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AI Magazine
Article . 2023 . Peer-reviewed
License: CC BY NC
Data sources: Crossref
DBLP
Article . 2023
Data sources: DBLP
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The anticipatory paradigm

Authors: Adam Amos-Binks; Dustin Dannenhauer; Leilani H. Gilpin;

The anticipatory paradigm

Abstract

AbstractAnticipatory thinking is necessary for managing risk in the safety‐ and mission‐critical domains where AI systems are being deployed. We analyze the intersection of anticipatory thinking, the optimization paradigm, and metaforesight to advance our understanding of AI systems and their adaptive capabilities when encountering low‐likelihood/high‐impact risks. We describe this intersection as the anticipatory paradigm. We detail these challenges in concrete examples and propose new types of anticipatory thinking, towards a paradigm shift in how AI systems are evaluated.

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    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
7
Top 10%
Average
Top 10%
hybrid