arXiv subjects

Kevin Paeth

Publications and source records attributed to Kevin Paeth.

5 recordsLinked to original sources

arXiv2026

FLARE-AI: Flaw Reporting for AI

Flaw reporting for deployed AI systems is fundamental to identifying system failures and improving AI safety. Yet the AI reporting ecosystem is fragmented: researchers who identify flaws often do not know what or where to report, and groups who receive reports rarely share them with other relevant stakeholders. As a result, good-faith reporters duplicate effort by submitting many different forms, and recipients lack standardized, triage-ready information. We audit 12 reporting systems published by AI developers, cybersecurity groups, and AI flaw aggregators, identifying five recurring design challenges spanning discoverability, scope, information collection, coordination, and guidance for strict-liability cases. Building on this analysis and feedback from 49 experts across 32 organizations representing developers, security researchers, and ecosystem coordinators, we introduce FLARE-AI, an open-source AI flaw reporting system designed for interoperability with existing systems. FLARE-AI streamlines flaw report creation by collecting triage-relevant information through conditional logic and early classification, then enables optional dissemination of standardized, machine-readable reports to multiple developers, coordinators, and incident registries from a single submission. By lowering barriers to reporting AI flaws and improving interoperability across stakeholders, FLARE-AI helps break down silos and accelerate remediation across the AI ecosystem.

cs.CY↗

arXiv2025

Independent Clinical Evaluation of General-Purpose LLM Responses to Signals of Suicide Risk

We introduce findings and methods to facilitate evidence-based discussion about how large language models (LLMs) should behave in response to user signals of risk of suicidal thoughts and behaviors (STB). People are already using LLMs as mental health resources, and several recent incidents implicate LLMs in mental health crises. Despite growing attention, few studies have been able to effectively generalize clinical guidelines to LLM use cases, and fewer still have proposed methodologies that can be iteratively applied as knowledge improves about the elements of human-AI interaction most in need of study. We introduce an assessment of LLM alignment with guidelines for ethical communication, adapted from clinical principles and applied to expressions of risk factors for STB in multi-turn conversations. Using a codebook created and validated by clinicians, mobilizing the volunteer participation of practicing therapists and trainees (N=43) based in the U.S., and using generalized linear mixed-effects models for statistical analysis, we assess a single fully open-source LLM, OLMo-2-32b. We show how to assess when a model deviates from clinically informed guidelines in a way that may pose a hazard and (thanks to its open nature) facilitates future investigation as to why. We find that contrary to clinical best practice, OLMo-2-32b, and, possibly by extension, other LLMs, will become less likely to invite continued dialog as users send more signals of STB risk in multi-turn settings. We also show that OLMo-2-32b responds differently depending on the risk factor expressed. This empirical evidence highlights that just as chatbots pose hazards if their responses reinforce delusions or assist in suicidal acts, they may also discourage further help-seeking or cause feelings of dismissal or abandonment by withdrawing from conversations when STB risk is expressed.

cs.HC↗

arXiv2024

Practice-Informed, Practice-Ready: An AI security incident taxonomy

With the increasing prevalence of AI systems, several real-world AI security incidents have been reported. However, despite forthcoming legal mandates, the reporting and collection of these incidents still lacks practical standards and proposals. We bridge this gap by establishing a rigorous foundation based on discussions with a diverse group of AI practitioners spanning industrial, non-profit, research, and governmental sectors. Our proposed taxonomy provides concrete guidance to identify affected parties, recommend relevant security measures, and gain an actionable overview of the evolving AI security landscape. Our tests show that different coders consistently identify similar topics, but that automating incident tagging via an LLM like ChatGPT is of limited use. Notably, our framework has already served as the scientific basis for an established industry standard, proving its utility and readiness for widespread adoption.

cs.CR↗

arXiv2024

Lessons for Editors of AI Incidents from the AI Incident Database

As artificial intelligence (AI) systems become increasingly deployed across the world, they are also increasingly implicated in AI incidents - harm events to individuals and society. As a result, industry, civil society, and governments worldwide are developing best practices and regulations for monitoring and analyzing AI incidents. The AI Incident Database (AIID) is a project that catalogs AI incidents and supports further research by providing a platform to classify incidents for different operational and research-oriented goals. This study reviews the AIID's dataset of 750+ AI incidents and two independent taxonomies applied to these incidents to identify common challenges to indexing and analyzing AI incidents. We find that certain patterns of AI incidents present structural ambiguities that challenge incident databasing and explore how epistemic uncertainty in AI incident reporting is unavoidable. We therefore report mitigations to make incident processes more robust to uncertainty related to cause, extent of harm, severity, or technical details of implicated systems. With these findings, we discuss how to develop future AI incident reporting practices.

cs.CY↗

arXiv2022

Indexing AI Risks with Incidents, Issues, and Variants

Two years after publicly launching the AI Incident Database (AIID) as a collection of harms or near harms produced by AI in the world, a backlog of "issues" that do not meet its incident ingestion criteria have accumulated in its review queue. Despite not passing the database's current criteria for incidents, these issues advance human understanding of where AI presents the potential for harm. Similar to databases in aviation and computer security, the AIID proposes to adopt a two-tiered system for indexing AI incidents (i.e., a harm or near harm event) and issues (i.e., a risk of a harm event). Further, as some machine learning-based systems will sometimes produce a large number of incidents, the notion of an incident "variant" is introduced. These proposed changes mark the transition of the AIID to a new version in response to lessons learned from editing 2,000+ incident reports and additional reports that fall under the new category of "issue."

cs.CY↗