 questions designed to evaluate AI inclusivity across five pillars: Humans, Data, Process, System, and …

DREAM: On hallucinations in AI-generated content for nuclear medicine imaging
Menghua Xia, Reimund Bayerlein, Yanis Chemli, Xiaofeng Liu, Jinsong Ouyang, Georges El Fakhri, Ramsey D. Badawi, Quanzheng Li, Chi Liu
https://arxiv.org/abs/2506.13995

DREAM: On hallucinations in AI-generated content for nuclear medicine imaging
Artificial intelligence-generated content (AIGC) has shown remarkable performance in nuclear medicine imaging (NMI), offering cost-effective software solutions for tasks such as image enhancement, motion correction, and attenuation correction. However, these advancements come with the risk of hallucinations, generating realistic yet factually incorrect content. Hallucinations can misrepresent anatomical and functional information, compromising diagnostic accuracy and clinical trust. This paper …

What has ... DRM (?!) ever done for us?
https://infosec.exchange/@dangoodin/114546912959367470

Social Group Bias in AI Finance
Thomas R. Cook, Sophia Kazinnik
https://arxiv.org/abs/2506.17490 https://arxiv.org/pdf/2506.17490

Social Group Bias in AI Finance
Financial institutions increasingly rely on large language models (LLMs) for high-stakes decision-making. However, these models risk perpetuating harmful biases if deployed without careful oversight. This paper investigates racial bias in LLMs specifically through the lens of credit decision-making tasks, operating on the premise that biases identified here are indicative of broader concerns across financial applications. We introduce a reproducible, counterfactual testing framework that evalua…

This https://arxiv.org/abs/2505.18422 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCY_…

A Task-Driven Human-AI Collaboration: When to Automate, When to Collaborate, When to Challenge
According to several empirical investigations, despite en-hancing human capabilities, human-AI cooperation fre-quently falls short of expectations and fails to reach true synergy. We propose a task-driven framework that reverses prevalent approaches by assigning AI roles according to how the task's requirements align with the capabilities of AI technology. Three major AI roles are identified through task analysis across risk and complexity dimensions: au-tonomous, assistive/collaborative, and a…

Calculating Software's Energy Use and Carbon Emissions: A Survey of the State of Art, Challenges, and the Way Ahead
Priyavanshi Pathania, Nikhil Bamby, Rohit Mehra, Samarth Sikand, Vibhu Saujanya Sharma, Vikrant Kaulgud, Sanjay Podder, Adam P. Burden
https://arxiv.org/abs/2506.09683 …

Calculating Software's Energy Use and Carbon Emissions: A Survey of the State of Art, Challenges, and the Way Ahead
The proliferation of software and AI comes with a hidden risk: its growing energy and carbon footprint. As concerns regarding environmental sustainability come to the forefront, understanding and optimizing how software impacts the environment becomes paramount. In this paper, we present a state-of-the-art review of methods and tools that enable the measurement of software and AI-related energy and/or carbon emissions. We introduce a taxonomy to categorize the existing work as Monitoring, Estim…

Mapping Caregiver Needs to AI Chatbot Design: Strengths and Gaps in Mental Health Support for Alzheimer's and Dementia Caregivers
Jiayue Melissa Shi, Dong Whi Yoo, Keran Wang, Violeta J. Rodriguez, Ravi Karkar, Koustuv Saha
https://arxiv.org/abs/2506.15047

Mapping Caregiver Needs to AI Chatbot Design: Strengths and Gaps in Mental Health Support for Alzheimer's and Dementia Caregivers
Family caregivers of individuals with Alzheimer's Disease and Related Dementia (AD/ADRD) face significant emotional and logistical challenges that place them at heightened risk for stress, anxiety, and depression. Although recent advances in generative AI -- particularly large language models (LLMs) -- offer new opportunities to support mental health, little is known about how caregivers perceive and engage with such technologies. To address this gap, we developed Carey, a GPT-4o-based chatbot …

Researchers find the first known "zero-click" attack on an AI agent; the now-fixed flaw in Microsoft 365 Copilot would let a hacker attack a user via an email (Sharon Goldman/Fortune)
https://fortune.com/2025/06/11/microsoft-cop…

Exclusive: New Microsoft Copilot flaw signals broader risk of AI agents being hacked—‘I would be terrified’
Microsoft fixed the Copilot flaw, but researchers warn the real danger lies in how all AI agents are built.

