Lavinia Marin

Many hypotheses have been advanced to explain the collective irrationality of COVID-19 vaccine hesitancy, such as partisanship and ideology, exposure to misinformation and conspiracy theories or the effectiveness of public messaging. This paper presents a complementary explanation to epistemic accounts of collective irrationality, focusing on the moral reasons underlying people’s decisions regarding vaccination. We argue that the moralization of COVID-19 risk mitigation measures contributed to the polarization of groups along moral values, which ultimately led to the emergence of collective irrational behaviors. Collective irrationality arises from groups explicitly or implicitly endorsing values that ultimately harm both themselves and those around. The role of social media platforms in amplifying this polarization and contributing to the emergence of collective irrationality is also examined. Finally, potential strategies for addressing the moral sources of collective irrationality are discussed.

This paper proposes a conceptual framework for evaluating how social networking platforms fare as epistemic environments for human users. I begin by proposing a situated concept of epistemic agency as fundamental for evaluating epistemic environments. Next, I show that algorithmic personalisation of information makes social networking platforms problematic for users’ epistemic agency because these platforms do not allow users to adapt their behaviour sufficiently. Using the tracing principle inspired by the ethics of self-driving cars, I operationalise it here and identify three requirements that automated epistemic environments need to fulfil: (a) the users need to be afforded a range of skilled actions; (b) users need to be sensitive to the possibility to use their skills; (c) the habits built when adapting to the platform should not undermine the user’s pre-existing skills. I then argue that these requirements are almost impossible to fulfil all at the same time on current SN platforms; yet nevertheless, we need to pay attention to these whenever we evaluate an epistemic environment with automatic features. Finally, as an illustration, I show how Twitter, a popular social networking platform, will fare regarding these requirements.