{"title":"健康人工智能治理的两条道路:家长制还是民主制。","authors":"Cori Crider","doi":"10.1016/j.fhj.2024.100180","DOIUrl":null,"url":null,"abstract":"<p><p>This article assesses the cyclical failures of NHS data modernisation programmes, and considers that they fail because they proceed from a faulty - excessively paternalistic - governance model. Bias in algorithmic delivery of healthcare, a demonstrated problem with many existing health applications, is another serious risk. To regain trust and move towards better use of data in the NHS, we should democratise the development of these systems, and de-risk operational systems from issues such as automation bias. As a comparison, the essay explores two approaches to trust and bias problems in other contexts: Taiwan's digital democracy, and American Airlines' struggles to overcome automation bias in their pilots.</p>","PeriodicalId":73125,"journal":{"name":"Future healthcare journal","volume":"11 3","pages":"100180"},"PeriodicalIF":0.0000,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11452825/pdf/","citationCount":"0","resultStr":"{\"title\":\"Two paths for health AI governance: paternalism or democracy.\",\"authors\":\"Cori Crider\",\"doi\":\"10.1016/j.fhj.2024.100180\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>This article assesses the cyclical failures of NHS data modernisation programmes, and considers that they fail because they proceed from a faulty - excessively paternalistic - governance model. Bias in algorithmic delivery of healthcare, a demonstrated problem with many existing health applications, is another serious risk. To regain trust and move towards better use of data in the NHS, we should democratise the development of these systems, and de-risk operational systems from issues such as automation bias. As a comparison, the essay explores two approaches to trust and bias problems in other contexts: Taiwan's digital democracy, and American Airlines' struggles to overcome automation bias in their pilots.</p>\",\"PeriodicalId\":73125,\"journal\":{\"name\":\"Future healthcare journal\",\"volume\":\"11 3\",\"pages\":\"100180\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-09-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11452825/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Future healthcare journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1016/j.fhj.2024.100180\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/9/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Future healthcare journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/j.fhj.2024.100180","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/9/1 0:00:00","PubModel":"eCollection","JCR":"","JCRName":"","Score":null,"Total":0}
Two paths for health AI governance: paternalism or democracy.
This article assesses the cyclical failures of NHS data modernisation programmes, and considers that they fail because they proceed from a faulty - excessively paternalistic - governance model. Bias in algorithmic delivery of healthcare, a demonstrated problem with many existing health applications, is another serious risk. To regain trust and move towards better use of data in the NHS, we should democratise the development of these systems, and de-risk operational systems from issues such as automation bias. As a comparison, the essay explores two approaches to trust and bias problems in other contexts: Taiwan's digital democracy, and American Airlines' struggles to overcome automation bias in their pilots.