Necessity and sufficiency are the building blocks of all successful explanations. Yet despite their importance, these notions have been conceptually underdeveloped and inconsistently applied in explainable artificial intelligence (XAI), a fast-growing research area that is so far lacking in firm theoretical foundations. Building on work in logic, probability, and causality, we establish the central role of necessity and sufficiency in XAI, unifying seemingly disparate methods in a single formal framework. We provide a sound and complete algorithm for computing explanatory factors with respect to a given context, and demonstrate its flexibility and competitive performance against state of the art alternatives on various tasks.

While the existence of a fundamental relationship between logic and information seems unquestionable, its precise nature has so far proved to be rather elusive and somewhat puzzling. The received view on this issue is epitomized by the traditional tenet that logical inference is ‘tautological’ (literally, repetitive and trivially true), namely that a valid inference is one in which the information carried by the conclusion is (in a sense variously specified) contained in the information carried by the (conjunction of the) premises. At the mid of the 20th century, Bar-Hillel and Carnap's controversial theory of ‘semantic information’ provided what is, to date, the strongest theoretical justification for this tenet. However, as remarked by a number of authors who have made the history of logic, including Frege, Dummett and Hintikka, the received view clashes with the intuitive idea that deductive arguments are useful just because, by their means, we obtain information that we did not possess before. Moreover, it also clashes with two 20th-century milestones of the theory of computation: the undecidability of first-order logic and the NP-hardness of propositional logic. How can logic be informationally trivial and, yet, computationally hard? Can we obtain an informational characterization of logical consequence that is more in tune with our intuition and with the negative results on the computational complexity of logical reasoning?

At least since the sixties, there have been many attempts to understand the logic of design, when the latter is broadly understood as a purposeful way of realizing an artefact. In this talk, I shall explore current methodologies to see how they may be adapted to cases in which what is being designed is information, in the sense of both a semantic artefact (e.g. a train timetable) and a communication process (e.g. the announcement that a specific train is leaving from particular platform).

In written and spoken communications, figures of speech (e.g., metaphors and synecdoche) are often used as an aid to help convey abstract or less tangible concepts. However, the benefits of using rhetorical illustrations or embellishments in visualization have so far been inconclusive. In this work, we report an empirical study to evaluate hypotheses that visual embellishments may aid memorization, visual search and concept comprehension. One major departure from related experiments in the literature is that we make use of a dual-task methodology in our experiment. This design offers an abstraction of typical situations where viewers do not have their full attention focused on visualization (e.g., in meetings and lectures). The secondary task introduces "divided attention", and makes the effects of visual embellishments more observable. In addition, it also serves as additional masking in memory-based trials. The results of this study show that visual embellishments can help participants better remember the information depicted in visualization. On the other hand, visual embellishments can have a negative impact on the speed of visual search. The results show a complex pattern as to the benefits of visual embellishments in helping participants grasp key concepts from visualization.

This article reviews eight proposed strategies for solving the symbol grounding problem (SGP), which was given its classic formulation in Harnad (Citation1990). After a concise introduction, the paper provides an analysis of the requirement that must be satisfied by any hypothesis seeking to solve the SGP, the zero semantical commitment condition. It is then used to assess the eight strategies, which are organized into three main approaches: representationalism, semi-representationalism and non-representationalism. The conclusion is that all the strategies are semantically committed and hence that none of them provides a valid solution to the SGP, which remains an open problem.