阿尔法和偏见:通过内在重量调整改善α大小的最坏情况公平性
概括
这项研究引入了阿尔法大小的最坏情况下的公平性,这是当人口统计数据缺失时群体公平性的新方法. 它使用样本重权和随机学习算法来提高公平性和数据隐私,优于现有的方法.
科学领域:
- 计算机科学 计算机科学
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 最坏的情况是,群体公平往往需要人口统计数据,这在现实应用中经常是不可用的.
- 现有的方法与数据隐私和缺少人口信息的实际限制作斗争.
研究的目的:
- 提出一个新的框架,用于最坏情况下的集团公平,使用一个简单的设置,称为α大小的最坏情况下的公平.
- 为了解决小组公平性和数据隐私之间的未经探索的联系.
- 为培养公平的机器学习模型开发高效,强大的培训方法.
主要方法:
- 引入了重权方法,根据公平性贡献分配样本权重.
- 开发了一个随机学习算法,以高效地处理全球最坏的目标.
- 提出了一个强大的变体,以减轻异常值的影响.
主要成果:
- 证明了阿尔法大小的最坏情况公平性对数据隐私的相关性.
- 展示了拟议的重权方法与现有的通过重权衡的公平性技术相连.
- 在标准公平性基准上,经验验证的优异绩效与强有力的基线相比.
结论:
- 拟议的阿尔法大小的最坏情况公平性框架为在有限的人口数据下实现集团公平性提供了切实可行的解决方案.
- 开发的方法为机器学习公平性提供了一种高效,稳健和隐私意识的方法.
- 这项工作弥合了理论上的公平目标和实际实施挑战之间的差距.
更多相关视频
08:24The Joint Effect of Social Comparison and Social Distance on Evaluation of Intertemporal Choice Outcomes in Event-related Potential Studies
Published on: August 25, 2023
806
07:35Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
Published on: October 11, 2018
7.6K
相关概念视频
Weighted Mean
5.3K
While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
5.3K
Improving Translational Accuracy
11.9K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
11.9K
One-Way ANOVA: Unequal Sample Sizes
5.9K
One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
5.9K
Bias
5.0K
Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
5.0K
Routh-Hurwitz Criterion II
418
In the application of the Routh-Hurwitz criterion, two specific scenarios can arise that complicate stability analysis.
The first scenario occurs when a singular zero appears in the first column of the Routh table. This situation creates a division by zero issues. To resolve this, a small positive or negative number, denoted as epsilon (∈), is substituted for the zero. The stability analysis proceeds by assuming a sign for ∈. If ∈ is positive, any sign change in the first...
The first scenario occurs when a singular zero appears in the first column of the Routh table. This situation creates a division by zero issues. To resolve this, a small positive or negative number, denoted as epsilon (∈), is substituted for the zero. The stability analysis proceeds by assuming a sign for ∈. If ∈ is positive, any sign change in the first...
418
Reducing Line Loss
196
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
196
