Fairness in Algorithmic Multi-disciplinary Team Formation

Fairness in team creation is becoming an increasingly important subject of study in computer science and artificial intelligence. As algorithms increasingly automate decision-making processes, ensuring these systems are fair and unbiased has become a key concern. Team formation is one area of study where algorithms are used to match individuals with complementary talents and expertise to establish productive teams. When it comes to the process of forming teams, fairness is an essential component. Based on the relevant research, the thesis proposes the Rule-Based Expert Extraction Method and the Group-project distance and Unfairness Optimization Method to improve fairness during the team-formation process. Additionally, To assess the unfairness, the two proposed approaches are compared with the Pair-round selection method, which was previously examined by Machado and Stefanidis (2019). The fairness improvement is evaluated and compared. Several metrics were taken to assess the refined performance in the team formation process to create a balanced and fair team. The primary goal was to increase fairness when forming multidisciplinary teams. In terms of promoting fairness, the results reveal that the Group-project distance and Unfairness Optimization Method and the Rule-Based Expert Extraction Method perform slightly better than the Pair-round choosing method. The Rule-Based Expert Extraction Method has the most significant Group-project distance, followed by the Group-project distance and Unfairness Optimization Method, and the Pair-rounds choosing method. However, the new approaches have improved fairness and mitigated the increased Group-project distance. Overall, the experimental evaluation demonstrates the potential of the Group-project distance and Unfairness Optimization method and the Rule-Based Expert Extraction method to improve fairness in team formation, which has significant consequences for businesses and organizations that rely on team collaboration

Digitalisation and Transformations of Women’s Labour in Sanitation Work

The waste management sector has attracted the private sector in India. Taking the case study of a start-up in waste sorting and recycling, the essayexamines how technologies used in such spaces affect women's work. It finds that there is a shift in the perceptions of who engages in this work and how thework itself is experienced and seen. But it also cautions against the perpetuation of the gendered division of labour in sanitation work, particularly in roles thatdemand technical (often digital) literacy/competence

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

Implementing the knowledge-based economy: Market devices as policy instruments

We chart the rise and fall of Business Link as a policy instrument for furthering the knowledge-based economy, while also examining how it was implemented by one particular Business Link Operator in Southern England. We zoom in on the specific policy objective to encourage SMEs to adopt e-commerce, which was singled out by the Blair government as a key innovation that marked a competitive "knowledge-driven economy." Drawing on qualitative data and analysis (including policy documents, media reports and interviews with Personal Business Advisers), we undertake a socio-material description of Business Link's enterprise support activities. We found that the implementation of Business Link by successive UK governments required the construction and operation of socio-technical devices to perform a variety of market functions to address a perceived market failure that was thought to impede the rate of SMEs’ adoption of managerial and technological innovations. We show that the effectiveness and efficiency of these market devices depended on their particular design, composition, and mode of deployment, and that after the Labour government’s 2005 reforms broke the original market devices, Business Link actors created new ones to perform those market functions and fulfil the policy objectives, sometimes in contravention of government rules

Team formation using recommendation systems

The importance of team formation has been realized since ages, but finding the most effective team out of the available human resources is a problem that persists to the date. Having members with complementary skills, along with a few must-have behavioral traits, such as trust and collaborativeness among the team members are the key ingredients behind team synergy and performance. This thesis designs and implements two different algorithms for the team formation problem using ideas adapted from the recommender systems literature. One of the proposed solutions uses the Glicko-2 rating system to rate the employees’ skills which can easily separate the skill ability and experience of the employees. The final contribution of this thesis is to build a system with ”plug-in” capability, meaning any new recommendation algorithm could be easily plugged in inside the system. Our extensive experimental analyses explore nuances of data sources, data storage methodologies, as well as characteristics of different recommendation algorithms with rating and ranking sub-systems

A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics

In today's competitive and fast-evolving business environment, it is a critical time for organizations to rethink how to make talent-related decisions in a quantitative manner. Indeed, the recent development of Big Data and Artificial Intelligence (AI) techniques have revolutionized human resource management. The availability of large-scale talent and management-related data provides unparalleled opportunities for business leaders to comprehend organizational behaviors and gain tangible knowledge from a data science perspective, which in turn delivers intelligence for real-time decision-making and effective talent management at work for their organizations. In the last decade, talent analytics has emerged as a promising field in applied data science for human resource management, garnering significant attention from AI communities and inspiring numerous research efforts. To this end, we present an up-to-date and comprehensive survey on AI technologies used for talent analytics in the field of human resource management. Specifically, we first provide the background knowledge of talent analytics and categorize various pertinent data. Subsequently, we offer a comprehensive taxonomy of relevant research efforts, categorized based on three distinct application-driven scenarios: talent management, organization management, and labor market analysis. In conclusion, we summarize the open challenges and potential prospects for future research directions in the domain of AI-driven talent analytics.Comment: 30 pages, 15 figure