Ludovica Marinucci

Christiaan Huygens’s late writings, ranging from 1686 to 1695, bear witness to his philosophical and theological reflections. In his Cosmotheoros, which was intended for publication, and other late writings that can be regarded as its preparatory drafts, Huygens deals with issues central to seventeenth-century philosophical debates: God’s power, divine and human intelligence, probabilistic epistemology, natural theology, and the plurality of worlds. This paper explains how Huygens’s reflections on animals and their souls, rational or not, play a key role in his epistemological reflections on natural theology. The issue of animal generation, as well as of animal souls, is crucial to identifying elements of continuity between the scientific topics of Huygens’s works, and may be considered as the point of intersection between his understanding of mechanism and of the teleology of nature. This neglected perspective on Huygens’s philosophical-natural animism reveals key elements of his model of rationality and of his attitude towards religion, demonstrating his involvement in the debate over animism, in which he seems to have been strongly influenced by English Protestant empiricism.

Biases in cognition are ubiquitous. Social psychologists suggested biases and stereotypes serve a multifarious set of cognitive goals, while at the same time stressing their potential harmfulness. Recently, biases and stereotypes became the purview of heated debates in the machine learning community too. Researchers and developers are becoming increasingly aware of the fact that some biases, like gender and race biases, are entrenched in the algorithms some AI applications rely upon. Here, taking into account several existing approaches that address the problem of implicit biases and stereotypes, we propose that a strategy to cope with this phenomenon is to unmask those found in AI systems by understanding their cognitive dimension, rather than simply trying to correct algorithms. To this extent, we present a discussion bridging together findings from cognitive science and insights from machine learning that can be integrated in a state-of-the-art semantic network. Remarkably, this resource can be of assistance to scholars (e.g., cognitive and computer scientists) while at the same time contributing to refine AI regulations affecting social life. We show how only through a thorough understanding of the cognitive processes leading to biases, and through an interdisciplinary effort, we can make the best of AI technology.