Assisting behavioral science and evidence-based policy making using online machine tools

• Applicant: Dr. Stefan Herzog
• Subject Area: Image and Language Processing, Computer Graphics and Visualisation, Human Computer Interaction, Ubiquitous and Wearable Computing ; General, Cognitive and Mathematical Psychology ; Applied Linguistics, Computational Linguistics ; Data Management, Data-Intensive Systems, Computer Science Methods in Business Informatics ; Security and Dependability, Operating-, Communication- and Distributed Systems ; Social Psychology, Industrial and Organisational Psychology
• Term: since 2021
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 458366841

To help meet the challenges stemming from COVID-19 and other, future global crises, the research objective of this project is to develop, deploy and empirically evaluate online machine tools (e.g., intelligent search and collaboration interfaces) that improve the scientific process and the interface between behavioral science and evidence-based policy making. We rely on state-of-the-art tools from natural language processing (NLP), machine learning (ML), and artificial intelligence (AI). This project builds on already operating systems implemented by, among others, team members Herzog, Hahn, and Lewandowsky (www.scibeh.org), and the extensive experience of team member Porciello in the creation and dissemination of evidence-based policy that uses NLP, ML and AI tools (see havos.org). The project will use the following three, interrelated real-life use cases as testbeds: (1) Facilitating rapid knowledge creation by connecting and enriching existing, ad hoc infrastructure to support efficient and rapid development, analysis, evaluation, and dissemination of emerging and extant research (e.g., consolidating a preprint’s timely, but scattered discussion on Twitter into a short, digestible format to assist the evaluation of preprints). (2) Facilitating rapid knowledge curation, integration and aggregation using natural language processing (NLP), information retrieval technologies and minimal, scalable human curation (e.g., visualizing emerging topics in the scibeh.org knowledge base using NLP tools, such as topic models). (3) Facilitating rapid policy making (e.g., by organizing machine-assisted rapid open think tanks by implementing a hybrid human/machine approach to create and synthesize key arguments and insights from scientific and policy discussions; collaboration with International Network for Government Science Advice, INGSA; www.ingsa.org; and via team member Lewandowsky who works closely with the European Commission's Joint Research Centre and will be a Knowledge Exchange Fellow in Brussels at the European Commission in 2021). This project will support all other projects in the MULTIPAN consortium (Multidisciplinary research consortium on preventing and curbing pandemic outbreaks) by providing timely and efficient means to tap into the current state of scientific knowledge (incl. preprints and their evaluation) and get feedback on research design, analysis plans etc. from the global scientific community. In particular, we will be in close collaboration with MULTIPAN project team #5 (Boosting citizens’ vaccination decision-making using effective online communication), facilitated by a close cooperation with project #5’s co-applicant Christina Leuker (Robert Koch Institute, Berlin). Finally, the tools provided by this project will boost the ability of scientists and policy makers more generally to quickly and successfully respond to other drastic global events beyond COVID-19.

• DFG Programme: Research Grants
• International Connection: United Kingdom, USA
• Cooperation Partners: Professorin Dr. Ulrike Hahn; Professor Stephan Lewandowsky, Ph.D.; Jaron Porciello