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    Developing unbiased artificial intelligence in recruitment and selection : a processual framework : a dissertation presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Management at Massey University, Albany, Auckland, New Zealand
    (Massey University, 2022) Soleimani, Melika
    For several generations, scientists have attempted to build enhanced intelligence into computer systems. Recently, progress in developing and implementing Artificial Intelligence (AI) has quickened. AI is now attracting the attention of business and government leaders as a potential way to optimise decisions and performance across all management levels from operational to strategic. One of the business areas where AI is being used widely is the Recruitment and Selection (R&S) process. However, in spite of this tremendous growth in interest in AI, there is a serious lack of understanding of the potential impact of AI on human life, society and culture. One of the most significant issues is the danger of biases being built into the gathering and analysis of data and subsequent decision-making. Cognitive biases occur in algorithmic models by reflecting the implicit values of the humans involved in defining, coding, collecting, selecting or using data to train the algorithm. The biases can then be self-reinforcing using machine learning, causing AI to engage in ‘biased’ decisions. In order to use AI systems to guide managers in making effective decisions, unbiased AI is required. This study adopted an exploratory and qualitative research design to explore potential biases in the R&S process and how cognitive biases can be mitigated in the development of AI-Recruitment Systems (AIRS). The classic grounded theory was used to guide the study design, data gathering and analysis. Thirty-nine HR managers and AI developers globally were interviewed. The findings empirically represent the development process of AIRS, as well as technical and non-technical techniques in each stage of the process to mitigate cognitive biases. The study contributes to the theory of information system design by explaining the phase of retraining that correlates with continuous mutability in developing AI. AI is developed through retraining the machine learning models as part of the development process, which shows the mutability of the system. The learning process over many training cycles improves the algorithms’ accuracy. This study also extends the knowledge sharing concepts by highlighting the importance of HR managers’ and AI developers’ cross-functional knowledge sharing to mitigate cognitive biases in developing AIRS. Knowledge sharing in developing AIRS can occur in understanding the essential criteria for each job position, preparing datasets for training ML models, testing ML models, and giving feedback, retraining, and improving ML models. Finally, this study contributes to our understanding of the concept of AI transparency by identifying two known cognitive biases  similar-to-me bias and stereotype bias  in the R&S process that assist in assessing the ML model outcome. In addition, the AIRS process model provides a good understanding of data collection, data preparation and training and retraining the ML model and indicates the role of HR managers and AI developers to mitigate biases and their accountability for AIRS decisions. The development process of unbiased AIRS offers significant implications for the human resource field as well as other fields/industries where AI is used today, such as the education system and insurance services, to mitigate cognitive biases in the development process of AI. In addition, this study provides information about the limitations of AI systems and educates human decision makers (i.e. HR managers) to avoid building biases into their systems in the first place.
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    Organisational decision-making processes behind incorporating autonomous task-performing technology and its impact on the future of work : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Management at Massey University, Manawatu, New Zealand
    (Massey University, 2022) Mrowinski, Benjamin Sirius
    In recent years there have been mounting discussions among scholars, business people, governments, and scientists to understand the impact of emerging technologies such as automation and AI and their subsequent impact on the future of work. The concern for the future of work has primarily circulated around predictions estimating that up to half of the workforce, if not more, could be impacted by these technologies by the year 2030. Despite these predictions, there remains a need to understand the impact of emerging technologies on the future of work from the perspective of organisations, which has largely been omitted from previous research into this phenomenon. One of the prominent limitations throughout the literature circulates around the assumption that organisations will adopt these technologies. In light of this, there remains unanswered questions pertaining to the extent in which organisations will use emerging technology and whether adopting this technology inherently leads to job loss. It is vital to develop insights into what drives organisational decision-making processes to adopt these technologies to understand better the relationship between organisations, employees, and emerging technologies. When it comes to understanding the future of work, there remains a distinct difference between the impact on a job task versus an entire job. To address this, Autonomous Task Performing Technology (ATPT) has been adopted throughout this paper to reflect on how emerging technologies can impact employees to different extents. The present study was designed to understand the organisational decision-making process behind adopting ATPT and the subsequent impact on the future of work. Two primary participant groups were identified using purposive sampling with the snowball approach to address this research, which includes a total of 34 top managers and 10 union representatives. The 34 top manager participants are made up of 17 top managers from the public sector and 17 top managers from the private sector representing twenty-two different industries/ line of work across telecommunications, agriculture, finance, healthcare, business, education, transportation, technology, architecture, energy, technical services, engineering, retail, produce, manufacturing, finance, social services, marketing, research, legal, environment, and emergency services. The data with top managers were collected using in-depth semi-structured interviews with an average interview time of just over 1 hour which translated to over 34 hours in total of interview recordings. The 10 union representatives consisted of four participants from the public sector and six from the private sector covering six different industries spanning across finance, education, business, retail, transportation, and healthcare. Data collected with union representatives utilised the critical incident technique to understand the impact of ATPT on employees from incidents where organisations adopted ATPT with an average interview time of over 25 minutes. Although the design of this research was to understand the organisational decision-making processes behind adopting ATPT, it remained critical to understand this phenomenon from both perspectives of top managers and unions. Triangulation was used to compare and analyse the data using thematic analysis with the Framework method between top managers and union representatives. This approach provided valuable insight into how organisations adopt ATPT and how the impact is experienced by employees. The findings from this research place distinct emphasise on how ATPT does not inherently predetermine job loss. Rather, the findings capture the highly variable nature of organisational adoption of ATPT and the subsequent impact on the future of work through the development of the ATPT impact Framework. The ATPT impact Framework was conceptualised through the underpinning of three core themes in this research: organisational drivers behind ATPT adoption, scenarios of ATPT adoption, and the outcome of organisational adoption of ATPT on employees/ and the future of work. Ultimately, the future of work is not determined by the capabilities of ATPT, but rather by the ATPT impact Framework and the ethical responsibility of organisations to use ATPT responsibly. The impact of ATPT on the future of work does not fall on the shoulders of organisations alone, but rather requires an ongoing collaboration and open dialogue between organisations, government, policy makers, scholars, employees and unions to establish a form of good practice and ethical responsibility behind adopting ATPT as society continues to navigate through the challenges of how to effectively use ATPT.