Automated Research Workflows for Accelerated Discovery Closing the Knowledge Discovery Loop (2022) / Chapter Skim
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Pages 136-143
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From page 136... ...
6 Conclusion The tools and techniques being developed under the large umbrella of automated research workflows (ARWs) promise to collapse the centuries-old serial method of research investigation into processes where thousands or even millions of simulations or experiments are iterated rapidly in closed loops, with the analysis of data and even the design of experiments or controlled observations being assisted by machine learning (ML)
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In addition, additional specialized expertise in areas such as software engineering, algorithm development, and data science will be required in a number of fields. Further, several lines of thought that emerged from the March 2020 workshop are germane not just to the task at hand, but more broadly across the scientific enterprise.
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From page 138... ...
Realizing the potential of ARWs could accelerate the pace of scientific discovery by orders of magnitude and thereby expand the research enterprise's contribution to society. Finding B: Additional Benefits In addition to increasing the speed and efficiency of research, the effective development and implementation of the technical and human infrastructure for automated research workflows (ARWs)
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From page 139... ...
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. Recommendation 1: Design Principles Organizations that fund, perform, and disseminate research, along with scientific societies, should support and enable automated research workflows (ARWs)
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Finding C: Research Enterprise Realizing the potential of automated research workflows (ARWs) 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.
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. Priorities include: ● Funding support for efforts by research institutions and societies to link disciplines so they can share and benefit from the expertise in statistics, machine learning, or data science, and engineering and computer science that is required to build and maintain sustainable infrastructure for ARWs.
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From page 142... ...
Recommendation 4: Culture and Incentives Research funders, research institutions, and disciplines should work to create an automated research workflow (ARW) -friendly culture by making changes in incentive and reward structures aimed at encouraging behaviors that are central to realizing the potential of ARWs.
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From page 143... ...
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 use-inspired applications in specific domains and explore solutions to make the outputs available through privacy-preserving algorithms, federated learning approaches to using data, and other methods. 143 PREPUBLICATION COPY -- Uncorrected Proofs
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