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ABSTRACT Where AI systems are increasingly and rapidly impacting engineering, science, and our daily lives, progress in AI safety for physical infrastructures is lagging. Most of the research and educational programs on AI safety do not consider that, in today's connected world, safety and security in physical infrastructures are increasingly entangled. This technical note sheds light, for the first time, on how computer science and engineering communities, for example, mechanical and civil, can collaborate on addressing AI safety issues in the physical infrastructures and the mutual benefits of this collaboration. We offer examples of how probabilistic views of engineers on safety can contribute to quantifying critical parameters such as “threshold” and “safety buffer” in the AI safety models, developed by the world‐leading computer scientists. We also offer examples of how novel AI and machine learning tools, for example,do‐operator, a mathematical operator for intervention (vs. conditioning);do‐calculus, machinery of causal calculus; and physics‐informed neural networks with a small number of samples can help fatigue and fracture research. We envision AI safety as a process, not an object, and contribute to realizing this vision by initiating a collaborative and interdisciplinary approach in establishing this process.
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Think about the stakeholders first! Toward an algorithmic transparency playbook for regulatory compliance
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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- PAR ID:
- 10432161
- Date Published:
- Journal Name:
- Data & Policy
- Volume:
- 5
- ISSN:
- 2632-3249
- 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/10.1017/dap.2023.8
