Modern explainable AI methods remain far from providing human-like answers to ‘why’ questions, let alone those that satisfactorily agree with human-level understanding. Instead, the results that such methods provide boil down to sets of causal attributions. Currently, the choice of accepted attributions rests largely, if not solely, on the explainee’s understanding of the quality of explanations. The paper argues that such decisions may be transferred from a human to an XAI agent, provided that its machine-learning algorithms perform genuinely abductive inferences. (...) The paper outlines the key predicament in the current inductive paradigm of ML and the associated XAI techniques, and sketches the desiderata for a truly participatory, second-generation XAI, which is endowed with abduction. (shrink)

The naturalization of logic aims at a revision of mainstream logic. In this article, I contend it is an urgent task to be completed. This new project will permit a new collaboration between logic and cognitive science. This can be accomplished doing for logic what many decades ago Quine and other philosophers undertook in the case of epistemology. First of all, this article analyzes how the naturalization can be achieved thanks to some insights provided by the recent John Woods’ book (...) Errors of Reasoning: Naturalizing the Logic of Inference; important concepts that regard a naturalized logic are synthetically analyzed: errors, paradigm creep, third-way reasoning, consequence-having and consequence drawing, agent based reasoning. The article also takes advantage of my own studies, which are aimed both at exculpating the negative fallacious character of abduction and at illustrating the EC-model of it, I have recently proposed. Aiming at encouraging the project of naturalization of logic, the article specifically recommends the increase of logical research on abduction, and emphasizes how current philosophical and logical research on human inferences is indebted towards Charles Sanders Peirce, a philosopher whose importance and modernity are too often underestimated. The final part of the article will introduce an analysis of the importance of the so-called optimization of situatedness, a concept that is necessary to understand that maximization of “abducibility”, which characterizes modern science. (shrink)

We present a dynamic approach to Peirce’s original construal of abductive logic as a logic of conjecture making, and provide a new decidable, contraction-free and cut-free proof system for the dynamic logic of abductive inferences with neighborhood semantics. Our formulation of the dynamic logic of abduction follows the philosophical and scientific track that led Peirce to his late, post-1903 characterization of abductive conclusions as investigands, namely invitations to investigate propositions conjectured at the level of pre-beliefs.

In this article, we want to demonstrate how thoughts experiments (TEs) incorporate cognitive structures—abductive inferences as conceptual metaphors—that reliably underpin everyday thinking and are enhanced and rendered more effective in scientific and philosophical contexts. Indeed one might successfully rethink the inferential structure at the heart of thought experiment production as the application of a generative abductive procedure. We shall characterize TES as possessing two characteristics that are essential to the definitions of abductive and metaphorical thinking, but when considered in relation (...) to TE’s description, can excuse mild bewilderment: both knowledge-enhancing and ignorance-preserving features. In sum, we will say that TEs realize extended conceptual metaphors, which instantiate forms of abductive reasoning and, therefore, partially preserve the ignorance of the authors who produce them (even if they also increase a bit their knowledge by—so to speak—mitigating ignorance). In certain fortunate and exceptional instances, however, TEs can also provide a purely knowledge-enhancing benefit; in order to do this, a reference to the innovative and creative function of thought experiments in Galileo’s findings is also included. (shrink)

Abductive conclusions are drawn in a special, co-hortative mood. Abductive conclusions are representative interpretants that represent abduction as a form of reasoning that can convey a general conception of the truth. The truth is not asserted; abduction merely delivers the idea of a matter of course, rendering that idea comparatively simple and natural, hence assuring us of its justified assertibility. Hence abductive reasoning is at home in addressing ‘How Possible’-questions in science. Abductive reasoning concerns the question of how things might, (...) could or would conceivably be such that they can be plausibly asserted. Peirce took all reasoning to be diagrammatic and representable using the graphical method of logic. Yet no examples have previously been found in his large manuscript corpus of what such non-deductive graphs might look like. This paper proposes a new interpretation of a sole exception, a sketch of two graphs from a rejected page from 1903, which might be the only surviving example of Peirce’s abductive graphs. The proposed interpretation takes them to be representative interpretants of this special inverse type of inference. (shrink)