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1. Examining the Relationship between Personality Characteristics and Worker’s Attention under Fall and Tripping Hazard Conditions

   https://doi.org/10.1061/9780784481288.040  

   Hasanzadeh, Sogand; Esmaeili, Behzad; Dodd, Michael D. (March 2018, Construction Research Congress 2018 April 2–4, 2018 | New Orleans, Louisiana)

   A worker’s attentional and cognitive failures—such as lack of attention, failure to identify a tripping hazard, or misperception about a hazard’s risks—can lead to unsafe behaviors and, consequently, accidents. Previous literature has shown that individual characteristics such as personality may affect human’s selective attention. However, few studies have attempted to empirically examine how a worker’s personality affects attention and situation awareness on a jobsite. The present study examines how workers’ emotional stability (neuroticism) affects their cognitive failures (especially attentional failure) when they are exposed to fall-to-same-level hazardous conditions. To achieve this goal—and given that eye movements represent the most direct manifestation of visual attention—the personalities of construction workers were assessed via self-completion questionnaires, and their attention and situation awareness were monitored continuously and in real-time using a mobile wearable eye-tracking apparatus. Correlational analyses revealed the significant relationship between neuroticism and the attentional distribution of workers. These results suggest that workers do not allocate their attention equally to all hazardous areas and these differences in attentional distribution are modulated by personality characteristics (neuroticism). A more detailed investigation of this connection yielded a specific pattern: less neurotic workers periodically look down and scan ahead to obtain feedforward information about tripping hazards, and these individuals remain fully aware of the environment and its associated hazards. The findings of this study suggest the value assessing personality to identify workers who are more likely to be involved in accidents. 

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2. Using Worker Characteristics, Personality, and Attentional Distribution to Predict Hazard Identification Performance: A Moderated Mediation Analysis

   https://doi.org/10.1061/(ASCE)CO.1943-7862.0002295  

   Aroke, Olugbemi; Hasanzadeh, Sogand; Esmaeili, Behzad; Dodd, Michael D.; Brock, Rebecca (June 2022, Journal of Construction Engineering and Management)
3. Computational personality assessment

   https://doi.org/10.5964/ps.6115  

   Stachl, Clemens; Boyd, Ryan L.; Horstmann, Kai T.; Khambatta, Poruz; Matz, Sandra C.; Harari, Gabriella M. (January 2021, Personality Science)

   null (Ed.)

   Relevance Statement Computational personality assessment based on digital footprints and high frequent behavioral data from in-vivo sensing studies could drastically change the concept of personality and its assessment. 

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4. Personality beyond taxonomy

   https://doi.org/10.1038/s41562-020-00989-3  

   Dubois, Julien; Eberhardt, Frederick; Paul, Lynn K.; Adolphs, Ralph (November 2020, Nature Human Behaviour)

   null (Ed.)
5. Personality of AI

   Yu, Byunggu; Kim, Junwhan (June 2024, Springer LNCS)

   This research paper delves into the evolving landscape of fine-tuning large language models (LLMs) to align with human users, extending beyond basic alignment to propose "personality alignment" for language models in organizational settings. Acknowledging the impact of training methods on the formation of undefined personality traits in AI models, the study draws parallels with human fitting processes using personality tests. Through an original case study, we demonstrate the necessity of personality fine-tuning for AIs and raise intriguing questions about applying human-designed tests to AIs, engineering specialized AI personality tests, and shaping AI personalities to suit organizational roles. The paper serves as a starting point for discussions and developments in the burgeoning field of AI personality alignment, offering a foundational anchor for future exploration in human-machine teaming and co-existence. 

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