Browsing by Author "Caird-Daley, Antoinette"

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    Automating human skills : preliminary development of a human factors methodology to capture tacit cognitive skills
    (Cranfield University Press, 2013-09-19) Caird-Daley, Antoinette; Fletcher, Sarah R.; Baker, William
    Despite technological advances in intelligent automation, it remains difficult for engineers to discern which manual tasks, or task components, would be most suitable for transfer to automated alternatives. This research aimed to develop an accurate methodology for the measurement of both observable and unobservable physical and cognitive activities used in manual tasks for the capture of tacit skill. Experienced operators were observed and interviewed in detail, following which, hierarchical task analysis and task decomposition methods were used to systematically explore and classify the qualitative data. Results showed that a task analysis / decomposition methodology identified different types of skill (e.g. procedural or declarative) and knowledge (explicit or tacit) indicating this methodology could be used for further human skill capture studies. The benefit of this research will be to provide a methodology to capture human skill so that complex manual tasks can be more efficiently transferred into automated processes.
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    Task analysis of discrete and continuous skills: a dual methodology approach to human skills capture for automation
    (Taylor and Francis, 2015-04-11) Everitt, Jamie; Fletcher, Sarah R.; Caird-Daley, Antoinette
    There is a growing requirement within the field of intelligent automation for a formal methodology to capture and classify explicit and tacit skills deployed by operators during complex task performance. This paper describes the development of a dual methodology approach which recognises the inherent differences between continuous tasks and discrete tasks and which proposes separate methodologies for each. Both methodologies emphasise capturing operators’ physical, perceptual, and cognitive skills, however, they fundamentally differ in their approach. The continuous task analysis recognises the non-arbitrary nature of operation ordering and that identifying suitable cues for subtask is a vital component of the skill. Discrete task analysis is a more traditional, chronologically ordered methodology and is intended to increase the resolution of skill classification and be practical for assessing complex tasks involving multiple unique subtasks through the use of taxonomy of generic actions for physical, perceptual, and cognitive actions.
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    Unmasking deepfakes: a multidisciplinary examination of social impacts and regulatory responses
    (Springer, 2025-12-31) Alanazi, Sami; Asif, Seemal; Caird-Daley, Antoinette; Moulitsas, Irene
    This paper presents a comprehensive analysis of deepfake technology and its multifaceted impacts on society, privacy, trust, and information integrity. Deepfakes, synthetic media generated using AI-powered algorithms, pose significant challenges to individual privacy, societal trust, and the integrity of information. To explore these issues, we employed a mixed-methods approach that included in-depth expert interviews with professionals from diverse fields such as law, ethics, artificial intelligence, cybersecurity, and social sciences, along with a dichotomous question survey, which provided comprehensive insights from multiple perspectives. This methodological approach facilitated a multidimensional perspective on the potential risks and benefits of deepfakes. Our findings reveal unanimous concern among experts regarding the profound societal implications of deepfakes, particularly their capacity to amplify disinformation, erode public trust, and inflict psychological harm on individuals. Key themes identified include the urgent need for robust regulatory frameworks, the critical role of media literacy in enhancing public resilience, and the varying impacts of deepfakes across different demographic groups. The consensus among experts points out the necessity for an ethically guided approach to the development and deployment of deepfake technology, emphasizing the importance of interdisciplinary collaboration in crafting effective policy responses. This study advances the ongoing discourse on deepfake technology by providing stakeholders and policymakers with evidence-based recommendations aimed at mitigating the associated risks and harnessing potential benefits. These recommendations promote a balanced and informed approach to navigating the complexities of this emerging technological challenge.