This paper explores the practice of
explanation by status
, in which a truth with a certain status (i.e. necessary status, essential status, or status as a law) is supposed to be explained by its ...having that status. It first investigates whether such explanations are possible. Having found existing accounts of the practice wanting, it then argues for a novel account of explanation by status as
empty-base explanation
. The latter notion captures a certain limiting case of ordinary explanation so that according to the empty-base account, explanation by status can be fruitfully understood as a corresponding limiting case of ordinary explanation. One way in which the empty-base account is argued to be superior to other treatments of explanation by status is that it allows for a principled assessment of the possibility of particular kinds of explanation by status. Thus, one result of the present discussion is that explanation by essential status and status as a law are possible, while explanation by merely necessary status is not.
In this paper we explore the effect of explanations on reducing errors in the human decision making process caused by placing excessive reliance on automated decision support systems. We develop and ...implement different forms of explanations based on cognitive principles and evaluate their effect over two different domains: our new version of the Coloured Trails game, and over a simulated radiological task. We found that explanations did not reduce this aspect of automation bias and sometimes increased it. However, they reduced completion time and often increased user decision accuracy, despite not altering the perceived task load. Overall, explanations were beneficial though the benefits were highly context dependent. This work contributes to the complex interplay between automation bias, performance and explanations.
Dimensions of explanation Hochstein, Eric
Zagadnienia Filozoficzne w Nauce (Online),
01/2023, Letnik:
74, Številka:
74
Journal Article
Recenzirano
Odprti dostop
Some argue that the term “explanation” in science is ambiguous, referring to at least three distinct concepts: a communicative concept, a representational concept, and an ontic concept. Each is ...defined in a different way with its own sets of norms and goals, and each of which can apply in contexts where the others do not. In this paper, I argue that such a view is false. Instead, I propose that a scientific explanation is a complex entity that can always be analyzed along a communicative dimension, a representational dimension, and an ontic dimension. But all three are always present within scientific explanations. I highlight what such an account looks like, and the potential problems it faces (namely that a single explanation can appear to have incompatible sets of norms and goals that govern it). I propose a solution to this problem and demonstrate how this account can help to dissolve current disputes in philosophy of science regarding debates between epistemic and ontic accounts of mechanistic explanations in the life sciences.
Drawing on the philosophy of psychological explanation, we suggest that psychological science, by focusing on effects, may lose sight of its primary explananda: psychological capacities. We revisit ...Marr’s levels-of-analysis framework, which has been remarkably productive and useful for cognitive psychological explanation. We discuss ways in which Marr’s framework may be extended to other areas of psychology, such as social, developmental, and evolutionary psychology, bringing new benefits to these fields. We then show how theoretical analyses can endow a theory with minimal plausibility even before contact with empirical data: We call this the theoretical cycle. Finally, we explain how our proposal may contribute to addressing critical issues in psychological science, including how to leverage effects to understand capacities better.
•The area under the analyst certainty curve is a better measure compared to the minimum feature prefix.•For the outlier-based sequential explanations (SE), the particle swarm optimisation search ...method only outperforms the greedy search methods that make use of the same outlier scoring measure.•The sample-based SE, support vector machine recursive feature elimination SE, returned the best performing SE overall.•The best performing outlier and sample-based SEs outperformed the best performing sequential feature explanations (SFE).
In most applications, anomaly detection operates in an unsupervised mode by looking for outliers hoping that they are anomalies. Unfortunately, most anomaly detectors do not come with explanations about which features make a detected outlier point anomalous. Therefore, it requires human analysts to manually browse through each detected outlier point’s feature space to obtain the subset of features that will help them determine whether they are genuinely anomalous or not. This paper introduces sequential explanation (SE) methods that sequentially explain to the analyst which features make the detected outlier anomalous. We present two methods for computing SEs called the outlier and sample-based SE that will work alongside any anomaly detector. The outlier-based SE methods use an anomaly detector’s outlier scoring measure guided by a search algorithm to compute the SEs. Meanwhile, the sample-based SE methods employ sampling to turn the problem into a classical feature selection problem. In our experiments, we compare the performances of the different outlier- and sample-based SEs. Our results show that both the outlier and sample-based methods compute SEs that perform well and outperform sequential feature explanations.
There are at least two deep and related debates about explanation: about its nature and about its norms. The aim of this special issue of Philosophical Problems in Science/Zagadnienia Filozoficzne w ...Nauce (ZFN) is to survey whether or not a consensus is at hand in these debates and to help settle what it can. The overarching foci are twofold: (i) the nature of scientific explanation, with special attention to the debate between ontic and epistemic conception of explanation, and (ii) the norms of scientific explanation, with special attention to so-called ‘ontic’ (or better, ‘alethic’) norms like truth and referential success and epistemic norms like intelligibility and idealized understanding. It called for advocates of various conceptions to articulate the current state of these debates. Researchers and scholars from around the globe—including Poland, Canada, Korea, The Netherlands, the United States, Greece, Austria, and Belgium—contributed. The special issue also attempts to provide an opening for new work on the norms of explanation, such as truth or model-based accuracy, information compression, abstraction, and generalization.
Psychology has historically been concerned, first and foremost, with explaining the causal mechanisms that give rise to behavior. Randomized, tightly controlled experiments are enshrined as the gold ...standard of psychological research, and there are endless investigations of the various mediating and moderating variables that govern various behaviors. We argue that psychology’s near-total focus on explaining the causes of behavior has led much of the field to be populated by research programs that provide intricate theories of psychological mechanism but that have little (or unknown) ability to predict future behaviors with any appreciable accuracy. We propose that principles and techniques from the field of machine learning can help psychology become a more predictive science. We review some of the fundamental concepts and tools of machine learning and point out examples where these concepts have been used to conduct interesting and important psychological research that focuses on predictive research questions. We suggest that an increased focus on prediction, rather than explanation, can ultimately lead us to greater understanding of behavior.
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