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相关概念视频

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Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
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Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
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Today, scientists agree that good research is ethical in nature and is guided by a basic respect for human dignity and safety. However, this has not always been the case. Modern researchers must demonstrate that the research they perform is ethically sound.
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If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances...
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在可信的研究环境中使用机器学习模型 - 了解运营风险

Felix Ritchie1, Amy Tilbrook2, Christian Cole3

  • 1Bristol Business School, University of the West of England, Coldharbour Lane, Bristol BS16 1QY.

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概括
此摘要是机器生成的。

值得信赖的研究环境 (TREs) 面临来自机器学习 (ML) 模型的新披露风险. 了解这些新风险对于TRE管理人员来说至关重要,以安全地使用ML进行数据分析.

关键词:
人工智能的人工智能是人工智能.的保密性,保密性.数据封闭区数据封闭区.机器学习是机器学习.输出检查 输出检查值得信赖的研究环境

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科学领域:

  • 数据安全 数据安全
  • 计算机科学 计算机科学
  • 统计披露控制 统计披露控制

背景情况:

  • 值得信赖的研究环境 (TREs) 提供对敏感数据的安全访问,使用手动检查来减轻披露风险.
  • 机器学习 (ML) 模型虽然强大,但在TRE中对个人数据进行培训时,会引入独特且可扩展的披露风险.

研究的目的:

  • 为TRE管理人员介绍ML披露风险带来的概念挑战.
  • 概述正在进行的工作,以解决TRE中的这些新风险.

主要方法:

  • 与传统的统计输出相比,证明ML披露风险的质量不同.
  • 分析 ML 模型开发产生的风险的规模和类型.

主要成果:

  • 在ML披露风险管理中确定了大量尚未解决的问题.
  • 强调在特定领域的进展,同时承认剩余的不确定性.
  • 提交了对TRE的可用补救反应.

结论:

  • 目前,ML模型的披露检查是一个专业领域.
  • TRE 管理人员需要对 ML 风险的基础知识,以便对使用 TRE 进行 ML 开发做出明智的决定.