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6 Conclusion
Pages 136-143

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From page 136...
... The committee's exploration of ARWs illustrates that the research enterprise stands at an important inflection point. The scientific revolution of the 17th century ushered in an unprecedented era of human progress, leading directly to discoveries and innovations that have transformed tasks requiring the application of muscle or simple technologies into services performed by ever more effective machines.
From page 137...
... These themes include the need to break down academic silos, provide incentives for greater collaboration among researchers, ensure greater interoperability across technologies, foster sharing of a broader range of research outputs, and address issues such as striking an appropriate balance between access to and protection of data. The committee's findings and recommendations point to promising areas of focus for the research enterprise in facilitating the effective implementation of ARWs.
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)
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)
From page 140...
... 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.
From page 141...
... ● Funders and research institutions enabling reuse, reproducibility, and long-term sharing of FAIR data and software resources through support of repositories that make archival and updated versions of these resources available within and across disciplines, and providing approaches to sustain those repositories. ● Publishers updating their data-sharing requirements by directly associating articles to data in FAIR repositories.
From page 142...
... ● Developing the human resources needed to build, maintain, and operate ARW hardware and software, including hardware and software engineers who build, maintain, and operate automated laboratories and the software needed to learn from data and to design experiments. ● Fostering collaborative research that aims at developing and using ARWs and that facilitates sharing workflows, code, data, and data products in ways that respect and protect privacy considerations.
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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