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Abstract Increasingly, laws are being proposed and passed by governments around the world to regulate artificial intelligence (AI) systems implemented into the public and private sectors. Many of these regulations address the transparency of AI systems, and related citizen-aware issues like allowing individuals to have the right to an explanation about how an AI system makes a decision that impacts them. Yet, almost all AI governance documents to date have a significant drawback: they have focused on what to do (or what not to do) with respect to making AI systems transparent, but have left the brunt of the work to technologists to figure out how to build transparent systems. We fill this gap by proposing a stakeholder-first approach that assists technologists in designing transparent, regulatory-compliant systems. We also describe a real-world case study that illustrates how this approach can be used in practice.
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How to track the economic impact of public investments in AI
Government spending on artificial intelligence (AI) has surged across the world. Quantifying the return on research investments is notoriously difficult, especially in newly emerging economic sectors. Here, we propose a novel way to describe and analyze where AI ideas are being used and how they spread—by tracing the people and academic communities involved in AI research as they transition from government-funded research labs to private sector companies, carrying cutting-edge “AI know-how” with them. Linking existing university administrative data with state employment records allows several quantifiable inferences about the value of AI research to be drawn from these academia-to-industry migrations. Here we describe a pilot implementation of this system, which is being developed in the State of Ohio. It offers a template for governments and policy makers all over the world. Importantly, the metrics discussed below offer a way to measure the economic impact of scientific research in general, with implications for critical and emerging technologies that go far beyond AI.
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- PAR ID:
- 10542936
- Publisher / Repository:
- Nature
- Date Published:
- Journal Name:
- Nature
- Volume:
- 630
- Issue:
- 8016
- ISSN:
- 0028-0836
- Page Range / eLocation ID:
- 302 to 304
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1038/d41586-024-01721-1
