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Personalized uncertainty quantification in artificial intelligence

delete2025-04-23
delete0
PRE
AI
T
Tapabrata Chakraborti *
C
Christopher R. S. Banerji
A
Ariane Marandon
V
Vicky Hellon
R
Robin Mitra
B
Brauninger, Leandra
M
McGough, Sarah
T
Turkay, Cagatay
F
Frangi, Alejandro F.
B
Bianconi, Ginestra
L
Li, Weizi
R
Rackham, Owen
P
Parashar, Deepak
H
Harbron, Chris
M
MacArthur, Ben
DOI：10.1038/s42256-025-01024-8delete
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Abstract

Abstract

En 中文
Artificial intelligence (AI) tools are increasingly being used to help make consequential decisions about individuals. While AI models may be accurate on average, they can simultaneously be highly uncertain about outcomes associated with specific individuals or groups of individuals. For high-stakes applications (such as healthcare and medicine, defence and security, banking and finance), AI decision-support systems must be able to make personalized assessments of uncertainty in a rigorous manner. However, the statistical frameworks needed to do so are currently incomplete. Here, we outline current approaches to personalized uncertainty quantification (PUQ) and define a set of grand challenges associated with the development and use of PUQ in a range of areas, including multimodal AI, explainable AI, generative AI and AI fairness.
Keywords:
PREDICTION
HEALTH
INFERENCE
BIAS

Journal

Nature Machine Intelligence cover
Nature Machine Intelligence
IF:
23.9
Papers:
1.3K
Citations:
1.5W

Organization

No organization information available