Browsing by Subject "automation"

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    Open Access
    Examining personality assessment in asynchronous video interviews (AVI): convergence between human personality judgements and AI/ML scoring
    (2025) Cronje, Jacobus Fouche; de Kock, Francois
    The assessment of personality is an essential component of personnel selection due to its validity in predicting job performance. To assess personality, asynchronous video interviews (AVIs) scored using artificial intelligence (AI) algorithms are increasingly used, allowing candidates to record responses to interview prompts that are subsequently evaluated automatically by AI algorithms and/or human raters. As questions remain about the validity of AI-based AVI scoring approaches, this study examines the convergence between human-and AI-scored personality assessments. To measure personality, the study focuses on the HEXACO model, which measures Honesty-Humility, Emotionality, Extraversion, Agreeableness, Conscientiousness, and Openness to Experience. Verbal responses were transcribed from videotaped AVIs of 161 mock interview candidates who answered five AVI questions. Responses were scored by 15 trained human raters and a closed-dictionary text-analysis keyword-counting AI algorithm developed for this study, respectively. The correlation between trait-level scores produced by human judges and AI scoring was tested both across traits and within traits (trait-level) to assess scoring convergence. Moreover, in addition to comparing score levels produced by the two scoring methods (AI vs. human raters), score spread (i.e., variability), rank-order stability, and rating reliability were evaluated. The findings revealed a moderately positive and significant overall convergence (r = .29, p < .001) across traits between human and AI evaluations, which suggests that AI scoring may potentially be useful as a replacement of human evaluations when general screening is desired. Trait-level convergence varied between scoring methods, with the scoring consensus between human raters and AI being higher for some traits than for others, suggesting that these methods rely on different information and/or may interpret interview responses differently. The research highlights the potential of AI to complement human- based scoring of AVIs used in recruitment, selection, and assessment while also identifying the limitations of algorithm-based scoring in capturing complex human behaviour in interviews. The findings may further contribute to understanding the role of AI in personality assessment and implications for organisational practices.
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    Open Access
    The impact of air traffic management automation on the human performance of air traffic controllers in aviation law
    (2025) Hendrikse, Cindy; Salazar, PH-J
    Advancements in communication, navigation, surveillance and air traffic management (CNS/ATM) systems directly impact air traffic controllers (ATCs), who must interact with these technologies within a regulated framework. The hypothesis is that aligning these advancements with their governing international, regional, and national legislation and operational procedures with ATCs in mind will significantly enhance ATCs' performance and trust in these advancements and increase operational safety in a progressively technology-driven environment. Therefore, the research investigated the impact of CNS/ATM advancements on ATCs, the extent to which international, regional and national legislation consider ATCs, and whether the legislation can effectively address the rapid development and growing consequences caused by automation, including artificial intelligence. The study employed a multidisciplinary approach, incorporating a review of human factors research, an analysis of relevant international and European Union aviation law and initiatives, and a comparison of national policies and legislation of the United Kingdom, the Netherlands, and South Africa. Lastly, it included a qualitative survey directed at ATCs to draw from their operational expertise. The human factors literature review highlighted the growing implications of automation, including issues such as complacency, overreliance, distrust in automation, and diminishing manual skills. The legislative analyses unveiled various shortcomings at each level, while the survey revealed that ATCs follow operational procedures regardless of accuracy. Additionally, the survey showed that automation failures significantly increase ATC workload. Lastly, although no participant could indicate how artificial intelligence is currently employed in ATM, most do not trust it nor believe it would be able to control air traffic without any ATC input.