The traditional sciences often bracket away ambiguity through the imposition of “enabling constraints”—making a set of assumptions and then declaring ceteris paribus. These enabling constraints take the form of uncritically examined presuppositions or “uceps.” Second order science reveals hidden issues, problems and assumptions which all too often escape the attention of the practicing scientist. These hidden values—precisely because they are hidden and not made explicit—can get in the way of the public’s acceptance of a scientific claim. A conflict in understood (...) meaning—between the scientist’s restricted claims and the public’s broader understanding of those same claims can result in cognitive dissonance or the equivalent of the Mori Uncanny Valley. Scientists often react to these differences by claiming “incommensurability” between their restricted claim and the public’s understanding. Second order science, by explicating the effects of variations in values assumed for these uceps and associated impacts on related scientific claims, can often moot these assertions of incommensurability and thereby promote greater scientific understanding. This article explores how second order science can address issues of public doubt regarding the scientific enterprise using examples from medicine, diet and climate science. (shrink)

A widespread need to explain the behavior and outcomes of AI-based systems has emerged, due to their ubiquitous presence. Thus, providing renewed momentum to the relatively new research area of eXplainable AI (XAI). Nowadays, the importance of XAI lies in the fact that the increasing control transference to this kind of system for decision making -or, at least, its use for assisting executive stakeholders- already affects many sensitive realms (as in Politics, Social Sciences, or Law). The decision-making power handover to (...) opaque AI systems makes mandatory explaining those, primarily in application scenarios where the stakeholders are unaware of both the high technology applied and the basic principles governing the technological solutions. The issue should not be reduced to a merely technical problem; the explainer would be compelled to transmit richer knowledge about the system (including its role within the informational ecosystem where he/she works). To achieve such an aim, the explainer could exploit, if necessary, practices from other scientific and humanistic areas. The first aim of the paper is to emphasize and justify the need for a multidisciplinary approach that is beneficiated from part of the scientific and philosophical corpus on Explaining, underscoring the particular nuances of the issue within the field of Data Science. The second objective is to develop some arguments justifying the authors’ bet by a more relevant role of ideas inspired by, on the one hand, formal techniques from Knowledge Representation and Reasoning, and on the other hand, the modeling of human reasoning when facing the explanation. This way, explaining modeling practices would seek a sound balance between the pure technical justification and the explainer-explainee agreement. (shrink)