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相关概念视频

Stereotype Content Model02:16

Stereotype Content Model

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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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Stereotype Threat and Self-fulfilling Prophecies02:09

Stereotype Threat and Self-fulfilling Prophecies

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When we hold a stereotype about a person, we have expectations that he or she will fulfill that stereotype. A self-fulfilling prophecy is an expectation held by a person that alters his or her behavior in a way that tends to make it true. When we hold stereotypes about a person, we tend to treat the person according to our expectations. This treatment can influence the person to act according to our stereotypic expectations, thus confirming our stereotypic beliefs. Research by Rosenthal and...
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Confirmation Biases01:31

Confirmation Biases

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The confirmation bias is the tendency to focus on information that confirms our existing beliefs and ignore information that is inconsistent with our expectations. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis. Have you ever fallen prey to the confirmation bias, either as the source or target of such bias?
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Quantitative Analysis01:12

Quantitative Analysis

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Quantitative analysis is a technique for measuring the amount of specific constituents in a sample. When the sample's composition is unknown, qualitative analysis is performed first to identify its components, which ensures that the correct substances are measured during the quantitative phase.
In quantitative analysis, two key measurements are made: the sample quantity and a property proportional to the amount of the analyte (the substance being analyzed). This forms the basis of the...
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Case Studies01:22

Case Studies

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There are many research methods available to psychologists in their efforts to understand, describe, and explain behavior and the cognitive and biological processes that underlie it.
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使用可解释的AI对女性学生对金融技术的看法进行细分.

Christos Adam1,2

  • 1Department of Economics, University of Crete, Rethymnon, Greece.

Frontiers in artificial intelligence
|December 27, 2024
PubMed
概括

女性采用金融技术 (金融科技) 显示出两个群体:"金融科技友好"和"金融科技怀疑". 感知到的好处,如方便驱动Fintech采用,告知利益相关者战略.

科学领域:

  • 社会科学 社会科学 社会科学
  • 技术与创新 技术与创新
  • 性别研究是性别研究.

背景情况:

  • 建议金融技术 (金融科技) 减少性别差距.
  • 有证据表明,金融科技尚未实现这一目标.
  • 妇女对金融科技工具的看法和采用情况尚不清楚.

研究的目的:

  • 细分女性对金融科技工具的看法.
  • 使用机器学习来解释这些细分.
  • 确定影响女性采用金融科技的因素.

主要方法:

  • 机器学习技术用于细分.
  • 分析的重点是确定细分市场之间的关键差异化因素.
  • 对细分特征的定性解释.

主要成果:

  • 两个不同的细分市场出现了:"金融科技友好"和"金融科技怀疑".
  • "金融科技友好"群体的关键驱动因素包括感知到的好处:易用性,时空便利性和整体优势性质.
  • 导致怀疑的因素需要进一步调查.

结论:

关键词:
金融科技公司这就是 SHAP SHAP 的意思.在XAI,XAI就是XAI.它们的分类是分类分类.集群集成是指集群集成.机器学习是机器学习.频谱聚类是指光谱聚类.女人 女人 女人 女人 女人

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  • 女性对金融科技的看法不均,需要有针对性的方法.
  • 强调方便性和易用性可以鼓励金融科技的采用.
  • 利益相关者应该考虑为不同细分市场量身定制的教育和用户体验.