An ontological analysis of risk in Basic Formal Ontology
- URL: http://arxiv.org/abs/2507.21171v1
- Date: Sat, 26 Jul 2025 00:44:47 GMT
- Title: An ontological analysis of risk in Basic Formal Ontology
- Authors: Federico Donato, Adrien Barton,
- Abstract summary: The paper explores the nature of risk, providing a characterization using the categories of the Basic Formal Ontology (BFO)<n>It argues that the category Risk is a subclass of BFO:Role, contrasting it with a similar view classifying Risk as a subclass of BFO:Disposition.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The paper explores the nature of risk, providing a characterization using the categories of the Basic Formal Ontology (BFO). It argues that the category Risk is a subclass of BFO:Role, contrasting it with a similar view classifying Risk as a subclass of BFO:Disposition. This modeling choice is applied on one example of risk, which represents objects, processes (both physical and mental) and their interrelations, then generalizing from the instances in the example to obtain an overall analysis of risk, making explicit what are the sufficient conditions for being a risk. Plausible necessary conditions are also mentioned for future work. Index Terms: ontology, risk, BFO, role, disposition
Related papers
- Explaining Risks: Axiomatic Risk Attributions for Financial Models [0.0]
In recent years, machine learning models have achieved great success at the expense of highly complex black-box structures.<n>In high-risk sectors such as finance, risk is just as important as mean predictions.
arXiv Detail & Related papers (2025-06-07T04:15:27Z) - Adapting Probabilistic Risk Assessment for AI [0.0]
General-purpose artificial intelligence (AI) systems present an urgent risk management challenge.<n>Current methods often rely on selective testing and undocumented assumptions about risk priorities.<n>This paper introduces the probabilistic risk assessment (PRA) for AI framework.
arXiv Detail & Related papers (2025-04-25T17:59:14Z) - WATCHDOG: an ontology-aWare risk AssessmenT approaCH via object-oriented DisruptiOn Graphs [0.9387233631570749]
The Common Ontology of Value and Risk (COVER) highlights how the role of objects and their relationships remains pivotal to performing transparent, complete and accountable risk assessment.<n>We operationalize some of the notions proposed by COVER by presenting a new framework for risk assessment: WATCHDOG.
arXiv Detail & Related papers (2024-12-18T15:44:04Z) - Risks and NLP Design: A Case Study on Procedural Document QA [52.557503571760215]
We argue that clearer assessments of risks and harms to users will be possible when we specialize the analysis to more concrete applications and their plausible users.
We conduct a risk-oriented error analysis that could then inform the design of a future system to be deployed with lower risk of harm and better performance.
arXiv Detail & Related papers (2024-08-16T17:23:43Z) - EARBench: Towards Evaluating Physical Risk Awareness for Task Planning of Foundation Model-based Embodied AI Agents [53.717918131568936]
Embodied artificial intelligence (EAI) integrates advanced AI models into physical entities for real-world interaction.<n>Foundation models as the "brain" of EAI agents for high-level task planning have shown promising results.<n>However, the deployment of these agents in physical environments presents significant safety challenges.<n>This study introduces EARBench, a novel framework for automated physical risk assessment in EAI scenarios.
arXiv Detail & Related papers (2024-08-08T13:19:37Z) - Towards Probing Speech-Specific Risks in Large Multimodal Models: A Taxonomy, Benchmark, and Insights [50.89022445197919]
We propose a speech-specific risk taxonomy, covering 8 risk categories under hostility (malicious sarcasm and threats), malicious imitation (age, gender, ethnicity), and stereotypical biases (age, gender, ethnicity)
Based on the taxonomy, we create a small-scale dataset for evaluating current LMMs capability in detecting these categories of risk.
arXiv Detail & Related papers (2024-06-25T10:08:45Z) - A Farewell to Harms: Risk Management for Medical Devices via the Riskman Ontology & Shapes [1.2435714232193142]
We introduce the Riskman ontology & shapes for representing and analysing information about risk management for medical devices.
Our proposed methodology has the potential to save many person-hours for both manufacturers and notified bodies.
arXiv Detail & Related papers (2024-05-16T07:53:07Z) - On the Societal Impact of Open Foundation Models [93.67389739906561]
We focus on open foundation models, defined here as those with broadly available model weights.
We identify five distinctive properties of open foundation models that lead to both their benefits and risks.
arXiv Detail & Related papers (2024-02-27T16:49:53Z) - C-RAG: Certified Generation Risks for Retrieval-Augmented Language Models [57.10361282229501]
We propose C-RAG, the first framework to certify generation risks for RAG models.
Specifically, we provide conformal risk analysis for RAG models and certify an upper confidence bound of generation risks.
We prove that RAG achieves a lower conformal generation risk than that of a single LLM when the quality of the retrieval model and transformer is non-trivial.
arXiv Detail & Related papers (2024-02-05T16:46:16Z) - Self-Certifying Classification by Linearized Deep Assignment [65.0100925582087]
We propose a novel class of deep predictors for classifying metric data on graphs within PAC-Bayes risk certification paradigm.
Building on the recent PAC-Bayes literature and data-dependent priors, this approach enables learning posterior distributions on the hypothesis space.
arXiv Detail & Related papers (2022-01-26T19:59:14Z) - Fighting Money Laundering with Statistics and Machine Learning [95.42181254494287]
There is little scientific literature on statistical and machine learning methods for anti-money laundering.
We propose a unifying terminology with two central elements: (i) client risk profiling and (ii) suspicious behavior flagging.
arXiv Detail & Related papers (2022-01-11T21:31:18Z) - Quantifying Uncertainty in Risk Assessment using Fuzzy Theory [0.0]
Risk specialists are trying to understand risk better and use complex models for risk assessment.
Traditional risk models are based on classical set theory.
We will discuss the methodology, framework, and process of using fuzzy logic systems in risk assessment.
arXiv Detail & Related papers (2020-09-20T02:12:44Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.