Saudi Cultural Missions Theses & Dissertations
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Item Restricted Bush and Blair’s decision to invade Iraq(King's College London, 2023-12-01) Alqarni, Khalid; SefaNyarko, ClementThe decision made by Bush and Blair to invade Iraq was one of the most questionable decisions in the global foreign affairs in the last 50 years due to the lengthy war that occurred after the invasion which resulted in the insecurity that came in Iraq and the region. Such a major decision by two top leaders in the global foreign affairs needs to be studied based on leadership studies. Hence, this research will adapt the process-based leadership analytical framework in order to analyse the whole invasion starting from the history that led to the decision, and how both leaders emerged to make such a decision. This is to reach the main aim of this research which is to measure the effectiveness of Bush and Blair decision to go to war in Iraq. The analysis of the decision ended in showing how ineffective was both Bush and Blair decision on the U.S., UK and Iraq because it led to a deadly civil war, death of Americans and British soldiers, and large civilian death toll. furthermore, the decision led to long lasting consequences which impacted the following governments of Bush and Blair such as sectarian war in Iraq, economical effect on the U.S, UK, emerge of ISIS. The current studies of leadership did not take this major event to account as a situation, but rather it was examined based on the leaders themselves. Thus, this research adapted the process-based leadership approach because events in regards of conflict and peace needs to be studied through the situation not the leaders, because it will help to measure the effectiveness and detect any new emerging situation that needs a new leader to emerge. However, studying a case through the leader will end in judging the leader of being good or bad, and this is not helpful in reaching a solution if the process was ineffective.19 0Item Restricted How Does Bias Affect Users of Artificial Intelligence Systems?(2023) Bubakr, Hebah Abdullah; Baber,ChrisIn large companies, artificial intelligence (AI) is being used to optimise workflow and ensure efficiency. An assumption is that the AI system should remain unaffected by bias or prejudices to contribute to providing fairer results. For example, in the recruitment process, AI ensures that each applicant is judged by the exact criteria in the job description. Our results suggest otherwise; therefore, we wondered whether the problem of bias extends from the training data (which, replicates existing inequalities in organisations) to the design of the AI systems themselves. These learning systems are dependent on knowledge elicited from human experts. However, if the systems are trained to perform and think in the same way as a human, most of the tools would use unacceptable criteria because people consider many personal parameters that a machine should not use. The question remains whether the potential impact of bias is considered in the design of an AI system. In this thesis, several experiments are conducted to study unconscious bias in the application of AI with the aid of two qualitative frameworks and two quantitative questionnaires. We first explore the unconscious bias in user interface designs, then examine programmers’ understanding of bias when creating a purposely biased machine using medical databases. A third study addresses the effect of AI recommendations on decision-making, and finally, we explore whether user acceptance is dependent on the type of AI recommendation, testing various suggestions. This project raises awareness of how the developers of AI and machine learning might have a narrow perspective of ‘bias’ as a statistical problem rather than a social or ethical problem. This limitation is not because they are unaware of these wider concerns but because the requirements relating to the management of data and the implementation of algorithms might restrict their focus to technical challenges. Consequently, bias outcomes can be produced unconsciously because developers are simply not attending to these broader concerns. Creating accurate and effective models is important but so is ensuring that all races, ethnicities and socioeconomic levels are adequately represented in the data model (O’Neil, 2016).24 0