Knowledge-augmented Risk Assessment (KaRA): a hybrid-intelligence
framework for supporting knowledge-intensive risk assessment of prospect
candidates
- URL: http://arxiv.org/abs/2303.05288v1
- Date: Thu, 9 Mar 2023 14:32:11 GMT
- Title: Knowledge-augmented Risk Assessment (KaRA): a hybrid-intelligence
framework for supporting knowledge-intensive risk assessment of prospect
candidates
- Abstract summary: In many contexts, assessing the Probability of Success (PoS) of prospects heavily depends on experts' knowledge, often leading to biased and inconsistent assessments.
We have developed the framework named KARA to address these issues.
It combines multiple AI techniques that consider SMEs (Subject Matter Experts) feedback on top of a structured domain knowledge-base to support risk assessment processes of prospect candidates.
- Score: 2.3311636727756055
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Evaluating the potential of a prospective candidate is a common task in
multiple decision-making processes in different industries. We refer to a
prospect as something or someone that could potentially produce positive
results in a given context, e.g., an area where an oil company could find oil,
a compound that, when synthesized, results in a material with required
properties, and so on. In many contexts, assessing the Probability of Success
(PoS) of prospects heavily depends on experts' knowledge, often leading to
biased and inconsistent assessments. We have developed the framework named KARA
(Knowledge-augmented Risk Assessment) to address these issues. It combines
multiple AI techniques that consider SMEs (Subject Matter Experts) feedback on
top of a structured domain knowledge-base to support risk assessment processes
of prospect candidates in knowledge-intensive contexts.
Related papers
- Open Problems in AI Risk Modeling: Insights from a Workshop on the Technical Foundations of AI Risk Modeling [0.0]
We investigate the design of robust risk models to assess societal risks posed by advanced AI systems.<n>We identify the key methodological and institutional challenges that currently limit the adoption of risk modeling.
arXiv Detail & Related papers (2026-09-02T21:42:09Z) - Applications of Risk Science to AI Fairness Evaluation: Principles, Challenges, and Best Practices [8.423021413553464]
We conduct a literature review of works purporting to evaluate the bias or fairness of technological systems used for tasks related to hiring and employment.<n>We find that most characterize the severity of bias- or fairness-related consequences but do not follow best practices to characterize the uncertainty around either the occurrence of these consequences or severity estimates.<n>Next, we conduct a case study of fairness evaluation for an AI-mediated resume screening task and demonstrate how principles of risk science can be incorporated into such an evaluation.
arXiv Detail & Related papers (2026-08-30T00:13:01Z) - InnoEval: On Research Idea Evaluation as a Knowledge-Grounded, Multi-Perspective Reasoning Problem [87.30601926271864]
InnoEval is a deep innovation evaluation framework designed to emulate human-level idea assessment.<n>We apply a heterogeneous deep knowledge search engine that retrieves and grounds dynamic evidence from diverse online sources.<n>We construct comprehensive datasets derived from authoritative peer-reviewed submissions to benchmark InnoEval.
arXiv Detail & Related papers (2026-02-16T00:40:31Z) - Toward Quantitative Modeling of Cybersecurity Risks Due to AI Misuse [50.87630846876635]
We develop nine detailed cyber risk models.<n>Each model decomposes attacks into steps using the MITRE ATT&CK framework.<n>Individual estimates are aggregated through Monte Carlo simulation.
arXiv Detail & Related papers (2025-12-09T17:54:17Z) - Societal Capacity Assessment Framework: Measuring Resilience to Inform Advanced AI Risk Management [0.14963505712040906]
Societal Capacity Assessment Framework (SCAF) is an indicators-based approach to measuring a society's vulnerability, coping capacity, and adaptive capacity in response to AI-related risks.<n>SCAF adapts established resilience analysis methodologies to AI, enabling organisations to ground risk management in insights about country-level deployment conditions.
arXiv Detail & Related papers (2025-09-26T02:55:53Z) - Expert Preference-based Evaluation of Automated Related Work Generation [54.29459509574242]
We propose GREP, a multi-turn evaluation framework that integrates classical related work evaluation criteria with expert-specific preferences.<n>For better accessibility, we design two variants of GREP: a more precise variant with proprietary LLMs as evaluators, and a cheaper alternative with open-weight LLMs.
arXiv Detail & Related papers (2025-08-11T13:08:07Z) - Adapting Probabilistic Risk Assessment for AI [0.0]
General-purpose artificial intelligence (AI) systems present an urgent risk management challenge.
Current methods often rely on selective testing and undocumented assumptions about risk priorities.
