论文标题
为合成生物学的道德，法律和社会影响开发基于NLP的推荐系统
Developing an NLP-based Recommender System for the Ethical, Legal, and Social Implications of Synthetic Biology
论文作者
论文摘要
合成生物学是一个新兴领域，涉及有机体的工程和重新设计，例如粮食安全，健康和环境保护。因此，它对研究人员和政策制定者构成了许多道德，法律和社会影响（ELSI）。确保对社会负责的合成生物学的各种努力正在进行中。政策制定是一种监管途径，其他举措则试图将社会科学家和伦理学家纳入合成生物学项目。但是，鉴于合成生物学的纳斯奇，其跨越的异质领域的数量以及许多道德问题的开放性质，它证明是在建立广泛的具体政策的挑战，包括合成生物学团队的社会科学家和伦理学家在内，都取得了成功。 本文提出了一种不同的方法，询问是否有可能根据自然语言处理（NLP）开发出良好的推荐模型，以将合成生物学家与有关其特定研究的ELSI信息联系起来？该推荐人是作为建立合成生物学知识系统（SBK）的较大项目的一部分开发的，以加速发现和探索合成生物学设计空间。我们的方法旨在提炼合成生物学家相关的伦理和社会科学信息，并将其嵌入合成生物学研究工作流程中。
Synthetic biology is an emerging field that involves the engineering and re-design of organisms for purposes such as food security, health, and environmental protection. As such, it poses numerous ethical, legal, and social implications (ELSI) for researchers and policy makers. Various efforts to ensure socially responsible synthetic biology are underway. Policy making is one regulatory avenue, and other initiatives have sought to embed social scientists and ethicists on synthetic biology projects. However, given the nascency of synthetic biology, the number of heterogeneous domains it spans, and the open nature of many ethical questions, it has proven challenging to establish widespread concrete policies, and including social scientists and ethicists on synthetic biology teams has met with mixed success. This text proposes a different approach, asking instead is it possible to develop a well-performing recommender model based upon natural language processing (NLP) to connect synthetic biologists with information on the ELSI of their specific research? This recommender was developed as part of a larger project building a Synthetic Biology Knowledge System (SBKS) to accelerate discovery and exploration of the synthetic biology design space. Our approach aims to distill for synthetic biologists relevant ethical and social scientific information and embed it into synthetic biology research workflows.
