Issues of reliability are claiming center-stage in the epistemology of machine learning. This paper unifies different branches in the literature and points to promising research directions, whilst also providing an accessible introduction to key concepts in statistics and machine learning – as far as they are concerned with reliability.

The application of machine-learning technologies to medical practice promises to enhance the capabilities of healthcare professionals in the assessment, diagnosis, and treatment, of medical conditions. However, there is growing concern that algorithmic bias may perpetuate or exacerbate existing health inequalities. Hence, it matters that we make precise the different respects in which algorithmic bias can arise in medicine, and also make clear the normative relevance of these different kinds of algorithmic bias for broader questions about justice and fairness in healthcare. (...) In this paper, we provide the building blocks for an account of algorithmic bias and its normative relevance in medicine. (shrink)

Many foundational concepts in machine learning have been criticized as inadequate. Philosophers have therefore taken it upon themselves to sort out the conceptual terrain—with conceptual engineering being the method of choice. This paper takes a step back to provide theoretical and methodological grounding for future work on conceptual engineering in machine learning. To this end, we consider the functional roles of concepts in machine learning, the underlying causes and types of deficiency, and map out criteria for the successful propagation of (...) reengineered concepts within and beyond the machine learning community. Moreover, we discuss how the space of viable conceptual revisions in machine learning is constrained by the need for operationalization, and how tensions can occur between the sociopolitical desirability and the computational implementability of relevant conceptual engineering projects. Overall, our goal is to delineate how conceptual work in philosophy ought to be if the goal is for our contribution to permeate through the science and practice of machine learning. (shrink)