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Funding for basic/applied psychological research

APA responds to federal plans to democratize artificial intelligence data and resources for US researchers

The National Science Foundation and the White House Office of Science and Technology Policy are developing an implementation roadmap for a shared U.S. artificial intelligence research infrastructure. They sought feedback from a broad array of stakeholders, including psychological scientists.

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American Psychological Association. (2021, October 18). APA responds to federal plans to democratize artificial intelligence data and resources for US researchers. http://www.apaservices.org/advocacy/news/democratize-artificial-intelligence-data

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APA responded to the The National Science Foundation (NSF) and the White House Office of Science and Technology Policy (OSTP) Request for Information (RFI) on an Implementation Plan for a National Artificial Intelligence Research Resource (NAIRR). NSF and OSTP established a task force that solicited comments on ways to provide artificial intelligence (AI) researchers and students from all scientific disciplines with greater access to computational resources, high-quality datasets, and educational tools.

APA provided feedback on NAIRR’s priorities (PDF, 285KB), the ethical and responsible development of AI, and the potential limitations of these efforts. As a broad field, psychology can contribute to the development of AI, enhance its positive impacts for individuals and society, and reduce unintended negative consequences. APA’s response highlighted the importance of psychological science to the success of the task force’s roadmap. It recommended that computer scientists and engineers must collaborate with the social, behavioral, and psychological sciences to ensure the development of safer AI systems that have the potential to spark positive social change. APA is committed to increasing access to scientific and educational tools for underrepresented research institutions and investigators, and to elevating the principles of racial and gender equity, fairness, bias, explainability, transparency, interpretability, and accountability in the research and development of AI.

For more information, contact Joseph Keller.