We examine the epistemological foundations of a leading technique in the search for evidence of life on exosolar planets. Specifically, we consider the “transit method” for spectroscopic analysis of exoplanet atmospheres, and the practice of treating anomalous chemical compositions of the atmospheres of exosolar planets as indicators of the potential presence of life. We propose a methodology for ranking the anomalousness of atmospheres that uses the mathematical apparatus of support vector machines, and which aims to be agnostic with respect to (...) the particular chemical biosignatures of life. We argue that our approach is justified by an appeal to the “hinge” model of epistemic justification first proposed by as reported by Wittgenstein. We then compare our approach to previous work due to Walker et al. :779–824, 2018) and Cleland :722–729, 2019a; Does ‘life’ have a definition?, Cambridge University Press, New York, 2019b). (shrink)

Arbitrariness is a distinctive feature of human language, and a growing body of comparative work is investigating its presence in animal communication. But what is arbitrariness, exactly? We propose to distinguish four notions of semiotic arbitrariness: a notion of opaque association between sign forms and semiotic functions, one of sign‐function mapping optionality, one of acquisition‐dependent sign‐function coupling, and one of lack of motivatedness. We characterize these notions, illustrate the benefits of keeping them apart, and describe two reactions to our proposal: (...) abandoning arbitrariness‐talk in favor of the newly introduced conceptual vocabulary, or feeding the distinctions back into the parent concept. (shrink)

It has been argued that our currently most satisfactory social epistemology of science can’t account for science that is based on artificial intelligence (AI) because this social epistemology requires trust between scientists that can take full responsibility for the research tools they use, and scientists can’t take full responsibility for the AI tools they use since these systems are epistemically opaque. I think this argument overlooks that much AI-based science can be done without opaque models, and that agents can take (...) full responsibility for the systems they use even if these systems are opaque. Requiring that an agent fully understand how a system works is an untenably strong condition for that agent to take full responsibility for the system and risks absolving AI developers from responsibility for their products. AI-based science need not create trust-related social epistemological problems if we keep in mind that what makes both individual scientists and their use of AI systems trustworthy isn’t full transparency of the internal processing but their adherence to social and institutional norms that ensure that scientific claims can be trusted. (shrink)