{"title":"人工智能与 ML 圆桌讨论的主题","authors":"Joanna F. DeFranco","doi":"10.1109/MRL.2024.3382945","DOIUrl":null,"url":null,"abstract":"Artificial intelligence (AI) and machine learning (ML) are technologies that are increasingly being integrated into many critical domains such as healthcare, finance, and vehicles. These are all critical systems given their consequences of failure. Therefore, aspects of these systems such as the data gathered to train them need to be representative of the real world. For systems to be trusted by the public in the sense that they will work as intended and will not cause harm, systems should have the characteristics of trustworthy AI as outlined in NIST AI 100-1: valid and reliable, safe, secure, and resilient, accountable and transparent, explainable and interpretable, privacy-enhanced, and fair with harmful bias managed. NIST “Artificial Intelligence Risk Management Framework (AI RMF 1.0”), January 2023, https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf","PeriodicalId":517825,"journal":{"name":"IEEE Reliability Magazine","volume":"6 9","pages":"42-45"},"PeriodicalIF":0.0000,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Themes From an AI and ML Roundtable Discussion\",\"authors\":\"Joanna F. DeFranco\",\"doi\":\"10.1109/MRL.2024.3382945\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Artificial intelligence (AI) and machine learning (ML) are technologies that are increasingly being integrated into many critical domains such as healthcare, finance, and vehicles. These are all critical systems given their consequences of failure. Therefore, aspects of these systems such as the data gathered to train them need to be representative of the real world. For systems to be trusted by the public in the sense that they will work as intended and will not cause harm, systems should have the characteristics of trustworthy AI as outlined in NIST AI 100-1: valid and reliable, safe, secure, and resilient, accountable and transparent, explainable and interpretable, privacy-enhanced, and fair with harmful bias managed. NIST “Artificial Intelligence Risk Management Framework (AI RMF 1.0”), January 2023, https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf\",\"PeriodicalId\":517825,\"journal\":{\"name\":\"IEEE Reliability Magazine\",\"volume\":\"6 9\",\"pages\":\"42-45\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Reliability Magazine\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MRL.2024.3382945\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Reliability Magazine","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MRL.2024.3382945","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Artificial intelligence (AI) and machine learning (ML) are technologies that are increasingly being integrated into many critical domains such as healthcare, finance, and vehicles. These are all critical systems given their consequences of failure. Therefore, aspects of these systems such as the data gathered to train them need to be representative of the real world. For systems to be trusted by the public in the sense that they will work as intended and will not cause harm, systems should have the characteristics of trustworthy AI as outlined in NIST AI 100-1: valid and reliable, safe, secure, and resilient, accountable and transparent, explainable and interpretable, privacy-enhanced, and fair with harmful bias managed. NIST “Artificial Intelligence Risk Management Framework (AI RMF 1.0”), January 2023, https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
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