This paper introduces the probabilistic risk assessment (PRA) for AI framework.
arXiv Detail & Related papers (2025-04-25T17:59:14Z) - AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons [62.374792825813394]
This paper introduces AILuminate v1.0, the first comprehensive industry-standard benchmark for assessing AI-product risk and reliability.
The benchmark evaluates an AI system's resistance to prompts designed to elicit dangerous, illegal, or undesirable behavior in 12 hazard categories.
arXiv Detail & Related papers (2025-02-19T05:58:52Z) - Human services organizations and the responsible integration of AI: Considering ethics and contextualizing risk(s) [0.0]
Authors argue that ethical concerns about AI deployment vary significantly based on implementation context and specific use cases.
They propose a dimensional risk assessment approach that considers factors like data sensitivity, professional oversight requirements, and potential impact on client wellbeing.
arXiv Detail & Related papers (2025-01-20T19:38:21Z) - Mapping Public Perception of Artificial Intelligence: Expectations, Risk-Benefit Tradeoffs, and Value As Determinants for Societal Acceptance [0.20971479389679332]
Using a representative sample of 1100 participants from Germany, this study examines mental models of AI.
Participants quantitatively evaluated 71 statements about AI's future capabilities.
We present rankings of these projections alongside visual mappings illustrating public risk-benefit tradeoffs.
arXiv Detail & Related papers (2024-11-28T20:03:01Z) - Responsible AI in Open Ecosystems: Reconciling Innovation with Risk Assessment and Disclosure [4.578401882034969]
We focus on how model performance evaluation may inform or inhibit probing of model limitations, biases, and other risks.
Our findings can inform AI providers and legal scholars in designing interventions and policies that preserve open-source innovation while incentivizing ethical uptake.
arXiv Detail & Related papers (2024-09-27T19:09:40Z) - 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) - Reasons to Doubt the Impact of AI Risk Evaluations [0.0]
This paper asks whether evaluations significantly improve our understanding of AI risks and our ability to mitigate those risks.
It concludes with considerations for improving evaluation practices and 12 recommendations for AI labs, external evaluators, regulators, and academic researchers.
arXiv Detail & Related papers (2024-08-05T15:42:51Z) - Privacy Risks of General-Purpose AI Systems: A Foundation for Investigating Practitioner Perspectives [47.17703009473386]
Powerful AI models have led to impressive leaps in performance across a wide range of tasks.
Privacy concerns have led to a wealth of literature covering various privacy risks and vulnerabilities of AI models.
We conduct a systematic review of these survey papers to provide a concise and usable overview of privacy risks in GPAIS.
arXiv Detail & Related papers (2024-07-02T07:49:48Z) - Quantitative AI Risk Assessments: Opportunities and Challenges [9.262092738841979]
AI-based systems are increasingly being leveraged to provide value to organizations, individuals, and society.
Risks have led to proposed regulations, litigation, and general societal concerns.
This paper explores the concept of a quantitative AI Risk Assessment.
arXiv Detail & Related papers (2022-09-13T21:47:25Z) - Uncertainty-Driven Action Quality Assessment [67.20617610820857]
We propose a novel probabilistic model, named Uncertainty-Driven AQA (UD-AQA), to capture the diversity among multiple judge scores.
We generate the estimation of uncertainty for each prediction, which is employed to re-weight AQA regression loss.
Our proposed method achieves competitive results on three benchmarks including the Olympic events MTL-AQA and FineDiving, and the surgical skill JIGSAWS datasets.
arXiv Detail & Related papers (2022-07-29T07:21:15Z) - A Unified End-to-End Retriever-Reader Framework for Knowledge-based VQA [67.75989848202343]
This paper presents a unified end-to-end retriever-reader framework towards knowledge-based VQA.
We shed light on the multi-modal implicit knowledge from vision-language pre-training models to mine its potential in knowledge reasoning.
Our scheme is able to not only provide guidance for knowledge retrieval, but also drop these instances potentially error-prone towards question answering.
arXiv Detail & Related papers (2022-06-30T02:35:04Z) - 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) - Towards a multi-stakeholder value-based assessment framework for
algorithmic systems [76.79703106646967]
We develop a value-based assessment framework that visualizes closeness and tensions between values.
We give guidelines on how to operationalize them, while opening up the evaluation and deliberation process to a wide range of stakeholders.
arXiv Detail & Related papers (2022-05-09T19:28:32Z) - What's a Good Prediction? Challenges in evaluating an agent's knowledge [0.9281671380673306]
We show the conflict between accuracy and usefulness of general knowledge.
We propose an alternate evaluation approach that arises continually in the online continual learning setting.
This paper contributes a first look into evaluation of predictions through their use.
arXiv Detail & Related papers (2020-01-23T21:44:43Z)
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.