不同专家对使用机器学习预测撒哈拉以南非洲地区 HIV/AIDS 风险的伦理问题的看法：一项改良德尔菲研究。
Diverse experts' perspectives on ethical issues of using machine learning to predict HIV/AIDS risk in sub-Saharan Africa: a modified Delphi study.
机构信息
Center for Biomedical Ethics, Stanford University School of Medicine, Stanford, California, USA
Department of Primary Care and Population Health, Stanford University School of Medicine, Stanford, California, USA.
出版信息
BMJ Open. 2021 Jul 28;11(7):e052287. doi: 10.1136/bmjopen-2021-052287.
OBJECTIVE
To better understand diverse experts' views about the ethical implications of ongoing research funded by the National Institutes of Health that uses machine learning to predict HIV/AIDS risk in sub-Saharan Africa (SSA) based on publicly available Demographic and Health Surveys data.
DESIGN
Three rounds of semi-structured surveys in an online expert panel using a modified Delphi approach.
PARTICIPANTS
Experts in informatics, African public health and HIV/AIDS and bioethics were invited to participate.
MEASURES
Perceived importance of or agreement about relevance of ethical issues on 5-point unipolar Likert scales. Qualitative data analysis identified emergent themes related to ethical issues and development of an ethical framework and recommendations for open-ended questions.
RESULTS
Of the 35 invited experts, 22 participated in the online expert panel (63%). Emergent themes were the inclusion of African researchers in all aspects of study design, analysis and dissemination to identify and address local contextual issues, as well as engagement of communities. Experts focused on engagement with health and science professionals to address risks, benefits and communication of findings. Respondents prioritised the mitigation of stigma to research participants but recognised trade-offs between privacy and the need to disseminate findings to realise public health benefits. Strategies for responsible communication of results were suggested, including careful word choice in presentation of results and limited dissemination to need-to-know stakeholders such as public health planners.
CONCLUSION
Experts identified ethical issues specific to the African context and to research on sensitive, publicly available data and strategies for addressing these issues. These findings can be used to inform an ethical implementation framework with research stage-specific recommendations on how to use publicly available data for machine learning-based predictive analytics to predict HIV/AIDS risk in SSA.
目的
更好地理解不同专家对美国国立卫生研究院（NIH）资助的正在进行的研究的伦理含义的看法，该研究利用机器学习根据公开的人口与健康调查数据预测撒哈拉以南非洲（SSA）的艾滋病毒/艾滋病风险。
设计
采用改良德尔菲法在在线专家小组中进行三轮半结构化调查。
参与者
邀请了信息学、非洲公共卫生和艾滋病毒/艾滋病以及生物伦理学方面的专家参加。
措施
使用 5 点单极李克特量表评估对伦理问题重要性的感知或相关性的认同。对定性数据分析确定了与伦理问题相关的新兴主题，并制定了伦理框架和对开放式问题的建议。
结果
在 35 名受邀专家中，有 22 名参加了在线专家小组（63%）。新兴主题包括让非洲研究人员参与研究设计、分析和传播的各个方面，以确定和解决当地的背景问题，以及社区的参与。专家们重点关注与健康和科学专业人员的接触，以解决风险、利益和研究结果的交流问题。受访者优先考虑减轻研究参与者的耻辱感，但认识到隐私和传播研究结果以实现公共卫生利益之间的权衡。还提出了负责任地交流研究结果的策略，包括在结果陈述中谨慎选择措辞以及将传播限制在需要了解的利益相关者，如公共卫生规划者。
结论
专家确定了与非洲背景以及对敏感的、公开可用的数据进行研究相关的具体伦理问题，并提出了应对这些问题的策略。这些发现可用于为一个伦理实施框架提供信息，该框架具有针对研究特定阶段的建议，说明如何使用公开可用的数据进行基于机器学习的预测性分析，以预测 SSA 的艾滋病毒/艾滋病风险。
Zotero 插件