{"title":"灾难性损失的责任与保险:核电先例及对人工智能的启示","authors":"Cristian Trout","doi":"arxiv-2409.06673","DOIUrl":null,"url":null,"abstract":"As AI systems become more autonomous and capable, experts warn of them\npotentially causing catastrophic losses. Drawing on the successful precedent\nset by the nuclear power industry, this paper argues that developers of\nfrontier AI models should be assigned limited, strict, and exclusive third\nparty liability for harms resulting from Critical AI Occurrences (CAIOs) -\nevents that cause or easily could have caused catastrophic losses. Mandatory\ninsurance for CAIO liability is recommended to overcome developers'\njudgment-proofness, mitigate winner's curse dynamics, and leverage insurers'\nquasi-regulatory abilities. Based on theoretical arguments and observations\nfrom the analogous nuclear power context, insurers are expected to engage in a\nmix of causal risk-modeling, monitoring, lobbying for stricter regulation, and\nproviding loss prevention guidance in the context of insuring against\nheavy-tail risks from AI. While not a substitute for regulation, clear\nliability assignment and mandatory insurance can help efficiently allocate\nresources to risk-modeling and safe design, facilitating future regulatory\nefforts.","PeriodicalId":501112,"journal":{"name":"arXiv - CS - Computers and Society","volume":"73 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Liability and Insurance for Catastrophic Losses: the Nuclear Power Precedent and Lessons for AI\",\"authors\":\"Cristian Trout\",\"doi\":\"arxiv-2409.06673\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"As AI systems become more autonomous and capable, experts warn of them\\npotentially causing catastrophic losses. Drawing on the successful precedent\\nset by the nuclear power industry, this paper argues that developers of\\nfrontier AI models should be assigned limited, strict, and exclusive third\\nparty liability for harms resulting from Critical AI Occurrences (CAIOs) -\\nevents that cause or easily could have caused catastrophic losses. Mandatory\\ninsurance for CAIO liability is recommended to overcome developers'\\njudgment-proofness, mitigate winner's curse dynamics, and leverage insurers'\\nquasi-regulatory abilities. Based on theoretical arguments and observations\\nfrom the analogous nuclear power context, insurers are expected to engage in a\\nmix of causal risk-modeling, monitoring, lobbying for stricter regulation, and\\nproviding loss prevention guidance in the context of insuring against\\nheavy-tail risks from AI. While not a substitute for regulation, clear\\nliability assignment and mandatory insurance can help efficiently allocate\\nresources to risk-modeling and safe design, facilitating future regulatory\\nefforts.\",\"PeriodicalId\":501112,\"journal\":{\"name\":\"arXiv - CS - Computers and Society\",\"volume\":\"73 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-09-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - CS - Computers and Society\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2409.06673\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Computers and Society","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.06673","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Liability and Insurance for Catastrophic Losses: the Nuclear Power Precedent and Lessons for AI
As AI systems become more autonomous and capable, experts warn of them
potentially causing catastrophic losses. Drawing on the successful precedent
set by the nuclear power industry, this paper argues that developers of
frontier AI models should be assigned limited, strict, and exclusive third
party liability for harms resulting from Critical AI Occurrences (CAIOs) -
events that cause or easily could have caused catastrophic losses. Mandatory
insurance for CAIO liability is recommended to overcome developers'
judgment-proofness, mitigate winner's curse dynamics, and leverage insurers'
quasi-regulatory abilities. Based on theoretical arguments and observations
from the analogous nuclear power context, insurers are expected to engage in a
mix of causal risk-modeling, monitoring, lobbying for stricter regulation, and
providing loss prevention guidance in the context of insuring against
heavy-tail risks from AI. While not a substitute for regulation, clear
liability assignment and mandatory insurance can help efficiently allocate
resources to risk-modeling and safe design, facilitating future regulatory
efforts.