{"title":"分析人工智能学习系统中的故障(FAILS)","authors":"Francis Durso, M. Raunak, Rick Kuhn, R. Kacker","doi":"10.1109/STC55697.2022.00010","DOIUrl":null,"url":null,"abstract":"We learn more from analyzing failures in engineering than by studying successes. There is significant value in documenting and tracking AI failures in sufficient detail to understand their root causes, and to put processes and practices in place toward preventing similar problems in the future. Similar efforts to track and record vulnerabilities in traditional software led to the establishment of National Vulnerability Database, which has contributed towards understanding vulnerability trends, their root causes, and how to prevent them [1], [3].","PeriodicalId":170123,"journal":{"name":"2022 IEEE 29th Annual Software Technology Conference (STC)","volume":"217 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Analyzing Failures in Artificial Intelligent Learning Systems (FAILS)\",\"authors\":\"Francis Durso, M. Raunak, Rick Kuhn, R. Kacker\",\"doi\":\"10.1109/STC55697.2022.00010\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We learn more from analyzing failures in engineering than by studying successes. There is significant value in documenting and tracking AI failures in sufficient detail to understand their root causes, and to put processes and practices in place toward preventing similar problems in the future. Similar efforts to track and record vulnerabilities in traditional software led to the establishment of National Vulnerability Database, which has contributed towards understanding vulnerability trends, their root causes, and how to prevent them [1], [3].\",\"PeriodicalId\":170123,\"journal\":{\"name\":\"2022 IEEE 29th Annual Software Technology Conference (STC)\",\"volume\":\"217 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE 29th Annual Software Technology Conference (STC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/STC55697.2022.00010\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE 29th Annual Software Technology Conference (STC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/STC55697.2022.00010","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Analyzing Failures in Artificial Intelligent Learning Systems (FAILS)
We learn more from analyzing failures in engineering than by studying successes. There is significant value in documenting and tracking AI failures in sufficient detail to understand their root causes, and to put processes and practices in place toward preventing similar problems in the future. Similar efforts to track and record vulnerabilities in traditional software led to the establishment of National Vulnerability Database, which has contributed towards understanding vulnerability trends, their root causes, and how to prevent them [1], [3].
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