Beyond Transparency

Abstract

This chapter examines the epistemology of algorithms, framing the discussion as a question of epistemic justification. Current approaches emphasize algorithmic transparency, which involves elucidating internal mechanisms—such as functions and variables—and demonstrating how (or that) these compute outputs. Thus, the mode of justification through transparency is contingent on what can be shown about the algorithm and, in this sense, is internal to the algorithm. In contrast, I propose an externalist epistemology of algorithms called computational reliabilism (CR). While I have previously developed CR in the context of computer simulations (Durán, Explaining simulated phenomena: A defense of the epistemic power of computer simulations, 2013; Durán, Computer simulations in science and engineering. Concepts - practices - perspectives. Springer, 2018; Durán, Formanek, Minds and Machines 28(4), 645–666, 2018), this chapter extends the framework to a broader range of algorithms used across scientific disciplines, particularly in machine learning and deep neural networks. At its core, CR posits that an algorithm’s output is justified if it is generated by a reliable algorithm, where reliability is determined by reliability indicators. These indicators arise from formal methods, algorithmic metrics, expert competencies, research cultures, and other scientific practices. The chapter’s primary objectives are to delineate the foundations of CR, explain its operational mechanisms, and outline its potential as an externalist epistemology of algorithms.