Last year I wrote a paper on how "GenAI" is a risk for #citizenscience (it being an academic paper it's still slowly dying in review). One of the points was that using "AI" devalues the contributions made by human volunteers, leading to contributors (rightfully) disengaging.
Yesterday, #inaturalist announced an "AI"-partnership with Google, and unsurprisingly that's exactly the backlash they got…
https://www.inaturalist.org/blog/113184-inaturalist-receives-grant-to-improve-species-suggestions

This https://arxiv.org/abs/2506.03988 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

RAID: A Dataset for Testing the Adversarial Robustness of AI-Generated Image Detectors
AI-generated images have reached a quality level at which humans are incapable of reliably distinguishing them from real images. To counteract the inherent risk of fraud and disinformation, the detection of AI-generated images is a pressing challenge and an active research topic. While many of the presented methods claim to achieve high detection accuracy, they are usually evaluated under idealized conditions. In particular, the adversarial robustness is often neglected, potentially due to a la…

Programming Geotechnical Reliability Algorithms using Generative AI
Atma Sharma, Jie Zhang, Meng Lu, Shuangyi Wu, Baoxiang Li
https://arxiv.org/abs/2506.19536

Programming Geotechnical Reliability Algorithms using Generative AI
Programming reliability algorithms is crucial for risk assessment in geotechnical engineering. This study explores the possibility of automating and accelerating this task using Generative AI based on Large Language Models (LLMs). Specifically, the most popular LLM, i.e., ChatGPT, is used to test the ability to generate MATLAB codes for four classical reliability algorithms. The four specific examples considered in this study are: (1) First Order Reliability Method (FORM); (2) Subset simulation…

This https://arxiv.org/abs/2502.09716 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCY_…

Principles and Policy Recommendations for Comprehensive Genetic Data Governance
Genetic data collection has become ubiquitous, producing genetic information about health, ancestry, and social traits. However, unregulated use, especially amid evolving scientific understanding, poses serious privacy and discrimination risks. These risks are intensified by advancing AI, particularly multi-modal systems integrating genetic, clinical, behavioral, and environmental data. In this work, we organize the uses of genetic data along four distinct "pillars", and develop a risk assessme…

Treefera, which uses satellite imagery, drone imagery, and AI to provide real-time insights into supply chains, raised a $30M Series B led by Notion Capital (Cate Lawrence/Tech.eu)
https://tech.eu/2025/06/03/treefera-secure…

Treefera secures $20M Series B for AI-driven supply chain resilience solutions
Supply chain resilience startup Treeferachas secured $20 million in its Series B funding round, led by Notion Capital with participation from other investors.  In the face of growing economic uncertainty, a stringent regulatory landscape, and increasing environmental risk, Treefera’s platform utilises satellite imagery, drone imagery, and adaptive AI in order to provide real-time, tailored insights into supply chains.  This is especially significant within the first mile of supply chains,…

Artificial Intelligence in Team Dynamics: Who Gets Replaced and Why?
Xienan Cheng, Mustafa Dogan, Pinar Yildirim
https://arxiv.org/abs/2506.12337 https://

Artificial Intelligence in Team Dynamics: Who Gets Replaced and Why?
This study investigates the effects of artificial intelligence (AI) adoption in organizations. We ask: (1) How should a principal optimally deploy limited AI resources to replace workers in a team? (2) In a sequential workflow, which workers face the highest risk of AI replacement? (3) How does substitution with AI affect both the replaced and non-replaced workers' wages? We develop a sequential team production model in which a principal can use peer monitoring -- where each worker observes the…

This https://arxiv.org/abs/2503.01148 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qfi…

Dynamic spillovers and investment strategies across artificial intelligence ETFs, artificial intelligence tokens, and green markets
This paper investigates the risk spillovers among AI ETFs, AI tokens, and green markets using the R2 decomposition method. We reveal several key insights. First, the overall transmission connectedness index (TCI) closely aligns with the contemporaneous TCI, while the lagged TCI is significantly lower. Second, AI ETFs and clean energy act as risk transmitters, whereas AI tokens and green bond function as risk receivers. Third, AI tokens are difficult to hedge and provide limited hedging ability …

Replaced article(s) found for cs.ET. https://arxiv.org/list/cs.ET/new/
[1/1]:
AI as Decision-Maker: Ethics and Risk Preferences of LLMs
https://

Military AI Cyber Agents (MAICAs) Constitute a Global Threat to Critical Infrastructure
Timothy Dubber, Seth Lazar
https://arxiv.org/abs/2506.12094 https:/…

Military AI Cyber Agents (MAICAs) Constitute a Global Threat to Critical Infrastructure
This paper argues that autonomous AI cyber-weapons - Military-AI Cyber Agents (MAICAs) - create a credible pathway to catastrophic risk. It sets out the technical feasibility of MAICAs, explains why geopolitics and the nature of cyberspace make MAICAs a catastrophic risk, and proposes political, defensive-AI and analogue-resilience measures to blunt the threat.

