{"title":"人工智能文档：责任之路","authors":"Florian Königstorfer, Stefan Thalmann","doi":"10.1016/j.jrt.2022.100043","DOIUrl":null,"url":null,"abstract":"<div><p>Artificial Intelligence (AI) promises huge potential for businesses but due to its black-box character has also substantial drawbacks. This is a particular challenge in regulated use cases, where software needs to be certified or validated before deployment. Traditional software documentation is not sufficient to provide the required evidence to auditors and AI-specific guidelines are not available yet. Thus, AI faces significant adoption barriers in regulated use cases, since accountability of AI cannot be ensured to a sufficient extent. This interview study aims to determine the current state of documenting AI in regulated use cases. We found that the risk level of AI use cases has an impact on the AI adoption and the scope of AI documentation. Further, we discuss how AI is currently documented and which challenges practitioners face when documenting AI.</p></div>","PeriodicalId":73937,"journal":{"name":"Journal of responsible technology","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666659622000208/pdfft?md5=bb63316f230d774001f337edc4c0fa62&pid=1-s2.0-S2666659622000208-main.pdf","citationCount":"9","resultStr":"{\"title\":\"AI Documentation: A path to accountability\",\"authors\":\"Florian Königstorfer, Stefan Thalmann\",\"doi\":\"10.1016/j.jrt.2022.100043\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Artificial Intelligence (AI) promises huge potential for businesses but due to its black-box character has also substantial drawbacks. This is a particular challenge in regulated use cases, where software needs to be certified or validated before deployment. Traditional software documentation is not sufficient to provide the required evidence to auditors and AI-specific guidelines are not available yet. Thus, AI faces significant adoption barriers in regulated use cases, since accountability of AI cannot be ensured to a sufficient extent. This interview study aims to determine the current state of documenting AI in regulated use cases. We found that the risk level of AI use cases has an impact on the AI adoption and the scope of AI documentation. Further, we discuss how AI is currently documented and which challenges practitioners face when documenting AI.</p></div>\",\"PeriodicalId\":73937,\"journal\":{\"name\":\"Journal of responsible technology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2666659622000208/pdfft?md5=bb63316f230d774001f337edc4c0fa62&pid=1-s2.0-S2666659622000208-main.pdf\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of responsible technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2666659622000208\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of responsible technology","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666659622000208","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Artificial Intelligence (AI) promises huge potential for businesses but due to its black-box character has also substantial drawbacks. This is a particular challenge in regulated use cases, where software needs to be certified or validated before deployment. Traditional software documentation is not sufficient to provide the required evidence to auditors and AI-specific guidelines are not available yet. Thus, AI faces significant adoption barriers in regulated use cases, since accountability of AI cannot be ensured to a sufficient extent. This interview study aims to determine the current state of documenting AI in regulated use cases. We found that the risk level of AI use cases has an impact on the AI adoption and the scope of AI documentation. Further, we discuss how AI is currently documented and which challenges practitioners face when documenting AI.
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