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Pages 1-18

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From page 1...
... Computation and automation of data acquisition allow automated systems to analyze data and extract knowledge, to create predictive models, and to use those models to guide the acquisition of additional data, closing and automating the loop in a typical research workflow. The committee uses the term automated research workflow (ARW)
From page 2...
... To explore the benefits and challenges, as well as to suggest opportunities to move forward, the National Academies of Sciences, Engineering, and Medicine's Board on Research Data and Information, in collaboration with the Board on Mathematical Sciences and Analytics and the Computer Science and Telecommunications Board, launched a study aimed at examining current efforts to develop advanced and automated workflows to accelerate research progress. A committee of nine members undertook the study, with support provided by Schmidt Futures.
From page 3...
... Fully realized ARWs are not common at present, and so the study examines how and where progress is being made in areas such as advanced computation, use of workflow management systems, laboratory automation, and the use of AI both as a workflow component as well as in directing the "outer loop" of the research process. This report constitutes an initial effort to create awareness, momentum, and synergies to help realize the potential of ARWs in scientific discovery.
From page 4...
... . ● In drug discovery, an active learning algorithm identified 57 percent of the active compounds by performing 2.5 percent of the possible experiments, compared with 20 percent identified through a traditional approach of building a model for each target (Kangas et al., 2014)
From page 5...
... . ● Researchers in the social and behavioral sciences are using new data resources and advanced analytics to better understand and address a range of pressing problems, including poverty alleviation and strengthening the delivery of public services in cities (O'Brien et al., 2017; )
From page 6...
... Broader access to research workflows and results and the enhanced ability to uncover and correct errors can contribute to greater confidence in research findings and the research enterprise and reduce redundancy among research efforts. In addition, incorporating emerging principles and guidelines for responsible artificial intelligence and machine learning advocated by various organizations, such as building in human review of algorithms, uncovering and addressing bias, and supporting transparency and reproducibility, will also help to secure the benefits of ARWs.
From page 7...
... . RECOMMENDATION 1: DESIGN PRINCIPLES Organizations that fund, perform, and disseminate research, along with scientific societies, should support and enable automated research workflows (ARWs)
From page 8...
... As these principles and other recommendations demonstrate, the focus of the committee's effort went beyond the use of AI as a component in a workflow to the use of AI methods to design experiments and to automatically control them. We offer our findings and recommendations related to design principles, infrastructure sustainability, human resources, culture and incentives, and privacy protection as contributions to the next step in the transformative application of computing to scientific discovery.
From page 9...
... will require modification of the research enterprise, including sustainable funding for the necessary hardware, software, and human resources, educating the scientific workforce, reporting and sharing research results, and structuring researcher rewards and incentives. Multidisciplinary, multirole collaboration is essential to realize the potential of ARWs.
From page 10...
... In addition to creating a more supportive environment for the development and implementation of ARWs, the actions identified here will support more effective use of AI/ML and advanced computation in research more broadly. RECOMMENDATION 2: INFRASTRUCTURE, CODE, AND DATA SUSTAINABILITY Research funders, working with other stakeholders such as societies, research institutions, and publishers, should place greater priority on approaches to ensuring the creation and sustainability of key systems, tools, platforms, and data archives for automated research workflows (ARWs)
From page 11...
... Data Commons Consortium (NIH, 2018) , and a series of efforts to advance strategic computing and related technologies across agencies under the auspices of the Networking and Information Technology Research and Development Program 3 and its predecessors.
From page 12...
... . For example, NIH is planning to invest $23 million per year over 7 years to support Artificial Intelligence for Biomedical Excellence, which will generate new biomedically relevant data sets amenable to ML (NIH, 2020)
From page 13...
... Distributed international efforts such as GO FAIR, RDA, the Research Data Framework, CODATA, FORCE11, and the Research Software Alliance are working to develop standards and approaches to facilitate research data management and sharing and FAIR data and software. RECOMMENDATION 3: HUMAN RESOURCES Research funders, higher education, research institutions, and scientific and professional societies should support the development and implementation of educational programs and career pathways aimed at building the workforce needed to develop and utilize automated research workflows (ARWs)
From page 14...
... ● Using funding support and provisions for data management plans to encourage development and curation of FAIR, responsible, and good-quality data resources. ● Developing, improving, and sharing software resources.
From page 15...
... Currently, there are strong incentives for launching rapid research responses, but weak incentives for sharing the outputs and ensuring that such responses are rigorous and reliable. A shortage of shared domain resources being used, particularly well-characterized FAIR data and related infrastructure such as repositories and active curated services, is apparent in 15 PREPUBLICATION COPY -- Uncorrected Proofs
From page 16...
... FINDING D: LEGAL AND POLICY ISSUES In addition to barriers to progress that exist within the research process itself, there are legal and policy issues that affect implementation of automated research workflows in specific domains that will require international multistakeholder efforts to address. Data collected for use in ARWs will increasingly include data generated outside of a traditional research setting, such as personal health data collected from wearables and medical visits or behavioral data collected online from social media.
From page 17...
... . RECOMMENDATION 5: PRESERVING PRIVACY Research enterprise funders, performers, publishers, and beneficiaries should work with governments, data privacy experts, and other entities to address the legal, policy, and associated technical barriers to implementing automated research workflows in specific domains.
From page 18...
... . Examples of current work on privacy-preserving approaches for social sciences include how to reduce privacy loss when dealing with small sample sizes (Chetty and Friedman, 2019)


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