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相关概念视频

In- and Out-Groups01:31

In- and Out-Groups

38.9K
People all belong to a gender, race, age, and social economic group. These groups provide a powerful source of our identity and self-esteem (Tajfel & Turner, 1979) and serve as our in-groups. An in-group is a group that we identify with or see ourselves as belonging to.
38.9K
Stereotype Content Model02:16

Stereotype Content Model

14.0K
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...
14.0K
Stereotypes, Prejudice, and Discrimination02:55

Stereotypes, Prejudice, and Discrimination

90.0K
Humans are very diverse and although we share many similarities, we also have many differences. The social groups we belong to help form our identities (Tajfel, 1974). These differences may be difficult for some people to reconcile, which may lead to prejudice toward people who are different. Prejudice is a negative attitude and feeling toward an individual based solely on one’s membership in a particular social group (Allport, 1954; Brown, 2010). Prejudice is common against people who...
90.0K
Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

26
Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
26
The Representativeness Heuristic02:13

The Representativeness Heuristic

15.8K
The representative heuristic describes a biased way of thinking, in which you unintentionally stereotype someone or something. For example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.
15.8K
Levels of Use of a GIS01:29

Levels of Use of a GIS

45
Geographic Information Systems (GIS) operate across three levels of application, each representing an increasing degree of complexity: data management, analysis, and prediction. These levels reflect the expanding functionality and versatility of GIS technology in handling spatial data for diverse purposes.Data ManagementAt its foundational level, GIS serves as a tool for data management, enabling the input, storage, retrieval, and organization of spatial data. This level is often employed in...
45

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Poly2Vec: Polymorphic Fourier-Based Encoding of Geospatial Objects for GeoAI Applications.

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GeoToken: Hierarchical Geolocalization of Images via Next Token Prediction.

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ICAD: A Self-Supervised Autoregressive Approach for Multi-Context Anomaly Detection in Human Mobility Data.

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One Model, Many Cities: A Transferable Social Relationship Inference Framework for Human Mobility Data.

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相关实验视频

Updated: Jun 8, 2025

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.4K

公平的空间索引:一个群体空间公平的范式.

Sina Shaham1, Gabriel Ghinita2, Cyrus Shahabi1

  • 1Viterbi School of Engineering, University of Southern California, Los Angeles, California, USA.

Advances in database technology : proceedings. International Conference on Extending Database Technology
|November 4, 2024
PubMed
概括

本研究介绍了在机器学习 (ML) 中解决位置数据偏差的方法,确保在AI系统中获得更公平的结果. 我们的空间索引算法在不牺牲机器学习模型的准确性的情况下提高了公平性.

科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习
  • 数据科学数据科学数据科学

背景情况:

  • 机器学习 (ML) 模型越来越多地用于贷款批准和招聘等关键决策.
  • 现有的公平性研究往往忽视了地理空间数据,尽管它有可能引入与受保护属性相关的偏见.
  • 移动应用的广泛使用使得位置数据成为ML应用中的一个重要因素.

研究的目的:

  • 调查位置数据对机器学习公平性的影响.
  • 提出和评估用于缓解ML模型中的位置偏差和误校准的技术.
  • 引入空间群公平的概念,并开发解决它的算法.

主要方法:

  • 开发了一种新的空间索引算法,灵感来自KD树,以结合公平性考虑.
  • 专注于解决 ML 中的地理空间属性引起的错误校准问题.
  • 在现实世界的数据上进行了广泛的实验,以验证拟议的方法.

主要成果:

  • 拟议的空间索引算法显著提高了ML模型中的公平性.
  • 这种方法有效地减轻了位置数据带来的偏差.
  • 在提高公平度指标的同时,保持了高学习准确度.

更多相关视频

The Spatial Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition
05:15

The Spatial Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition

Published on: February 19, 2018

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The HoneyComb Paradigm for Research on Collective Human Behavior
06:48

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

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相关实验视频

Last Updated: Jun 8, 2025

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.4K
The Spatial Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition
05:15

The Spatial Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition

Published on: February 19, 2018

10.8K
The HoneyComb Paradigm for Research on Collective Human Behavior
06:48

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

9.3K

结论:

  • 位置数据可以在机器学习系统中引入显著的不公平偏见.
  • 开发的空间索引技术为实现空间组公平性提供了有效的解决方案.
  • 这项工作强调了考虑地理空间属性的重要性,以实现公平的AI发展.