Tracking Historical Changes in Trustworthiness Using Machine Learning Analyses of Facial Cues in Paintings

L. Safra, C. Chevallier, J. Grèzes & N. Baumard

November 11, 2020

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Social trust is linked to positive societal outcomes, (eg economics, less crime & inclusivity) but is difficult to document. Machine learning algorithm analyses of portrait facial cues documents trustworthiness increased (1500–2000) paralleling the decline of violence & the rise of democratic values.
#RelationalSpace #Science #Algorithm #Democracy

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Artists and scientists alike are focused on exploring the world and try to understand the universe through introspection, examination and experimentation. Their differences in perspective and methodology can lead to novel dialogue. Combining scientific exploration with creative expression offers unique opportunities for forming new knowledge, discovery and creativity. To tackle the intractable challenges of our global society requires a brand new language – a language of interdisciplinarity, interconnectedness, and intersectionality. Relational Space provides an immersive space for artistic expression and scholarly knowledge exchange with focus on Evidence-based Art: Art presented in relation to empirically supported ideas to promote transformational social change. Relational Space encourages communication and collaboration among artists, scientists, policy-makers and community activists. Our vision sees a more just and sustainable world, inspired by art and transformed through truth. 

 

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