open access publication

Article, 2024

On the Opacity of Deep Neural Networks

Canadian Journal of Philosophy, ISSN 0045-5091, 10.1017/can.2024.1

Contributors

Sogaard A. 0000-0001-5250-4276 (Corresponding author) [1]

Affiliations

  1. [1] University of Copenhagen
  2. [NORA names: KU University of Copenhagen; University; Denmark; Europe, EU; Nordic; OECD]

Abstract

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.

Keywords

deep neural networks, explainability, mitigation, model size, opacity

Data Provider: Elsevier