使用未经验证的开放数据的风险：印度使用机器学习技术预测妊娠结局的案例研究
Risks of Using Non-verified Open Data: A case study on using Machine Learning techniques for predicting Pregnancy Outcomes in India
摘要：
人工智能（AI）在过去的几年里有了很大的发展。虽然人工智能在零售和营销等领域的应用越来越普遍，但人工智能在解决发展中国家相关问题方面的应用仍然是一个新兴话题。特别是，在资源匮乏的环境中，人工智能应用仍然相对较新。在这样的环境中使用AI的范围很大。例如，研究人员已经开始探索人工智能应用，以减少贫困并提供广泛的关键公共服务。然而，尽管有许多有前途的用例，但在这些项目中必须克服许多与数据集相关的挑战。这些挑战通常表现为数据缺失、数据收集不正确和变量标签不正确等。因此，我们经常使用的数据并不能代表我们试图解决的问题。在本案例研究中，我们探讨了使用来自印度的开放数据集来预测重要健康结果的挑战。我们强调，在没有正确理解报告指标的情况下使用人工智能可能会导致错误的结论。
英文摘要：
Artificial intelligence (AI) has evolved considerably in the last few years. While applications of AI is now becoming more common in fields like retail and marketing, application of AI in solving problems related to developing countries is still an emerging topic. Specially, AI applications in resource-poor settings remains relatively nascent. There is a huge scope of AI being used in such settings. For example, researchers have started exploring AI applications to reduce poverty and deliver a broad range of critical public services. However, despite many promising use cases, there are many dataset related challenges that one has to overcome in such projects. These challenges often take the form of missing data, incorrectly collected data and improperly labeled variables, among other factors. As a result, we can often end up using data that is not representative of the problem we are trying to solve. In this case study, we explore the challenges of using such an open dataset from India, to predict an important health outcome. We highlight how the use of AI without proper understanding of reporting metrics can lead to erroneous conclusions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
