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相关概念视频

The Anchoring-and-Adjustment Heuristic01:25

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In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. However, sometimes, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the...
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In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
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A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light...
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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Quality Assurance01:19

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Quality assurance is the overarching term used to describe the activities employed to ensure the proper performance of a system. These activities can be classified into three categories: quality control, quality assessment, and internal corrective measures. Typically, these activities work cyclically: quality control is performed before and during the analysis, while quality assessment occurs during and after the investigation. Internal corrective measures are implemented based on the findings...
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Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
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基于搜索的自动修复,以确保决策软件的公平性和准确性.

Max Hort1, Jie M Zhang2, Federica Sarro3

  • 1Simula Research Laboratory, Oslo, Norway.

Empirical software engineering
|January 8, 2024
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概括
此摘要是机器生成的。

本研究引入了一种新的基于搜索的方法来解决机器学习 (ML) 软件中的公平问题. 它同时提高了公平性和准确性,与降低准确性的旧方法不同.

关键词:
减轻偏见的偏见分类 分类 分类 分类.多目标优化多目标优化软件的公平性 软件的公平性

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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:

  • 机器学习 (ML) 决策软件可以表现出公平性问题,根据性别或种族等敏感属性不公平对待个人.
  • 现有的偏差缓解技术往往会降低模型的准确性以实现公平性,这对负责任的软件开发构成了挑战.

研究的目的:

  • 提出一种基于多目标搜索的新方法,用于修复机器学习软件中的公平性问题.
  • 展示一种在二元分类模型中同时提高公平性和准确性的方法,而没有典型的准确性权衡.

主要方法:

  • 开发了一种基于搜索的新算法,用于ML模型中的偏差缓解.
  • 将该方法应用于后勤回归和决策树,这是软件公平性研究中广泛使用的模型.
  • 通过使用三个公平度指标,将拟议的方法与七种最先进的偏见缓解技术进行了比较.

主要成果:

  • 在61%的研究案例中,提出的方法成功地提高了准确性和公平性.
  • 相比之下,现有的最先进的方法在试图减少偏差的同时,始终降低了准确性.
  • 这种新的方法可以在不影响预测性能的情况下改善公平性.

结论:

  • 这项研究为二进制分类提供了第一个基于多目标搜索的偏差缓解方法,该方法不以准确性换取公平性.
  • 软件工程师现在可以提高机器学习模型的公平性,而不必担心精度下降.
  • 该方法有助于创建更负责任和公平的ML驱动的决策系统.