Variantism about Responsibility

This chapter argues that experimental assessment of intuition pumps in the moral responsibility literature presses a dilemma on philosophically orthodox approaches to moral responsibility, which frequently maintain both invariantism and conservativism. Invariantist approaches maintain that there are exceptionlessly relevant criteria for responsibility attribution, while conservative approaches maintain that folk theories of responsibility are (defeasible, but substantial) constraints of philosophical theories of responsibility. However, the experimental record suggests that folk theory is conspicuously variantist (or pluralist) and evinces little commitment to exceptionlessly relevant attribution criteria. Therefore, invariantism and conservativism cannot be simultaneously maintained. Implications for theory choice and revision are discussed.

Rationality and reasonableness are central tenets of folk theories of sound judgment

We explore folk theories of sound judgment across two cultures, with a particular focus on the distinction between rationality and reasonableness and how people apply these concepts in a range of social and non-social contexts. Four studies using English-speaking samples in North America (Studies 1-3; N=1,826) and a Mandarin-speaking sample in China (Study 4; N=659) examine spontaneous descriptions of characteristics of sound judgment, preferences for and perception of agents in different social contexts (varying in demands for rule-based vs. holistic approaches to decision-making), and categorization of non-social objects. People spontaneously considered both rationality and reasonableness as central features of sound judgment and yet assigned unique attributes to these standards when mapping concept networks. In experiments, people favored rational agents for contexts demanding analytic reasoning and reasonable agents for contexts demanding interpretive/holistic reasoning. Moreover, across cultures, people used rule-based categorization for rational judgment and overall-similarity categorization for reasonable judgment of non-social objects.

Deplatforming, demotion and folk theories of Big Tech persecution

This article examines the moderation of conspiracy narratives surrounding COVID-19 through digital methods analysis of deplatformed or demoted videos. Building upon the literature on moderation, it performs a comparison of the types of content moderated by YouTube during the early stages of the pandemic. It seeks to determine the extent to which YouTube's own moderation actions are brought in as part of the conspiratorial narratives surrounding COVID-19, while investigating how it is that moderation becomes entangled with questions of truth and visibility.

Only “sheep” trust journalists? How citizens’ self-perceptions shape their approach to news

The all-consuming nature of coronavirus news coverage has made the COVID-19 pandemic a unique opportunity to explore the relationship between audience trust in and engagement with news. This study examines that relationship through 60 Zoom-based qualitative interviews conducted with a diverse sample of US adults during the early phase of the pandemic. We find that how people approach the news stems not only from how they perceive the trustworthiness of individual news outlets, but also from their own self-perceptions. News consumers believe journalism generally suffers from issues of bias, but that they are savvy and independent-minded enough to see through those biases to find the truth. Putting the concept of partisan selective exposure into conversation with folk theories of news consumption, we conclude that people’s approach to and trust in news is as dependent on what they bring to the news as it is on what news brings to them.

“Crystal Is Creepy, but Cool”: Mapping Folk Theories and Responses to Automated Personality Recognition Algorithms

This article examines Crystal Knows, a company that generates automated personality profiles through an algorithm and sells access to their database. These algorithms are the result of a long line of research into computational and predictive algorithms that track social media practices and uses them to infer individual characteristics and make psychometric assessments. Although it is now computationally possible, these algorithms are not widely known or understood by the general public. Little is known about how people would respond to them, particularly when they do not even know their online activities are being assessed by the algorithm. This study examines how people construct “snap” folk theories about the ways personality algorithms operate as well as how they react when shown their outputs. Through qualitative interviews ( n = 37) with people after being presented with their own profile, this study identifies a series of folk theories that people came up with to explain the personality algorithm across four dimensions (data source, scope, collection process, and outputs). In addition, this study examined how those folk theories contributed to certain reactions, fears, and justifications people had about the algorithm. This study builds on our theoretical understanding of folk theory literature as well as certain limitations of algorithmic transparency/sovereignty when these types of inferential and predictive algorithms get coupled with people’s hopes and fears about employment, hiring, and promotion.