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

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

Cognitive biases in developing biased Artificial Intelligence recruitment system

Artificial Intelligence (AI) in a business context is designed to provide organizations with valuable insight into decision-making and planning. Although AI can help managers make decisions, it may pose unprecedented issues, such as datasets and implicit biases built into algorithms. To assist managers with making unbiased effective decisions, AI needs to be unbiased too. Therefore, it is important to identify biases that may arise in the design and use of AI. One of the areas where AI is increasingly used is the Human Resources recruitment process. This article reports on the preliminary findings of an empirical study answering the question: how do cognitive biases arise in AI? We propose a model to determine people’s role in developing AI recruitment systems. Identifying the sources of cognitive biases can provide insight into how to develop unbiased AI. The academic and practical implications of the study are discussed

Analysing the Integration of Models of Technology Diffusion and Acceptance in Nigerian Higher Education

The use of technology in learning environments has produced a series of different theories and models about how technology is adopted, accepted and used. This paper attempts to show the relevance of combining the diffusion of innovation model (DIM) and a context-specific model of technology acceptance (TAM) to understanding the acceptance or rejection of educational technologies in Nigerian universities. Using empirical evidence, the analysis attempts to determine the extent to which the adoption, acceptance, and use of educational tools support or contradicts the components of the two models, emphasising how a range of technological, pedagogical, institutional, socio-cultural, and design-related factors informed, facilitated, and discouraged the diffusion, adoption, acceptance and use of blended eLearning systems in three Nigerian universities. The analysis suggests the ‘relevance’ and ‘limit’ of the determining components and identifiers of both models, arguing instead for a critical examination of the relationship between different models as to understanding the factors that might lead to the acceptance or rejection of technological innovation

Monitoring and debugging using an SDK for NFV-powered telecom applications

C

Expert system verification and validation study. Phase 2: Requirements identification. Delivery 1: Updated survey report

The purpose is to report the state-of-the-practice in Verification and Validation (V and V) of Expert Systems (ESs) on current NASA and Industry applications. This is the first task of a series which has the ultimate purpose of ensuring that adequate ES V and V tools and techniques are available for Space Station Knowledge Based Systems development. The strategy for determining the state-of-the-practice is to check how well each of the known ES V and V issues are being addressed and to what extent they have impacted the development of Expert Systems

Management implications of moving from a traditional structured systems development methodology to object-orientation

Thesis (M.S.) University of Alaska Fairbanks, 2003As software application systems become larger and more complex, many software employers and managers believe that the key to sustaining its competitive advantage in the computing technology market lies in its software engineering capabilities. Software crisis situation seems to be a common occurrence in the software development environment as systems become larger and more complex. Object Orientation (OO) has been proposed as a viable alternative to traditional approach (i.e., structured techniques), an approach that many hope will solve the current software crisis. 00 is a new paradigm, and it requires new types of knowledge, new specialists, and significant changes in the mindset, an entirely different way of thinking, representing and solving a problem. The transition of moving toward the 00 from the traditional approach may involve a high risk of failure if the managers do not understand the nature of paradigm shifts and do not anticipate the future. The problem of moving to 00 has become very important. An understanding of potential problems from migrating to the new paradigm helps managers make a smoother paradigm shift. The implications and challenges of the 00 paradigm are presented. The study suggests that Object-Oriented System Development (OOSD) requires more discipline, management and training than traditional software development does. Education and experience are keys for the success of any OOSD project

Electronic information sharing in local government authorities: Factors influencing the decision-making process

This is the post-print version of the final paper published in International Journal of Information Management. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2013 Elsevier B.V.Local Government Authorities (LGAs) are mainly characterised as information-intensive organisations. To satisfy their information requirements, effective information sharing within and among LGAs is necessary. Nevertheless, the dilemma of Inter-Organisational Information Sharing (IOIS) has been regarded as an inevitable issue for the public sector. Despite a decade of active research and practice, the field lacks a comprehensive framework to examine the factors influencing Electronic Information Sharing (EIS) among LGAs. The research presented in this paper contributes towards resolving this problem by developing a conceptual framework of factors influencing EIS in Government-to-Government (G2G) collaboration. By presenting this model, we attempt to clarify that EIS in LGAs is affected by a combination of environmental, organisational, business process, and technological factors and that it should not be scrutinised merely from a technical perspective. To validate the conceptual rationale, multiple case study based research strategy was selected. From an analysis of the empirical data from two case organisations, this paper exemplifies the importance (i.e. prioritisation) of these factors in influencing EIS by utilising the Analytical Hierarchy Process (AHP) technique. The intent herein is to offer LGA decision-makers with a systematic decision-making process in realising the importance (i.e. from most important to least important) of EIS influential factors. This systematic process will also assist LGA decision-makers in better interpreting EIS and its underlying problems. The research reported herein should be of interest to both academics and practitioners who are involved in IOIS, in general, and collaborative e-Government, in particular