Artificial Intelligence and Attribution

While it is expected that AI is replacing more and more of human-decisions, there has been an increasing concern about who is responsible for the failure (or negative consequences) of AI (e.g., accident caused by self-driving cars). We want to think about AI judges, AI doctors, and AI immigration control officers, and how people will attribute negative decisions from AIs. Eventually, we’re comparing the degree of frustration based on such negative decisions from AI and humans.

Principle Investigator:  Mo Jones-Jang

Co-investigator: Yong Jin Park (Howard U)

___________

Truth and Deception Bias

People are originally known to show a truth bias, the tendency to trust what others say, in the absence of evidence to the contrary. As this truth bias makes people vulnerable to misinformation, media literacy programs and correction efforts underscore how crucial it is to cultivate healthy skepticism among information consumers so they approach information with a critical eye. By highlighting how deeply misinformation has penetrated our information diet, one can hope that people would become more motivated to scrutinize information they encounter.

The current proposal, however, aims to tackle this taken-for-granted process by presenting the evidence of an opposite novel phenomenon, the rise of so-called deception bias among the public and examining how the emerging deception bias may disrupt public engagement and communication with experts in science and political realms. Specifically, this project examines whether and how efforts to combat misinformation, such as media literacy interventions and fact-checking efforts, may unexpectedly elevate public cynicism toward science and political processes. Cynicism is a well-known trigger that lowers public engagement and trust. The project employs both short-term experiments and long-term interventions. All questions are built upon preliminary evidence:

Principle Investigator:  Mo Jones-Jang

___________

Misinformation and Cynicism

Despite many concerns about the negative effects of fake news, little empirical evidence was provided to demonstrate long-term social effects. This project collected 2-week long panel data to show lagged effects of fake news exposure on subsequent cynicism.

This project also attempts to better understand the meaning of misinformation exposure by classifying it into four perceptions of compromised information integrity – perceptions of a) being exposed to misinformation, b) being exposed to misinformation that is fact-checked and proven false, c) prevalence of misinformation in society, and d) being exposed to biased information. We tested whether these perceptions would play distinct roles in increasing cynicism toward politicians and the media during the 2020 election campaign.

Principle Investigator:  Mo Jones-Jang

Co-investigator: Dam Hee Kim (Arizona U) Kate Kenski (Arizona U) Sangwon Lee (New Mexico State U)

___________

Does AI Reduce People’s Partisan Biases?

Upon a surge of misinformation surrounding COVID-19, fact-checking has received much attention as a tool to fight the rampant misinformation. However, such correction efforts have faced challenges from partisans’ biased information processing. For example, partisans trust or distrust a fact-checking message based on whether the message benefits or harms their supporting party. To minimize such politically biased processing of corrective health information, this experimental study examined how different source labels of fact-checkers (human experts vs. AI vs. user consensus) affect partisans’ perceived credibility of fact-checking messages about COVID-19. Our findings showed that AI and user consensus (vs. human experts) source labels on fact-checking messages significantly reduced partisan-based motivated reasoning in evaluating fact-checking message credibility.

Principle Investigators:  Wonki Moon (U Florida) Myo Chung (Northeastern U) Mo Jones-Jang

___________

Why do Minorities Particularly Suffer from Misinformation?

Boston Mayor Michelle Wu has suffered from various fake scandals. Most unverified rumors about Wu were created to appeal to people’s stereotypical beliefs about minority (Asian Women). Triggered by these incidents, this project will identify the psychological explanation of why people are susceptible to misinformation about minority groups.

Principle Investigator:  Mo Jones-Jang