In this talk, I aim to explore the significance of trust and justification in machine learning (ML). To begin, I'll briefly touch upon two promising epistemologies for ML — transparency and computational reliabilism (CR). However, my focus will be on defending the latter, requiring a more in-depth discussion. I'll dedicate some time to elucidate how CR operates, and which assumptions are built-in. Next, I plan to illustrate how CR works in the context of Forensic ML. Lastly, I'll address two objections against CR: i) the concern that, under CR, statistically insignificant yet serious errors can compromise the reliability of AI algorithms; and ii) the argument that CR, being a reliabilist epistemology, demands a high frequency of success, ultimately posing an issue of high predictive accuracy. I'll present arguments to counter these objections, advocating for computational reliabilism as a promising epistemology for ML.

Juan M. Durán is an Assistant Professor at Delft University of Technology, The Netherlands. He has been working on the intersection between philosophy of science, epistemology, ethics and technology, first with computer simulations and more recently with machine learning. He is the author of Computer Simulations in Science and Engineering (Springer, 2018) and the awardee of the 2019 Herbert Simon Award.