{"title":"法律如何解决算法偏见在医疗保健领域的影响?","authors":"Zoya","doi":"10.61315/lselr.651","DOIUrl":null,"url":null,"abstract":"This paper examines how UK ‘hard laws’ can adapt to regulate algorithmic bias in the healthcare context. I explore the causes of algorithmic bias which sets the foundation for how the law will address this issue. I critically analyse elements of the tort of negligence, the Equality Act 2010, and the Medical Devices Regulations 2002 which reveal the inadequacies of these frameworks in their application to algorithmic bias. Following this, I make recommendations on how the law can adjust to ensure that algorithms do not perpetuate existing biases and discriminate against patients. This paper acknowledges that addressing algorithmic bias will involve a mixture of hard and soft law measures, but in the final section, it will be argued that urgent systemic change (data sharing and workplace diversity) is also needed to enable the law to address the effects of algorithmic bias in the healthcare context.","PeriodicalId":514338,"journal":{"name":"LSE Law Review","volume":"61 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"How Can the Law Address the Effects of Algorithmic Bias in the Healthcare Context?\",\"authors\":\"Zoya\",\"doi\":\"10.61315/lselr.651\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper examines how UK ‘hard laws’ can adapt to regulate algorithmic bias in the healthcare context. I explore the causes of algorithmic bias which sets the foundation for how the law will address this issue. I critically analyse elements of the tort of negligence, the Equality Act 2010, and the Medical Devices Regulations 2002 which reveal the inadequacies of these frameworks in their application to algorithmic bias. Following this, I make recommendations on how the law can adjust to ensure that algorithms do not perpetuate existing biases and discriminate against patients. This paper acknowledges that addressing algorithmic bias will involve a mixture of hard and soft law measures, but in the final section, it will be argued that urgent systemic change (data sharing and workplace diversity) is also needed to enable the law to address the effects of algorithmic bias in the healthcare context.\",\"PeriodicalId\":514338,\"journal\":{\"name\":\"LSE Law Review\",\"volume\":\"61 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-03-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"LSE Law Review\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.61315/lselr.651\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"LSE Law Review","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.61315/lselr.651","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
How Can the Law Address the Effects of Algorithmic Bias in the Healthcare Context?
This paper examines how UK ‘hard laws’ can adapt to regulate algorithmic bias in the healthcare context. I explore the causes of algorithmic bias which sets the foundation for how the law will address this issue. I critically analyse elements of the tort of negligence, the Equality Act 2010, and the Medical Devices Regulations 2002 which reveal the inadequacies of these frameworks in their application to algorithmic bias. Following this, I make recommendations on how the law can adjust to ensure that algorithms do not perpetuate existing biases and discriminate against patients. This paper acknowledges that addressing algorithmic bias will involve a mixture of hard and soft law measures, but in the final section, it will be argued that urgent systemic change (data sharing and workplace diversity) is also needed to enable the law to address the effects of algorithmic bias in the healthcare context.