Concerns About Automation and Negative Sentiment Toward Immigration

Political Bot Bias in the Perception of Online Discourse

Four nationally representative studies ( N = 1,986; three preregistered) find evidence for a bias in how people perceive opposing viewpoints expressed through online discourse. These studies elucidate a political bot bias, where political partisans (vs. their out-party) are more likely to view counter-ideological (vs. ideologically consistent) tweets to be social media bots (vs. humans). Study 1 demonstrates that American Democrats and Republicans are more likely to attribute tweets to bots when those tweets express counter-ideological views. Study 2 demonstrated this bias with actual bot tweets generated by the Russian government and comparable human tweets. Study 3 demonstrated this bias manifests in the context of real recent elections and is associated with markers of political animosity. Study 4 experimentally demonstrates the consequences of bot attribution for perceptions of online political discourse. Our findings document a consistent bias that has implications for political discussion online and political polarization more broadly.

AI Experience Predicts Identification with Humankind

Artificial intelligence is becoming a potential outgroup of humans, which, according to social identity theory, may make humanity more salient. To explore how identification with humankind correlates to being exposed to artificial intelligence, we developed an AI Experience Questionnaire to measure this relationship and demonstrated that AI experience positively predicted human identity (Study 1a, N = 806). This correlation held when controlling for AI threats, educational level, international mobility experience, gender, and age (Study 2, N = 981, M_age = 27.55 ± 6.74; 448 males, 533 females). Study 1a also demonstrated that AI awareness-consisting of perceived anthropomorphism and perceived proximity-mediated the relationship between AI experience and human identity. This mediation model was replicated half a year later (Study 1b, N = 886). Moreover, a moderation analysis demonstrated that for both Easterners and Westerners, the correlation between AI experience and human identity was significantly positive; however, Western culture amplified the correlation (Study 3; N = 177, M_age = 32.35 ± 10.99; 90 Easterners, 87 Westerners). To conclude, persons with more AI experience may be more inclined to perceive AI as an outgroup of humans, and therefore AI experience positively predicts identification with humankind.

Threat Rejection Fuels Political Dehumanization

Americans disagree about many things, including what threats are most pressing. We suggest people morally condemn and dehumanize opponents when they are perceived as rejecting the existence or severity of important perceived threats. We explore perceived “threat rejection” across five studies ( N = 2,404) both in the real-world COVID-19 pandemic and in novel contexts. Americans morally condemned and dehumanized policy opponents when they seemed to reject realistic group threats (e.g., threat to the physical health or resources of the group). Believing opponents rejected symbolic group threats (e.g., to collective identity) was not reliably linked to condemnation and dehumanization. Importantly, the political dehumanization caused by perceived threat rejection can be soothed with a “threat acknowledgement” intervention.

Individual vulnerability to industrial robot adoption increases support for the radical right

The increasing success of populist and radical-right parties is one of the most remarkable developments in the politics of advanced democracies. We investigate the impact of industrial robot adoption on individual voting behavior in 13 western European countries between 1999 and 2015. We argue for the importance of the distributional consequences triggered by automation, which generates winners and losers also within a given geographic area. Analysis that exploits only cross-regional variation in the incidence of robot adoption might miss important facets of this process. In fact, patterns in individual indicators of economic distress and political dissatisfaction are masked in regional-level analysis, but can be clearly detected by exploiting individual-level variation. We argue that traditional measures of individual exposure to automation based on the current occupation of respondents are potentially contaminated by the consequences of automation itself, due to direct and indirect occupational displacement. We introduce a measure of individual exposure to automation that combines three elements: 1) estimates of occupational probabilities based on employment patterns prevailing in the preautomation historical labor market, 2) occupation-specific automatability scores, and 3) the pace of robot adoption in a given country and year. We find that individuals more exposed to automation tend to display higher support for the radical right. This result is robust to controlling for several other drivers of radical-right support identified by earlier literature: nativism, status threat, cultural traditionalism, and globalization. We also find evidence of significant interplay between automation and these other drivers.