Talent management practices for the future of work: how can artificial intelligence reconcile recruitment tensions in organizations

Talent Management Practices for the Future of Work: How Can Artificial Intelligence Reconcile Recruitment Tensions in Organizations? Over the years, the academic field has been coming up with new studies, frameworks, and definitions of what work is and, consecutively, what its future is, taking into account all the technological evolution and disruptors, such as the case of Covid-19. In this way, it is necessary to follow what are the new trends in the market and be able to adapt. This project is then based on an in-depth analysis of the Company XYZ to understand its readiness for the Future of Work within Talent Management, focusing more concretely on the role of Artificial Intelligence in Recruitment

A Conceptual Artificial Intelligence Application Framework in Human Resource Management

This study proposes a conceptional framework of artificial intelligence (AI) technology application for human resource management (HRM). Based on the theory of the six basic dimensions of human resource management, which includes human resource strategy and planning, recruitment, training and development process, performance management, salary evaluation, and the employee relationship management, is combine with its potential corresponding AI technology application. With the cases analysis on recruitment of leap.ai and online training of Baidu, the recruitment dimension and training dimension with AI are further explored. Finally, the practical implication and future study are supplemented. This AIHRM conceptual model provides suggestions and directions for the development of AI in enterprise human resource management

Mitigating Gender Bias in Machine Learning Data Sets

Artificial Intelligence has the capacity to amplify and perpetuate societal biases and presents profound ethical implications for society. Gender bias has been identified in the context of employment advertising and recruitment tools, due to their reliance on underlying language processing and recommendation algorithms. Attempts to address such issues have involved testing learned associations, integrating concepts of fairness to machine learning and performing more rigorous analysis of training data. Mitigating bias when algorithms are trained on textual data is particularly challenging given the complex way gender ideology is embedded in language. This paper proposes a framework for the identification of gender bias in training data for machine learning.The work draws upon gender theory and sociolinguistics to systematically indicate levels of bias in textual training data and associated neural word embedding models, thus highlighting pathways for both removing bias from training data and critically assessing its impact.Comment: 10 pages, 5 figures, 5 Tables, Presented as Bias2020 workshop (as part of the ECIR Conference) - http://bias.disim.univaq.i

Transformation of the system for forming qualified personnel in the digital economy

At the moment, there is an active digital transformation of the practice and processes in HR management. The use of electronic means, artificial intelligence and information technology can significantly increase the efficiency of HR management. This study presents the concept and various aspects of the use of artificial intelligence in HR management. The purpose of the study is to identify the key aspects of the highly qualified stuff formation in the digital economy. The research method is a comparative and causal analysis of management decisions and steps to prepare and implement the digital HR transformation. As a result of the study, a model of the recruitment process based on artificial intelligence and a map of possible risks were obtained. The results of the study can be useful in the implementation of HR management based on artificial intelligence and information technology

Search-Based Fairness Testing: An Overview

Artificial Intelligence (AI) has demonstrated remarkable capabilities in domains such as recruitment, finance, healthcare, and the judiciary. However, biases in AI systems raise ethical and societal concerns, emphasizing the need for effective fairness testing methods. This paper reviews current research on fairness testing, particularly its application through search-based testing. Our analysis highlights progress and identifies areas of improvement in addressing AI systems biases. Future research should focus on leveraging established search-based testing methodologies for fairness testing.Comment: IEEE International Conference on Computing (ICOCO 2023), Langkawi Island, Malaysia, pp. 89-94, October 202

Intelligent recruitment: how to identify, select, and retain talents from around the world using artificial intelligence.

This research analyzes how digital technologies contribute to improving the successive stages of the recruitment process: identifying, selecting, and retaining talented people. E-recruitment is an emerging and polymorphous phenomenon that starts with identification of candidates on social networks, continues through gamification of recruitment and job interviews with chatbots, and ends by matching a candidate and a job using artificial intelligence. These technologies are particularly useful for social businesses looking to recruit not only skilled people, but above all employees who have behaviors and values that match their mission. The methodology is based on grounded theory, participant observation, and qualitative data collection. A multiple case study is designed to analyze, compare, and combine several technologies dedicated to recruitment: (1) a social network with LinkedIn, (2) a MOOC with Udacity, (3) a serious game called Reveal from L'Oréal, (4) a chatbot called Ari from TextRecruit, and (5) a massive data analysis matching system with Randstad.tech. The discussion examines the respective performance and limits of these tools and their convergence via a progressive integration that leads to an uberization of recruitment. Managerial recommendations are formulated to support recruiters in their adoption of e-recruitment

Cooperation of Nature and Physiologically Inspired Mechanism in Visualisation

A novel approach of integrating two swarm intelligence algorithms is considered, one simulating the behaviour of birds flocking (Particle Swarm Optimisation) and the other one (Stochastic Diffusion Search) mimics the recruitment behaviour of one species of ants – Leptothorax acervorum. This hybrid algorithm is assisted by a biological mechanism inspired by the behaviour of blood flow and cells in blood vessels, where the concept of high and low blood pressure is utilised. The performance of the nature-inspired algorithms and the biologically inspired mechanisms in the hybrid algorithm is reflected through a cooperative attempt to make a drawing on the canvas. The scientific value of the marriage between the two swarm intelligence algorithms is currently being investigated thoroughly on many benchmarks and the results reported suggest a promising prospect (al-Rifaie, Bishop & Blackwell, 2011). We also discuss whether or not the ‘art works’ generated by nature and biologically inspired algorithms can possibly be considered as ‘computationally creative’

Creativity and Autonomy in Swarm Intelligence Systems

This work introduces two swarm intelligence algorithms -- one mimicking the behaviour of one species of ants (\emph{Leptothorax acervorum}) foraging (a `Stochastic Diffusion Search', SDS) and the other algorithm mimicking the behaviour of birds flocking (a `Particle Swarm Optimiser', PSO) -- and outlines a novel integration strategy exploiting the local search properties of the PSO with global SDS behaviour. The resulting hybrid algorithm is used to sketch novel drawings of an input image, exploliting an artistic tension between the local behaviour of the `birds flocking' - as they seek to follow the input sketch - and the global behaviour of the `ants foraging' - as they seek to encourage the flock to explore novel regions of the canvas. The paper concludes by exploring the putative `creativity' of this hybrid swarm system in the philosophical light of the `rhizome' and Deleuze's well known `Orchid and Wasp' metaphor