Neuroplasticity, neural reuse, and the language module

What conception of mental architecture can survive the evidence of neuroplasticity and neural reuse in the human brain? In particular, what sorts of modules are compatible with this evidence? I aim to show how developmental and adult neuroplasticity, as well as evidence of pervasive neural reuse, forces us to revise the standard conception of modularity and spells the end of a hardwired and dedicated language module. I argue from principles of both neural reuse and neural redundancy that language is facilitated by a composite of modules (or module-like entities), few if any of which are likely to be linguistically special, and that neuroplasticity provides evidence that (in key respects and to an appreciable extent) few if any of them ought to be considered developmentally robust, though their development does seem to be constrained by features intrinsic to particular regions of cortex (manifesting as domain-specific predispositions or acquisition biases). In the course of doing so I articulate a schematically and neurobiologically precise framework for understanding modules and their supramodular interactions

Neural redundancy and its relation to neural reuse

Evidence of the pervasiveness of neural reuse in the human brain has forced a revision of the standard conception of modularity in the cognitive sciences. One persistent line of argument against such revision, however, draws from a large body of experimental literature attesting to the existence of cognitive dissociations. While numerous rejoinders to this argument have been offered over the years, few have grappled seriously with the phenomenon. This paper offers a fresh perspective. It takes the dissociations seriously, on the one hand, while affirming that traditional modularities of mind do not do justice to the evidence of neural reuse, on the other. The key to the puzzle is neural redundancy. The paper offers both a philosophical analysis of the relation between reuse and redundancy, as well as a plausible solution to the problem of dissociations

Explaining machine learning decisions

The operations of deep networks are widely acknowledged to be inscrutable. The growing field of “Explainable AI” (XAI) has emerged in direct response to this problem. However, owing to the nature of the opacity in question, XAI has been forced to prioritise interpretability at the expense of completeness, and even realism, so that its explanations are frequently interpretable without being underpinned by more comprehensive explanations faithful to the way a network computes its predictions. While this has been taken to be a shortcoming of the field of XAI, I argue that it is broadly the right approach to the problem

Algorithmic Decision-Making and the Control Problem

Abstract: The danger of human operators devolving responsibility to machines and failing to detect cases where they fail has been recognised for many years by industrial psychologists and engineers studying the human operators of complex machines. We call it “the control problem”, understood as the tendency of the human within a human–machine control loop to become complacent, over-reliant or unduly diffident when faced with the outputs of a reliable autonomous system. While the control problem has been investigated for some time, up to this point its manifestation in machine learning contexts has not received serious attention. This paper aims to fill that gap. We argue that, except in certain special circumstances, algorithmic decision tools should not be used in high-stakes or safety-critical decisions unless the systems concerned are significantly “better than human” in the relevant domain or subdomain of decision-making. More concretely, we recommend three strategies to address the control problem, the most promising of which involves a complementary (and potentially dynamic) coupling between highly proficient algorithmic tools and human agents working alongside one another. We also identify six key principles which all such human–machine systems should reflect in their design. These can serve as a framework both for assessing the viability of any such human–machine system as well as guiding the design and implementation of such systems generally

Government Use of Artificial Intelligence in New Zealand

Final Report on Phase 1 of the New Zealand Law Foundation’s Artificial Intelligence and Law in New Zealand Projec