{"title":"大规模人工智能的公平、问责和透明度:来自国家项目的经验教训","authors":"M. Ahmad, A. Teredesai, C. Eckert","doi":"10.1145/3351095.3375690","DOIUrl":null,"url":null,"abstract":"The panel aims to elucidate how different national govenmental programs are implementing accountability of machine learning systems in healthcare and how accountability is operationlized in different cultural settings in legislation, policy and deployment. We have representatives from three different govenments, UAE, Singapore and Maldives who will discuss what accountability of AI and machine learning means in their contexts and use cases. We hope to have a fruitful conversation around FAT ML as it is operationalized ccross cultures, national boundries and legislative constraints.","PeriodicalId":377829,"journal":{"name":"Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency","volume":" 10","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"17","resultStr":"{\"title\":\"Fairness, accountability, transparency in AI at scale: lessons from national programs\",\"authors\":\"M. Ahmad, A. Teredesai, C. Eckert\",\"doi\":\"10.1145/3351095.3375690\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The panel aims to elucidate how different national govenmental programs are implementing accountability of machine learning systems in healthcare and how accountability is operationlized in different cultural settings in legislation, policy and deployment. We have representatives from three different govenments, UAE, Singapore and Maldives who will discuss what accountability of AI and machine learning means in their contexts and use cases. We hope to have a fruitful conversation around FAT ML as it is operationalized ccross cultures, national boundries and legislative constraints.\",\"PeriodicalId\":377829,\"journal\":{\"name\":\"Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency\",\"volume\":\" 10\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-01-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"17\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3351095.3375690\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3351095.3375690","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Fairness, accountability, transparency in AI at scale: lessons from national programs
The panel aims to elucidate how different national govenmental programs are implementing accountability of machine learning systems in healthcare and how accountability is operationlized in different cultural settings in legislation, policy and deployment. We have representatives from three different govenments, UAE, Singapore and Maldives who will discuss what accountability of AI and machine learning means in their contexts and use cases. We hope to have a fruitful conversation around FAT ML as it is operationalized ccross cultures, national boundries and legislative constraints.