  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.
