標題: Titlebook: Discrimination and Privacy in the Information Society; Data Mining and Prof Bart Custers,Toon Calders,Tal Zarsky Book 2013 Springer-Verlag [打印本頁] 作者: hector 時間: 2025-3-21 17:14
書目名稱Discrimination and Privacy in the Information Society影響因子(影響力)
書目名稱Discrimination and Privacy in the Information Society影響因子(影響力)學科排名
書目名稱Discrimination and Privacy in the Information Society網(wǎng)絡公開度
書目名稱Discrimination and Privacy in the Information Society網(wǎng)絡公開度學科排名
書目名稱Discrimination and Privacy in the Information Society被引頻次
書目名稱Discrimination and Privacy in the Information Society被引頻次學科排名
書目名稱Discrimination and Privacy in the Information Society年度引用
書目名稱Discrimination and Privacy in the Information Society年度引用學科排名
書目名稱Discrimination and Privacy in the Information Society讀者反饋
書目名稱Discrimination and Privacy in the Information Society讀者反饋學科排名
作者: ATP861 時間: 2025-3-21 23:18
Studies in Applied Philosophy, Epistemology and Rational Ethicshttp://image.papertrans.cn/e/image/281223.jpg作者: abolish 時間: 2025-3-22 01:05
B. A. Harmon,W. D. Goran,R. S. Harmonand privacy issues of data mining and profiling and solutions (both technological and non-technological) for these issues. A large part of this book is based on research results of a project on how and to what extent legal and ethical rules can be integrated in data mining algorithms to prevent disc作者: 無底 時間: 2025-3-22 07:11
https://doi.org/10.1007/978-94-007-7161-1he ability to interpret and analyze the data, and to base future policies and decisions on the outcome of the analysis determines the value of data. The amounts of data collected nowadays not only offer unprecedented opportunities to improve decision procedures for companies and governments, but als作者: 享樂主義者 時間: 2025-3-22 08:43 作者: HACK 時間: 2025-3-22 15:29 作者: HACK 時間: 2025-3-22 20:24
Informal Housing and Marginal Settlementson records.We discuss the challenging problems in discrimination discovery, and present, in a unified form, a framework based on classification rules extraction and filtering on the basis of legally-grounded interestingness measures. The framework is implemented in the publicly available DCUBE tool.作者: aquatic 時間: 2025-3-22 23:00 作者: 懶鬼才會衰弱 時間: 2025-3-23 04:08
Global Housing Policies and Governancelude discrimination, de-individualisation and stereotyping. To mitigate these risks, the right to privacy is traditionally invoked. However, given the rapid technological developments in the area of profiling, it is questionable whether the right to informational privacy and data protection law prov作者: 忙碌 時間: 2025-3-23 07:55
https://doi.org/10.1007/978-4-431-78147-9ssifiers) premised on historical data. If the historical data was discriminating towards socially and legally protected groups, a model learnt over this data will make discriminatory decisions in the future. As a solution, most of the discrimination free modeling techniques force the treatment of th作者: 支架 時間: 2025-3-23 12:18 作者: aesthetic 時間: 2025-3-23 15:42
Kiyoshi Takami,Kiichiro Hatoyamaed. Combined data from different organizations may then be analyzed, for instance, to investigate how specific groups of suspects move through the system. Such insight is useful for several reasons, for example, to define an effective and coherent safety policy. To integrate or relate judicial data 作者: Glycogen 時間: 2025-3-23 19:32 作者: 軌道 時間: 2025-3-24 02:04 作者: GIST 時間: 2025-3-24 05:09
Victor Temiloluwalase Ojotisa,Shihab Ibrahimat most people do not want to be discriminated because of their gender, religion, nationality, age and so on, especially when those attributes are used for making decisions about them like giving them a job, loan, insurance, etc. Discovering such potential biases and eliminating them from the traini作者: Slit-Lamp 時間: 2025-3-24 08:26 作者: Acupressure 時間: 2025-3-24 10:40 作者: MAIM 時間: 2025-3-24 17:18 作者: LAVA 時間: 2025-3-24 22:37 作者: 流動才波動 時間: 2025-3-25 03:15
Data Dilemmas in the Information Society: Introduction and Overviewand privacy issues of data mining and profiling and solutions (both technological and non-technological) for these issues. A large part of this book is based on research results of a project on how and to what extent legal and ethical rules can be integrated in data mining algorithms to prevent disc作者: 切碎 時間: 2025-3-25 06:51 作者: 譏諷 時間: 2025-3-25 11:24 作者: 該得 時間: 2025-3-25 13:33 作者: 飛來飛去真休 時間: 2025-3-25 18:25
The Discovery of Discriminationon records.We discuss the challenging problems in discrimination discovery, and present, in a unified form, a framework based on classification rules extraction and filtering on the basis of legally-grounded interestingness measures. The framework is implemented in the publicly available DCUBE tool.作者: Licentious 時間: 2025-3-25 22:17 作者: Nmda-Receptor 時間: 2025-3-26 00:42
Risks of Profiling and the Limits of Data Protection Lawlude discrimination, de-individualisation and stereotyping. To mitigate these risks, the right to privacy is traditionally invoked. However, given the rapid technological developments in the area of profiling, it is questionable whether the right to informational privacy and data protection law prov作者: textile 時間: 2025-3-26 06:02
Explainable and Non-explainable Discrimination in Classificationssifiers) premised on historical data. If the historical data was discriminating towards socially and legally protected groups, a model learnt over this data will make discriminatory decisions in the future. As a solution, most of the discrimination free modeling techniques force the treatment of th作者: 諷刺滑稽戲劇 時間: 2025-3-26 09:18 作者: 枕墊 時間: 2025-3-26 15:56
