On the Opacity of Deep Neural Networks
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On the Opacity of Deep Neural Networks. / Søgaard, Anders.
I: Canadian Journal of Philosophy, Bind 53, Nr. 3, 2023, s. 224–239.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - On the Opacity of Deep Neural Networks
AU - Søgaard, Anders
N1 - Publisher Copyright: © The Author(s), 2024. Published by Cambridge University Press on behalf of The Canadian Journal of Philosophy Inc.
PY - 2023
Y1 - 2023
N2 - Deep neural networks are said to be opaque, impeding the development of safe and trustworthy artificial intelligence, but where this opacity stems from is less clear. What are the sufficient properties for neural network opacity? Here, I discuss five common properties of deep neural networks and two different kinds of opacity. Which of these properties are sufficient for what type of opacity? I show how each kind of opacity stems from only one of these five properties, and then discuss to what extent the two kinds of opacity can be mitigated by explainability methods.
AB - Deep neural networks are said to be opaque, impeding the development of safe and trustworthy artificial intelligence, but where this opacity stems from is less clear. What are the sufficient properties for neural network opacity? Here, I discuss five common properties of deep neural networks and two different kinds of opacity. Which of these properties are sufficient for what type of opacity? I show how each kind of opacity stems from only one of these five properties, and then discuss to what extent the two kinds of opacity can be mitigated by explainability methods.
KW - deep neural networks
KW - explainability
KW - mitigation
KW - model size
KW - opacity
U2 - 10.1017/can.2024.1
DO - 10.1017/can.2024.1
M3 - Journal article
AN - SCOPUS:85190158253
VL - 53
SP - 224
EP - 239
JO - Canadian Journal of Philosophy
JF - Canadian Journal of Philosophy
SN - 0045-5091
IS - 3
ER -
ID: 389904615