José Augusto Bagatini, José Augusto Chaves Guimarães
{"title":"Algorithmic Discriminations and Their Ethical Impacts on Knowledge Organization: A Thematic Domain-Analysis","authors":"José Augusto Bagatini, José Augusto Chaves Guimarães","doi":"10.5771/0943-7444-2023-5-336","DOIUrl":null,"url":null,"abstract":"Personal data play a fundamental role in contemporary socioeconomic dynamics, with one of its primary aspects being the potential to facilitate discriminatory situations. This situation impacts the knowledge organization field especially because it considers personal data as elements (facets) to categorize persons under an economic and sometimes discriminatory perspective. The research corpus was collected at Scopus and Web of Science until the end of 2021, under the terms “data discrimination”, “algorithmic bias”, “algorithmic discrimination” and “fair algorithms”. The obtained results allowed to infer that the analyzed knowledge domain predominantly incorporates personal data, whether in its behavioral dimension or in the scope of the so-called sensitive data. These data are susceptible to the action of algorithms of different orders, such as relevance, filtering, predictive, social ranking, content recommendation and random classification. Such algorithms can have discriminatory biases in their programming related to gender, sexual orientation, race, nationality, religion, age, social class, socioeconomic profile, physical appearance, and political positioning.","PeriodicalId":46091,"journal":{"name":"Knowledge Organization","volume":"9 10 1","pages":"0"},"PeriodicalIF":0.6000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Knowledge Organization","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5771/0943-7444-2023-5-336","RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"INFORMATION SCIENCE & LIBRARY SCIENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Personal data play a fundamental role in contemporary socioeconomic dynamics, with one of its primary aspects being the potential to facilitate discriminatory situations. This situation impacts the knowledge organization field especially because it considers personal data as elements (facets) to categorize persons under an economic and sometimes discriminatory perspective. The research corpus was collected at Scopus and Web of Science until the end of 2021, under the terms “data discrimination”, “algorithmic bias”, “algorithmic discrimination” and “fair algorithms”. The obtained results allowed to infer that the analyzed knowledge domain predominantly incorporates personal data, whether in its behavioral dimension or in the scope of the so-called sensitive data. These data are susceptible to the action of algorithms of different orders, such as relevance, filtering, predictive, social ranking, content recommendation and random classification. Such algorithms can have discriminatory biases in their programming related to gender, sexual orientation, race, nationality, religion, age, social class, socioeconomic profile, physical appearance, and political positioning.
个人数据在当代社会经济动态中发挥着重要作用,其主要方面之一是可能助长歧视情况。这种情况影响了知识组织领域,特别是因为它将个人数据视为从经济角度(有时是歧视性角度)对人进行分类的要素(方面)。研究语料库在Scopus和Web of Science上收集到2021年底,分类为“数据歧视”、“算法偏见”、“算法歧视”和“公平算法”。获得的结果可以推断,所分析的知识领域主要包含个人数据,无论是在其行为维度还是在所谓的敏感数据范围内。这些数据容易受到不同顺序算法的作用,如相关性、过滤、预测性、社会排名、内容推荐和随机分类。这种算法在编程中可能存在与性别、性取向、种族、国籍、宗教、年龄、社会阶层、社会经济状况、外貌和政治定位相关的歧视性偏见。