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Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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Uncertainty: Overview00:59

Uncertainty: Overview

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In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
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Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

4.8K
The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
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Distribution Reliability and Automation01:25

Distribution Reliability and Automation

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Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
167
Unrealistic Optimism Bias01:30

Unrealistic Optimism Bias

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Unrealistic optimism bias is the tendency to overestimate the likelihood of positive outcomes. This cognitive bias makes individuals believe they are less likely to experience failures, setbacks, or risks and more likely to succeed than others. For example, people may assume they are less prone to health issues, accidents, or financial struggles than their peers, even when they share similar risk factors.One key component of this bias is the above-average effect, where individuals perceive...
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相关实验视频

Updated: Sep 18, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

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忽视AI增强安全管道中不确定性传播的风险

Emanuele Mezzi1, Aurora Papotti1, Fabio Massacci1,2

  • 1Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.

Risk analysis : an official publication of the Society for Risk Analysis
|June 22, 2025
PubMed
概括
此摘要是机器生成的。

本研究通过量化自动化管道中的错误传播来解决人工智能增强系统中的不确定性. 它提供了一个框架和模拟器来评估安全关键的人工智能应用中的风险.

关键词:
人工智能的人工智能是人工智能.自动程序修复自动程序修复不确定性量化不确定性量化

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科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 软件工程 软件工程 软件工程

背景情况:

  • 人工智能越来越多地集成到软件开发中,创建自动化管道与不确定的性能的人工智能子系统.
  • 由于潜在的错误传播,这种集成对安全关键领域构成重大风险.
  • 现有的风险分析方法无法充分解决人工智能增强系统中的不确定性.

研究的目的:

  • 开发一个正式的框架来捕捉和量化AI增强软件系统中的不确定性传播.
  • 创建一个模拟器来评估传播错误的影响.
  • 为人工智能系统评估政策提供建议.

主要方法:

  • 在AI管道中正式化不确定性传播的基础.
  • 开发一个模拟器来量化由错误传播引起的不确定性.
  • 进行一个案例研究,以评估传播错误的模拟.

主要成果:

  • 该研究提供了一种方法,以正式捕捉AI管道中的不确定性传播.
  • 开发和评估了一个模拟器,证明了不确定性的量化.
  • 讨论了该方法的通用性和局限性,并提出了评估政策的建议.

结论:

  • 开发的框架和模拟器提供了一种新的方法来评估AI增强系统中的不确定性.
  • 这些发现对于提高AI在关键应用中的安全性和可靠性至关重要.
  • 需要进一步的研究,以扩展这种方法到现实世界的系统,并放松现有的假设.