Deep Learning Opacity in Scientific Discovery
This addresses the problem of reconciling AI's opacity with its scientific utility for philosophers and scientists, but it is incremental as it builds on existing philosophical distinctions.
The paper tackles the disconnect between philosophical pessimism about deep learning opacity and scientific optimism by arguing that examining AI's actual use in science reveals epistemic justification for breakthroughs, showing opacity need not diminish AI's capacity for significant discoveries.
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.