Understanding and improving trust in scientific truths: an experimental approach – TrustSciTruths

The fact that many people do not believe in scientific truths has important and sometimes even dramatic consequences. Since people's rationality is bounded, trust in experts and in processes set up by experts is necessary. Trust in experts may depend on who the expert is. Furthermore, trust in a process may be affected by whether we like the outcome of the process or not. Our project is articulated in 4 axes.
The first axis is interested in algorithm aversion, the distrust the public can have in a scientific procedure aiming at reaching an outcome, namely the allocation of prospective students to universities.
The second axis investigates trust in experts depending on their gender and the gender-stereotypes associated to the field of expertise. Are we more likely to follow the recommendation when it comes from a male expert than from a female expert when the area of expertise is perceived as masculine? Will one also rather receive a recommendation from a male expert rather than from a female expert? Do the answers to these questions change when the area of expertise is perceived as feminine?
The third axis is interested in the trust we have in human reasoning depending on whether we agree with the conclusion or not. Is it more difficult to perceive flaws in logical reasoning when one agrees with the reasoning process's conclusion than when one does not?
The fourth axis will propose a theoretical mode that will aim at getting al deeper understanding of the interplay and dynamics between trust in a scientific process-matching algorithm- and the outcome of the process-an allocation of students to universities. It may also allow us to evaluate the performance of algorithms along a new dimension by distinguishing those for which a high-trust equilibrium exists from those for which trust is not a sustainable outcome.