{"title":"处理网络偏见2019:主席欢迎和研讨会总结","authors":"R. Baeza-Yates, Jeanna Neefe Matthews","doi":"10.1145/3328413.3328417","DOIUrl":null,"url":null,"abstract":"A key aspect of the Web Science conference is exploring the ethical challenges of technologies, data, algorithms, platforms, and people in the Web as well as detecting, preventing and predicting anomalies in web data including algorithmic and data biases. Handling Web Bias (HWB) is a new workshop focusing on best practices for identifying and handling web bias. Awareness of the problems of algorithmic and data bias has been growing but even with careful review of the algorithms and data sets, it may not be possible to delete all unwanted bias, particularly when systems learn from historical data that encodes cultural biases. This workshop will take a rich and cross-domain approach to this complicated problem, providing a venue for researchers to move beyond awareness of the problem of algorithmic and data bias to focus on practical strategies for handling it.","PeriodicalId":102426,"journal":{"name":"Companion Publication of the 10th ACM Conference on Web Science","volume":"82 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Handling Web Bias 2019: Chairs' Welcome and Workshop Summary\",\"authors\":\"R. Baeza-Yates, Jeanna Neefe Matthews\",\"doi\":\"10.1145/3328413.3328417\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A key aspect of the Web Science conference is exploring the ethical challenges of technologies, data, algorithms, platforms, and people in the Web as well as detecting, preventing and predicting anomalies in web data including algorithmic and data biases. Handling Web Bias (HWB) is a new workshop focusing on best practices for identifying and handling web bias. Awareness of the problems of algorithmic and data bias has been growing but even with careful review of the algorithms and data sets, it may not be possible to delete all unwanted bias, particularly when systems learn from historical data that encodes cultural biases. This workshop will take a rich and cross-domain approach to this complicated problem, providing a venue for researchers to move beyond awareness of the problem of algorithmic and data bias to focus on practical strategies for handling it.\",\"PeriodicalId\":102426,\"journal\":{\"name\":\"Companion Publication of the 10th ACM Conference on Web Science\",\"volume\":\"82 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-06-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Companion Publication of the 10th ACM Conference on Web Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3328413.3328417\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Companion Publication of the 10th ACM Conference on Web Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3328413.3328417","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Handling Web Bias 2019: Chairs' Welcome and Workshop Summary
A key aspect of the Web Science conference is exploring the ethical challenges of technologies, data, algorithms, platforms, and people in the Web as well as detecting, preventing and predicting anomalies in web data including algorithmic and data biases. Handling Web Bias (HWB) is a new workshop focusing on best practices for identifying and handling web bias. Awareness of the problems of algorithmic and data bias has been growing but even with careful review of the algorithms and data sets, it may not be possible to delete all unwanted bias, particularly when systems learn from historical data that encodes cultural biases. This workshop will take a rich and cross-domain approach to this complicated problem, providing a venue for researchers to move beyond awareness of the problem of algorithmic and data bias to focus on practical strategies for handling it.