Recent work in quantum gravity has prompted a re-evaluation of the fundamental nature of spacetime. Spacetime is potentially emergent from non-spatiotemporal entities posited by a theory of quantum gravity. Recent efforts have sought to interpret the relationship between spacetime and the fundamental entities through a mereological framework. These frameworks propose that spacetime can be conceived as either having non-spatiotemporal entities as its constituents or being a constituent part of a non-spatiotemporal structure. I present a roadmap for those interested in exploring (...) the role of composition in understanding the emergence of spacetime. I establish a taxonomy based on four crucial parameters that should be considered when constructing potential mereological models. Subsequently, I connect these models to a spectrum of perspectives found in current literature, with the aim of pinpointing areas that require further exploration. Finally, I identify three potential challenges facing mereological models of spacetime emergence, rooted in issues of mereological harmony, the distinction between continuous and discrete spacetime, and the implications of quantum superposition. (shrink)

Explainable AI (xAI) methods are important for establishing trust in using black-box models. However, recent criticism has mounted against current xAI methods that they disagree, are necessarily false, and can be manipulated, which has started to undermine the deployment of black-box models. Rudin (2019) goes so far as to say that we should stop using black-box models altogether in high-stakes cases because xAI explanations ‘must be wrong’. However, strict fidelity to the truth is historically not a desideratum in science. Idealizations (...) – the intentional distortions introduced to scientific theories and models – are commonplace in the natural sciences and are seen as a successful scientific tool. Thus, it is not falsehood qua falsehood that is the issue. In this paper, I outline the need for xAI research to engage in idealization evaluation. Drawing on the use of idealizations in the natural sciences and philosophy of science, I introduce a novel framework for evaluating whether xAI methods engage in successful idealizations or deceptive explanations (SIDEs). SIDEs evaluates whether the limitations of xAI methods, and the distortions that they introduce, can be part of a successful idealization or are indeed deceptive distortions as critics suggest. I discuss the role that existing research can play in idealization evaluation and where innovation is necessary. Through a qualitative analysis we find that leading feature importance methods and counterfactual explanations are subject to idealization failure and suggest remedies for ameliorating idealization failure. (shrink)

We argue that the trend toward providing users with feasible and actionable explanations of AI decisions—known as recourse explanations—comes with ethical downsides. Specifically, we argue that recourse explanations face several conceptual pitfalls and can lead to problematic explanation hacking, which undermines their ethical status. As an alternative, we advocate that explanations of AI decisions should aim at understanding.