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Trust is a central pillar of the scientific enterprise. Much work in the philosophy of science can be seen as coping with the problem of establishing trust in a certain theory, a certain model, or even science as a whole. However, trust in science is threatened by various developments. With the advent of more complex models and the increasing usage of computer methods such as machine learning and computer simulation, it seems increasingly challenging to establish trust in science. How and on what basis can an appropriate trust in science be built? We are interested in how trust is established in such cases of increasing complexity (of models and communication) and what could be appropriate measures to alleviate doubt.
List of contents
Synopsis of Contributions.- Introduction.- Philosophy of Trust.- Heresy and Honor. A Historical Perspective on Trust in Science.- Trusting Science: Is There Reasonable Distrust ofReputable Scientific Authority?.- Can There Be an Epistemic Authority?.- Trust Science With What? Trust-Building Dialogue between Scientists and the Public Trust in Science.- Scientific Experts, Epistemic Wisdom, and Justified Trust.- Confidence: Calibrating Trust in Science Trust and Policy.- Trust in Science During global challenges: the pandemic and trustworthy AI.- Science, Shame, and Trust: Against Shaming Policies, Sociological, Communicative and Media Aspects of Trust in Science. Establishing Trust in Algorithmic Results: Ground Truth Simulations and the First Empirical Images of a Black Hole.- Trust and Science Communication in the Internet Era: The Case of Mainstream Climate Blogging.- Emancipatory Data Literacy and the Value of Trust.- Only a Theory? Substantive and Methodological Strategies for Regaining Trust in Science.- Undermining Trust in Science: No Fraud Required.
Summary
Trust is a central pillar of the scientific enterprise. Much work in the philosophy of science can be seen as coping with the problem of establishing trust in a certain theory, a certain model, or even science as a whole. However, trust in science is threatened by various developments. With the advent of more complex models and the increasing usage of computer methods such as machine learning and computer simulation, it seems increasingly challenging to establish trust in science. How and on what basis can an appropriate trust in science be built? We are interested in how trust is established in such cases of increasing complexity (of models and communication) and what could be appropriate measures to alleviate doubt.
