A Framework for Institutional Risk Identification using Knowledge Graphs and Automated News Profiling

• URL: http://arxiv.org/abs/2109.09103v1
• Date: Sun, 19 Sep 2021 11:06:12 GMT
• Title: A Framework for Institutional Risk Identification using Knowledge Graphs and Automated News Profiling
• Authors: Mahmoud Mahfouz, Armineh Nourbakhsh, Sameena Shah
• Abstract summary: Organizations around the world face an array of risks impacting their operations globally. It is imperative to have a robust risk identification process to detect and evaluate the impact of potential risks before they materialize.
• Score: 5.631924211771643
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Organizations around the world face an array of risks impacting their operations globally. It is imperative to have a robust risk identification process to detect and evaluate the impact of potential risks before they materialize. Given the nature of the task and the current requirements of deep subject matter expertise, most organizations utilize a heavily manual process. In our work, we develop an automated system that (a) continuously monitors global news, (b) is able to autonomously identify and characterize risks, (c) is able to determine the proximity of reaching triggers to determine the distance from the manifestation of the risk impact and (d) identifies organization's operational areas that may be most impacted by the risk. Other contributions also include: (a) a knowledge graph representation of risks and (b) relevant news matching to risks identified by the organization utilizing a neural embedding model to match the textual description of a given risk with multi-lingual news.

Related papers

• AI Risk Categorization Decoded (AIR 2024): From Government Regulations to Corporate Policies [88.32153122712478]
  We identify 314 unique risk categories organized into a four-tiered taxonomy. At the highest level, this taxonomy encompasses System & Operational Risks, Content Safety Risks, Societal Risks, and Legal & Rights Risks. We aim to advance AI safety through information sharing across sectors and the promotion of best practices in risk mitigation for generative AI models and systems.
  arXiv  Detail & Related papers  (2024-06-25T18:13:05Z)
• AI and the Iterable Epistopics of Risk [1.26404863283601]
  The risks AI presents to society are broadly understood to be manageable through general calculus. This paper elaborates how risk is apprehended and managed by a regulator, developer and cyber-security expert.
  arXiv  Detail & Related papers  (2024-04-29T13:33:22Z)
• A Framework for Exploring the Consequences of AI-Mediated Enterprise Knowledge Access and Identifying Risks to Workers [3.4568218861862556]
  This paper presents the Consequence-Mechanism-Risk framework to identify risks to workers from AI-mediated enterprise knowledge access systems. We have drawn on wide-ranging literature detailing risks to workers, and categorised risks as being to worker value, power, and wellbeing. Future work could apply this framework to other technological systems to promote the protection of workers and other groups.
  arXiv  Detail & Related papers  (2023-12-08T17:05:40Z)
• RiskBench: A Scenario-based Benchmark for Risk Identification [4.263035319815899]
  This work focuses on risk identification, the process of identifying and analyzing risks stemming from dynamic traffic participants and unexpected events. We introduce textbfRiskBench, a large-scale scenario-based benchmark for risk identification. We assess the ability of ten algorithms to (1) detect and locate risks, (2) anticipate risks, and (3) facilitate decision-making.
  arXiv  Detail & Related papers  (2023-12-04T06:21:22Z)
• RiskQ: Risk-sensitive Multi-Agent Reinforcement Learning Value Factorization [49.26510528455664]
  We introduce the Risk-sensitive Individual-Global-Max (RIGM) principle as a generalization of the Individual-Global-Max (IGM) and Distributional IGM (DIGM) principles. We show that RiskQ can obtain promising performance through extensive experiments.
  arXiv  Detail & Related papers  (2023-11-03T07:18:36Z)
• Capsa: A Unified Framework for Quantifying Risk in Deep Neural Networks [142.67349734180445]
  Existing algorithms that provide risk-awareness to deep neural networks are complex and ad-hoc. Here we present capsa, a framework for extending models with risk-awareness.
  arXiv  Detail & Related papers  (2023-08-01T02:07:47Z)
• On the Security Risks of Knowledge Graph Reasoning [71.64027889145261]
  We systematize the security threats to KGR according to the adversary's objectives, knowledge, and attack vectors. We present ROAR, a new class of attacks that instantiate a variety of such threats. We explore potential countermeasures against ROAR, including filtering of potentially poisoning knowledge and training with adversarially augmented queries.
  arXiv  Detail & Related papers  (2023-05-03T18:47:42Z)
• Foveate, Attribute, and Rationalize: Towards Physically Safe and Trustworthy AI [76.28956947107372]
  Covertly unsafe text is an area of particular interest, as such text may arise from everyday scenarios and are challenging to detect as harmful. We propose FARM, a novel framework leveraging external knowledge for trustworthy rationale generation in the context of safety. Our experiments show that FARM obtains state-of-the-art results on the SafeText dataset, showing absolute improvement in safety classification accuracy by 5.9%.
  arXiv  Detail & Related papers  (2022-12-19T17:51:47Z)
• A Survey of Risk-Aware Multi-Armed Bandits [84.67376599822569]
  We review various risk measures of interest, and comment on their properties. We consider algorithms for the regret minimization setting, where the exploration-exploitation trade-off manifests. We conclude by commenting on persisting challenges and fertile areas for future research.
  arXiv  Detail & Related papers  (2022-05-12T02:20:34Z)
• Ethical and social risks of harm from Language Models [22.964941107198023]
  This paper aims to help structure the risk landscape associated with large-scale Language Models (LMs) A wide range of established and anticipated risks are analysed in detail, drawing on multidisciplinary expertise and literature from computer science, linguistics, and social sciences.
  arXiv  Detail & Related papers  (2021-12-08T16:09:48Z)
• Driver-centric Risk Object Identification [25.85690304998681]
  We propose a driver-centric definition of risk, i.e., risky objects influence driver behavior. We formulate the task as a cause-effect problem and present a novel two-stage risk object identification framework. A driver-centric Risk Object Identification dataset is curated to evaluate the proposed system.
  arXiv  Detail & Related papers  (2021-06-24T17:27:32Z)

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.