Deep Learning Opacity in Scientific Discovery

TL;DR

This paper argues that despite the opacity of deep neural networks, AI can still significantly contribute to scientific discovery when its role within the broader discovery process is properly understood, challenging philosophical pessimism.

Contribution

The paper highlights the importance of the discovery context in understanding AI's epistemic role, reconciling philosophical concerns with scientific successes.

Findings

AI-driven breakthroughs are justified within the discovery process.

Epistemic opacity does not necessarily hinder scientific progress.

The distinction between discovery and justification clarifies AI's epistemic role.

Abstract

Philosophers have recently focused on critical, epistemological challenges that arise from the opacity of deep neural networks. One might conclude from this literature that doing good science with opaque models is exceptionally challenging, if not impossible. Yet, this is hard to square with the recent boom in optimism for AI in science alongside a flood of recent scientific breakthroughs driven by AI methods. In this paper, I argue that the disconnect between philosophical pessimism and scientific optimism is driven by a failure to examine how AI is actually used in science. I show that, in order to understand the epistemic justification for AI-powered breakthroughs, philosophers must examine the role played by deep learning as part of a wider process of discovery. The philosophical distinction between the 'context of discovery' and the 'context of justification' is helpful in this regard. I demonstrate the importance of attending to this distinction with two cases drawn from the scientific literature, and show that epistemic opacity need not diminish AI's capacity to lead scientists to significant and justifiable breakthroughs.