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Asynchronous Video Interviews (AVIs) incorporating Artificial Intelligence (AI)-assisted assessment has become popular as a pre-employment screening method. The extent to which applicants engage in deceptive impression management (IM) behaviors during these interviews remains uncertain. Furthermore, the accuracy of human detection in identifying such deceptive IM behaviors is limited. This study seeks to explore differences in deceptive IM behaviors by applicants across video interview modes (AVIs vs Synchronous Video Interviews (SVIs)) and the use of AI-assisted assessment (AI vs non-AI). The study also investigates if video interview modes affect human interviewers' ability to detect deceptive IM behaviors.

The authors conducted a field study with four conditions based on two critical factors: the synchrony of video interviews (AVI vs SVI) and the presence of AI-assisted assessment (AI vs Non-AI): Non-AI-assisted AVIs, AI-assisted AVIs, Non-AI-assisted SVIs and AI-assisted SVIs. The study involved 144 pairs of interviewees and interviewers/assessors. To assess applicants' deceptive IM behaviors, the authors employed a combination of interviewee self-reports and interviewer perceptions.

The results indicate that AVIs elicited fewer instances of deceptive IM behaviors across all dimensions when compared to SVIs. Furthermore, using AI-assisted assessment in both video interview modes resulted in less extensive image creation than non-AI settings. However, the study revealed that human interviewers had difficulties detecting deceptive IM behaviors regardless of the mode used, except for extensive faking in AVIs.

The study is the first to address the call for research on the impact of video interview modes and AI on interviewee faking and interviewer accuracy. This research enhances the authors’ understanding of the practical implications associated with the use of different video interview modes and AI algorithms in the pre-employment screening process. The study contributes to the existing literature by refining the theoretical model of faking likelihood in employment interviews according to media richness theory and the model of volitional rating behavior based on expectancy theory in the context of AVIs and AI-assisted assessment.

While LIS scholarship emphasizes the need to be multi-literate by equipping people with critical information literacy, digital literacy, and media literacy skills to combat the phenomenon of fake news in the contemporary information society, the concept of political information literacy is still in its infancy. This chapter addresses this gap by developing an understanding of political information literacy and challenges the premise that information professionals and information organizations should remain neutral in the face of phenomena like censorship through noise and disinformation. In this endeavor, it reviews contemporary information environments vis-à-vis the growth of fake news and misinformation, and current information literacy approaches utilized by information organizations. Thereafter, it explores several cognitive barriers, such as the role of confirmation bias, information avoidance, information groupishness, and information overload, which affects people’s ability to process information. Finally, it encourages information professionals to hold regular information sessions on politically charged topics, tackle the cognitive factors increasing misinformation, and cultivate multidisciplinary approaches to confront fake news.

Purveyors of fake news perpetuate information that can harm society, including businesses. Social media's reach quickly amplifies distortions of fake news. Research has not yet fully explored the mechanisms of such adversarial behavior or the adversarial techniques of machine learning that might be deployed to detect fake news. Debiasing techniques are also explored to combat against the generation of fake news using adversarial data. The purpose of this paper is to present the challenges and opportunities in fake news detection.

First, this paper provides an overview of adversarial behaviors and current machine learning techniques. Next, it describes the use of long short-term memory (LSTM) to identify fake news in a corpus of articles. Finally, it presents the novel adversarial behavior approach to protect targeted business datasets from attacks.

This research highlights the need for a corpus of fake news that can be used to evaluate classification methods. Adversarial debiasing using IBM's Artificial Intelligence Fairness 360 (AIF360) toolkit can improve the disparate impact of unfavorable characteristics of a dataset. Debiasing also demonstrates significant potential to reduce fake news generation based on the inherent bias in the data. These findings provide avenues for further research on adversarial collaboration and robust information systems.

Adversarial debiasing of datasets demonstrates that by reducing bias related to protected attributes, such as sex, race and age, businesses can reduce the potential of exploitation to generate fake news through adversarial data.

The insidious proliferation of online misinformation represents a significant societal problem. With a wealth of research dedicated to the topic, it is still unclear what determines fake news sharing. This paper comparatively examines fake and accurate news sharing in a novel experimental setting that manipulates news about terrorism.

The authors follow an extended version of the uses-and-gratification framework for news sharing, complemented by variables commonly employed in fake news rebuttal studies.

Logistic regression and classification trees revealed worry about the topic, media literacy, information-seeking and conservatism as significant predictors of willingness to share news online. No significant association was found for general analytical thinking, journalism skepticism, conspiracy ideation, uses-and-gratification motives or pass-time coping strategies.

The current results broaden and expand the literature examining beliefs in and sharing of misinformation, highlighting the role of media literacy in protecting the public against the spread of fake news.

This is, to the authors’ knowledge, the first study to integrate a breadth of theoretically and empirically driven predictors of fake news sharing within a single experimental framework.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-12-2022-0693