Using Large Language Models to Simulate Human Behavioural Experiments: Port of Mars
Oliver Slumbers, Joel Z. Leibo, Marco A. Janssen
https://arxiv.org/abs/2506.05555

Using Large Language Models to Simulate Human Behavioural Experiments: Port of Mars
Collective risk social dilemmas (CRSD) highlight a trade-off between individual preferences and the need for all to contribute toward achieving a group objective. Problems such as climate change are in this category, and so it is critical to understand their social underpinnings. However, rigorous CRSD methodology often demands large-scale human experiments but it is difficult to guarantee sufficient power and heterogeneity over socio-demographic factors. Generative AI offers a potential comple…

AI-based Approach in Early Warning Systems: Focus on Emergency Communication Ecosystem and Citizen Participation in Nordic Countries
Fuzel Shaik, Getnet Demil, Mourad Oussalah
https://arxiv.org/abs/2506.18926

AI-based Approach in Early Warning Systems: Focus on Emergency Communication Ecosystem and Citizen Participation in Nordic Countries
Climate change and natural disasters are recognized as worldwide challenges requiring complex and efficient ecosystems to deal with social, economic, and environmental effects. This chapter advocates a holistic approach, distinguishing preparedness, emergency responses, and postcrisis phases. The role of the Early Warning System (EWS), Risk modeling and mitigation measures are particularly emphasized. The chapter reviews the various Artificial Intelligence (AI)-enabler technologies that can be …

Contemporary AI foundation models increase biological weapons risk
Roger Brent, T. Greg McKelvey Jr
https://arxiv.org/abs/2506.13798 https://

Contemporary AI foundation models increase biological weapons risk
The rapid advancement of artificial intelligence has raised concerns about its potential to facilitate biological weapons development. We argue existing safety assessments of contemporary foundation AI models underestimate this risk, largely due to flawed assumptions and inadequate evaluation methods. First, assessments mistakenly assume biological weapons development requires tacit knowledge, or skills gained through hands-on experience that cannot be easily verbalized. Second, they rely on im…

Deep Reinforcement Learning for Investor-Specific Portfolio Optimization: A Volatility-Guided Asset Selection Approach
Arishi Orra, Aryan Bhambu, Himanshu Choudhary, Manoj Thakur, Selvaraju Natarajan
https://arxiv.org/abs/2505.03760

Deep Reinforcement Learning for Investor-Specific Portfolio Optimization: A Volatility-Guided Asset Selection Approach
Portfolio optimization requires dynamic allocation of funds by balancing the risk and return tradeoff under dynamic market conditions. With the recent advancements in AI, Deep Reinforcement Learning (DRL) has gained prominence in providing adaptive and scalable strategies for portfolio optimization. However, the success of these strategies depends not only on their ability to adapt to market dynamics but also on the careful pre-selection of assets that influence overall portfolio performance. I…

This https://arxiv.org/abs/2502.04951 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Unsafe LLM-Based Search: Quantitative Analysis and Mitigation of Safety Risks in AI Web Search
Recent advancements in Large Language Models (LLMs) have significantly enhanced the capabilities of AI-Powered Search Engines (AIPSEs), offering precise and efficient responses by integrating external databases with pre-existing knowledge. However, we observe that these AIPSEs raise risks such as quoting malicious content or citing malicious websites, leading to harmful or unverified information dissemination. In this study, we conduct the first safety risk quantification on seven production AI…

Toward a Global Regime for Compute Governance: Building the Pause Button
Ananthi Al Ramiah, Raymond Koopmanschap, Josh Thorsteinson, Sadruddin Khan, Jim Zhou, Shafira Noh, Joep Meindertsma, Farhan Shafiq
https://arxiv.org/abs/2506.20530

Toward a Global Regime for Compute Governance: Building the Pause Button
As AI capabilities rapidly advance, the risk of catastrophic harm from large-scale training runs is growing. Yet the compute infrastructure that enables such development remains largely unregulated. This paper proposes a concrete framework for a global "Compute Pause Button": a governance system designed to prevent dangerously powerful AI systems from being trained by restricting access to computational resources. We identify three key intervention points -- technical, traceability, and regulat…

Foundation Time-Series AI Model for Realized Volatility Forecasting
Anubha Goel, Puneet Pasricha, Martin Magris, Juho Kanniainen
https://arxiv.org/abs/2505.11163

Foundation Time-Series AI Model for Realized Volatility Forecasting
Time series foundation models (FMs) have emerged as a popular paradigm for zero-shot multi-domain forecasting. These models are trained on numerous diverse datasets and claim to be effective forecasters across multiple different time series domains, including financial data. In this study, we evaluate the effectiveness of FMs, specifically the TimesFM model, for volatility forecasting, a core task in financial risk management. We first evaluate TimesFM in its pretrained (zero-shot) form, follow…

The Memory Paradox: Why Our Brains Need Knowledge in an Age of AI
Barbara Oakley, Michael Johnston, Ken-Zen Chen, Eulho Jung, Terrence J. Sejnowski
https://arxiv.org/abs/2506.11015

The Memory Paradox: Why Our Brains Need Knowledge in an Age of AI
In the age of generative AI and ubiquitous digital tools, human cognition faces a structural paradox: as external aids become more capable, internal memory systems risk atrophy. Drawing on neuroscience and cognitive psychology, this paper examines how heavy reliance on AI systems and discovery-based pedagogies may impair the consolidation of declarative and procedural memory -- systems essential for expertise, critical thinking, and long-term retention. We review how tools like ChatGPT and calc…

Replaced article(s) found for econ.GN. https://arxiv.org/list/econ.GN/new/
[1/1]:
AI as Decision-Maker: Ethics and Risk Preferences of LLMs
https:…