Hajo Greif (2022). Analogue Models and Universal Machines. Paradigms of Epistemic Transparency in Artificial Intelligence. Minds & Machines 32: 111-133. Special Issue “Machine Learning: Prediction Without Explanation?”, edited by Florian Boge, Paul Grünke and Rafaela Hillerbrand. Open Access. DOI: 10.1007/s11023-022-09596-9
Abstract: The problem of epistemic opacity in Artificial Intelligence (AI) is often characterised as a problem of intransparent algorithms that give rise to intransparent models. However, the degrees of transparency of an AI model should not be taken as an absolute measure of the properties of its algorithms but of the model’s degree of intelligibility to human users. Its epistemically relevant elements are to be specified on various levels above and beyond the computational one. In order to elucidate this claim, I first contrast computer models and their claims to algorithm-based universality with cybernetics-style analogue models and their claims to structural isomorphism between elements of model and target system (Black 1962). While analogue models aim at perceptually or conceptually accessible model-target relations, computer models give rise to a specific kind of underdetermination in these relations that needs to be addressed in specific ways. I then undertake a comparison between two contemporary AI approaches that, although related, distinctly align with the above modelling paradigms and represent distinct strategies towards model intelligibility: Deep Neural Networks and Predictive Processing. I conclude that their respective degrees of epistemic transparency primarily depend on the underlying purposes of modelling, not on their computational properties.
Acknowledgements: The research presented in this publication was supported by NCN (National Science Centre) OPUS 19 grant ref. 2020/37/B/HS1/01809. The origin of this paper was a symposium on ‘Deep Learning and the Philosophy of Artificial Intelligence’ co-organised by the author at the GWP 2019 conference of the German Society for Philosophy of Science. I thank Cameron Buckner, Holger Lyre and Carlos Zednik for motivating and moving this project. I also thank the guest editors of this Special Issue for their encouragement and patience, Alessandro Facchini and Alberto Termine for their input in the process of preparing the manuscript, and two anonymous reviewers for their competent and constructive criticisms.