Developing an NLP-based Recommender System for the Ethical, Legal, and Social Implications of Synthetic Biology

TL;DR

This paper proposes an NLP-based recommender system to connect synthetic biologists with relevant ethical, legal, and social implications information, aiming to integrate ELSI considerations into synthetic biology research workflows.

Contribution

It introduces a novel NLP-driven recommender model designed to facilitate access to ELSI information for synthetic biologists, enhancing responsible research practices.

Findings

Developed a high-performing NLP recommender system.

Successfully integrated ELSI information into synthetic biology workflows.

Improved access to ethical and social considerations for researchers.

Abstract

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.