{"title":"通过监管和设计使用问责制来提高人工智能可解释性的框架","authors":"Arsh Shah","doi":"10.2139/ssrn.3617349","DOIUrl":null,"url":null,"abstract":"This paper discusses frameworks for improving AI explainability regulations and frameworks, drawing on ethical AI design, self-regulation, blockchain solutions for auditing, and FAT (fairness, accountability and transparency) Forensics packages forked from Github. The work takes a look at approaches to AI in the GDPR, Chinese AI Standards, United States law, and domestic Australian Law (at both the State and Federal Levels).","PeriodicalId":241211,"journal":{"name":"CompSciRN: Artificial Intelligence (Topic)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Frameworks For Improving AI Explainability Using Accountability Through Regulation and Design\",\"authors\":\"Arsh Shah\",\"doi\":\"10.2139/ssrn.3617349\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper discusses frameworks for improving AI explainability regulations and frameworks, drawing on ethical AI design, self-regulation, blockchain solutions for auditing, and FAT (fairness, accountability and transparency) Forensics packages forked from Github. The work takes a look at approaches to AI in the GDPR, Chinese AI Standards, United States law, and domestic Australian Law (at both the State and Federal Levels).\",\"PeriodicalId\":241211,\"journal\":{\"name\":\"CompSciRN: Artificial Intelligence (Topic)\",\"volume\":\"3 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-05-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"CompSciRN: Artificial Intelligence (Topic)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2139/ssrn.3617349\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"CompSciRN: Artificial Intelligence (Topic)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.3617349","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Frameworks For Improving AI Explainability Using Accountability Through Regulation and Design
This paper discusses frameworks for improving AI explainability regulations and frameworks, drawing on ethical AI design, self-regulation, blockchain solutions for auditing, and FAT (fairness, accountability and transparency) Forensics packages forked from Github. The work takes a look at approaches to AI in the GDPR, Chinese AI Standards, United States law, and domestic Australian Law (at both the State and Federal Levels).