Combining and Analyzing Judicial Databasesed. Combined data from different organizations may then be analyzed, for instance, to investigate how specific groups of suspects move through the system. Such insight is useful for several reasons, for example, to define an effective and coherent safety policy. To integrate or relate judicial data 作者: 延期 時間: 2025-3-26 18:28
Privacy-Preserving Data Mining Techniques: Survey and Challenges tentative taxonomy of PPDM as a field. The main axes of this taxonomy specify what kind of data is being protected, and what is the ownership of the data (centralized or distributed). We comment on the relationship between PPDM and preventing discriminatory use of data mining techniques. We round u作者: debase 時間: 2025-3-26 23:03
Techniques for Discrimination-Free Predictive Modelsassification the goal is to learn a predictive model that classifies future data objects as accurately as possible, yet the predicted labels should be uncorrelated to a given sensitive attribute. For example, the task could be to learn a gender-neutral model that predicts whether a potential client 作者: 懸崖 時間: 2025-3-27 01:49
Direct and Indirect Discrimination Prevention Methodsat most people do not want to be discriminated because of their gender, religion, nationality, age and so on, especially when those attributes are used for making decisions about them like giving them a job, loan, insurance, etc. Discovering such potential biases and eliminating them from the traini作者: curettage 時間: 2025-3-27 06:11 作者: FLEET 時間: 2025-3-27 12:47
The Discovery of Discriminationextraction and filtering on the basis of legally-grounded interestingness measures. The framework is implemented in the publicly available DCUBE tool. As a running example, we use a public dataset on credit scoring.作者: Enthralling 時間: 2025-3-27 15:24 作者: 子女 時間: 2025-3-27 19:34
Privacy-Preserving Data Mining Techniques: Survey and Challengesdata (centralized or distributed). We comment on the relationship between PPDM and preventing discriminatory use of data mining techniques. We round up the chapter by discussing some of the new, arising challenges before PPDM as a field.作者: lanugo 時間: 2025-3-28 01:42
2192-6255 politics and public administration, and other people who ma.Vast amounts of data are nowadays collected, stored and processed, in an effort to assist in ?making a variety of administrative and governmental decisions. These innovative steps considerably improve the speed, effectiveness and quality o作者: zonules 時間: 2025-3-28 03:15 作者: surrogate 時間: 2025-3-28 06:27 作者: 干旱 時間: 2025-3-28 12:14
Zanyar Abdullah,Tahir ?elik,Tolga Celikdata (centralized or distributed). We comment on the relationship between PPDM and preventing discriminatory use of data mining techniques. We round up the chapter by discussing some of the new, arising challenges before PPDM as a field.作者: 或者發(fā)神韻 時間: 2025-3-28 14:40
https://doi.org/10.1007/978-94-007-7161-1mining emerged. In this chapter we position data mining with respect to other data analysis techniques and introduce the most important classes of techniques developed in the area: pattern mining, classification, and clustering and outlier detection. Also related, supporting techniques such as pre-processing and database coupling are discussed.作者: Emmenagogue 時間: 2025-3-28 22:31
What Is Data Mining and How Does It Work?mining emerged. In this chapter we position data mining with respect to other data analysis techniques and introduce the most important classes of techniques developed in the area: pattern mining, classification, and clustering and outlier detection. Also related, supporting techniques such as pre-processing and database coupling are discussed.作者: 包裹 時間: 2025-3-28 22:58
Informal Housing and Marginal Settlementsrights, that is, embodying the logic of negative freedom..The final section will examine situations of overlap between the rights, building upon the Huber and Test-Achats cases. This will lead to final conclusions on how to best articulate these rights.作者: Substance-Abuse 時間: 2025-3-29 06:15 作者: 逗它小傻瓜 時間: 2025-3-29 11:19 作者: NIB 時間: 2025-3-29 14:46 作者: Senescent 時間: 2025-3-29 17:38 作者: 山頂可休息 時間: 2025-3-29 20:51
Introducing Positive Discrimination in Predictive Modelsas a latent variable. By explicitly modeling the discrimination, we can reverse engineer decisions. Since all three models can be seen as ways to introduce positive discrimination, we end the chapter with a reflection on positive discrimination.作者: CURB 時間: 2025-3-30 00:59 作者: 辭職 時間: 2025-3-30 06:01
Discrimination and Privacy in the Information SocietyData Mining and Prof作者: relieve 時間: 2025-3-30 11:00
Data Dilemmas in the Information Society: Introduction and Overviewand code (i.e., constraints in the architecture of technologies). This chapter concludes with an overview of the structure of this book, containing chapters on the opportunities of data mining and profiling, possible discrimination and privacy issues, practical applications and solutions in code, la作者: 誘惑 時間: 2025-3-30 14:45
Why Unbiased Computational Processes Can Lead to Discriminative Decision Proceduresealistic scenarios in which an unbiased process can lead to discriminatory models. The effects of the implicit assumptions not being fulfilled are illustrated by examples. The chapter concludes with an outline of the main challenges and problems to be solved.
