The growing use of generative AI raises ethical concerns about authorship attribution and plagiarism. This study examines how people judge the reuse of AI-generated content, focusing on moral patiency and ownership perceptions. In an experiment, participants evaluated two substantively similar manuscripts in which the original source was described as authored by a human, an AI system, or an AI agent with a human-like name. Results showed that copying AI-generated work was judged less unethical, less plagiaristic, and less guilt-inducing than copying human-authored work. Mediation analyses revealed that this leniency stemmed from lower perceptions of AI's capacity to suffer harm (moral patiency) and greater ownership attributed to the human writer reusing AI-generated content. Anthropomorphic cues shaped moral evaluations indirectly by reducing perceived ownership. These findings shed light on how people morally disengage when using AI-generated work and highlight differences in how ethical judgments are applied to human versus AI-created content.

With the widespread use of artificial intelligence and automated decision-making (ADM), concerns are increasing about automated decisions biased against certain social groups, such as women and racial minorities. The public's skepticism and the danger of algorithmic discrimination are widely acknowledged, yet the role of key factors constituting the context of discriminatory situations is underexplored. This study examined people’s perceptions of gender bias in ADM, focusing on three factors influencing the responses to discriminatory automated decisions: the target of discrimination (subject vs. other), the gender identity of the subject, and situational contexts that engender biases. Based on a randomised experiment (N = 602), we found stronger negative reactions to automated decisions that discriminate against the gender group of the subject than those discriminating against other gender groups, evidenced by lower perceived fairness and trust in ADM, and greater negative emotion and tendency to question the outcome. The negative reactions were more pronounced among participants in underserved gender groups than men. Also, participants were more sensitive to biases in economic and occupational contexts than in other situations. These findings suggest that perceptions of algorithmic biases should be understood in relation to the public's lived experience of inequality and injustice in society.

There has been concern about the proliferation of the "QAnon" conspiracy theory on Facebook, but little is known about how its misleading narrative propagated on the world's largest social media platform. Thus, the present research analyzed content generated by 2,813 Facebook pages and groups that contributed to promoting the conspiracy narrative between 2017 and 2020. The result demonstrated that activities of QAnon pages and groups started a significant surge months before the 2020 U.S. Presidential Election. We found that these pages and groups increasingly relied on internal sources, i.e., Facebook accounts or their content on the platform, while their dependence on external information sources decreased continuously since 2017. It was also found that QAnon posts based on the Facebook internal sources attracted significantly more shares and comments compared with other QAnon posts. These findings suggest that QAnon pages and groups increasingly isolated themselves from sources outside Facebook while having more internal interactions within the platform, and the endogenous creation and circulation of disinformation might play a significant role in boosting the influence of the misleading narrative within Facebook. The findings imply that the efforts to tackle disinformation on social media should target not only the cross-platform infiltration of falsehood but also the intra-platform production and propagation of disinformation.

Design strategy for non-regenerative multiple-input multiple-output (MIMO) relay system with partial channel state information (CSI) is developed. We assume that the CSI of the base station-relay link is fully known to the relay, while the CSI of the relay-mobile station link is not known except its channel statistics. Based on this model, weighting matrices to increase ergodic capacity is proposed for downlink and uplink MIMO-relay system, respectively, with the approximation for both the high and low signal-to-noise ratio (SNR) region. We also propose a switching scheme to cover the intermediate SNR region. Numerical results show that the proposed scheme outperforms the conventional one especially when the SNR becomes high.