Large language models (LLMs) are now being considered and even deployed for applications that support high-stakes decision-making, such as recruitment and clinical decisions. While several methods have been proposed for measuring bias, there remains a gap between predictions, which are what the proposed methods consider, and how they are used to make decisions. In this work, we introduce Rank-Allocational-Based Bias Index (RABBI), a model-agnostic bias measure that assesses potential allocational harms arising from biases in LLM predictions. We compare RABBI and current bias metrics on two allocation decision tasks. We evaluate their predictive validity across ten LLMs and utility for model selection. Our results reveal that commonly-used bias metrics based on average performance gap and distribution distance fail to reliably capture group disparities in allocation outcomes, whereas RABBI exhibits a strong correlation with allocation disparities. Our work highlights the need to account for how models are used in contexts with limited resource constraints.

The current study proposes an innovative methodology for the profiling of psychological traits of Operations Management (OM) and Supply Chain Management (SCM) professionals. We use innovative methods and tools of text mining and social network analysis to map the demand for relevant skills from a set of job descriptions, with a focus on psychological characteristics. The proposed approach aims to evaluate the market demand for specific traits by combining relevant psychological constructs, text mining techniques, and an innovative measure, namely, the Semantic Brand Score. We apply the proposed methodology to a dataset of job descriptions for OM and SCM professionals, with the objective of providing a mapping of their relevant required skills, including psychological characteristics. In addition, the analysis is then detailed by considering the region of the organization that issues the job description, its organizational size, and the seniority level of the open position in order to understand their nuances. Finally, topic modeling is used to examine key components and their relative significance in job descriptions. By employing a novel methodology and considering contextual factors, we provide an innovative understanding of the attitudinal traits that differentiate professionals. This research contributes to talent management, recruitment practices, and professional development initiatives, since it provides new figures and perspectives to improve the effectiveness and success of Operations Management and Supply Chain Management professionals.

Big data has become more important than ever in the realm of cybersecurity. You are going to have to know more about AI, data analytics and other big data tools if you want to be a cybersecurity professional. As far as computer and information technology occupations go, security awareness training is a key starting point for anyone interested in the bright future that this sector offers. The need for cybersecurity personnel, technicians, officers, developers, and trainers have never been greater. As the need for these professions grows, it also becomes more important for them to have a background in big data and other forms of technology.

Do you want to get a job at Google? If the answer is yes, these are the most important skills that will help you get an engineering job at Google, and also I will help you with how to gain these important skills. Many of us that have some sort of engineering background have a dream wish to work in a company like Google, which has a huge impact on our lives and will have a huge impact on our future. Google LLC is an American multinational technology company that specializes in Internet-related services and products, which include online advertising technologies, a search engine, cloud computing, software, and hardware. Being one of the biggest companies in the world, the skills requirements for a job position are quite a lot and require you to have at least a bachelor's degree in engineering.

Machine learning is the subset of Artificial Intelligence or AI that has become the newest applied technology in most of the industries. With its ability to optimize and automate the hiring process, all sectors are welcoming it with open hands, including the recruitment sector. In fact, for the recruitment sector, machine learning technology has come out as a miraculous solution. The AI-powered tools and methodologies have made it easier to target the right people and find the best candidates for an opening. Additionally, machine learning methodologies can help screen hundreds and thousands of resumes in just a few minutes. But is machine learning only lucrative or does it have some negatives too? In this post, we will dig deeper into the functionalities and mechanics of machine learning in recruitment.

In this paper we present the Warm-starting Dynamic Thresholding algorithm, developed using dynamic programming, for a variant of the standard online selection problem. The problem allows job positions to be either free or already occupied at the beginning of the process. Throughout the selection process, the decision maker interviews one after the other the new candidates and reveals a quality score for each of them. Based on that information, she can (re)assign each job at most once by taking immediate and irrevocable decisions. We relax the hard requirement of the class of dynamic programming algorithms to perfectly know the distribution from which the scores of candidates are drawn, by presenting extensions for the partial and no-information cases, in which the decision maker can learn the underlying score distribution sequentially while interviewing candidates.

Efficient human resource management needs accurate assessment and representation of available competences as well as effective mapping of required competences for specific jobs and positions. In this regard, appropriate definition and identification of competence gaps express differences between acquired and required competences. Using a detailed quantification scheme together with a mathematical approach is a way to support accurate competence analytics, which can be applied in a wide variety of sectors and fields. This article describes the combined use of software technologies and mathematical and statistical methods for assessing and analyzing competences in human resource information systems. Based on a standard competence model, which is called a Professional, Innovative and Social competence tree, the proposed framework offers flexible tools to experts in real enterprise environments, either for evaluation of employees towards an optimal job assignment and vocational training or for recruitment processes. The system has been tested with real human resource data sets in the frame of the European project called ComProFITS.

Sometimes HR managers and recruiters have a tough time finding the right candidate for a job. Can conversational AI save the day? Recruiting the right talent is one of the most challenging tasks for recruiters. On top of these responsibilities, recruiters have to develop a recruitment strategy that meets business goals, takes into consideration competitor analysis and employee satisfaction rates. In reality, managing this workload effectively is a hard task to do, that's why many companies turned to Artificial Intelligence for extra help. In one of their recent reports, Deloitte covered a use case of a multinational bank struggling to optimize the workload of hundreds of service desk agents, who support HR management processes.

Company profiling is an analytical process to build an in-depth understanding of company's fundamental characteristics. It serves as an effective way to gain vital information of the target company and acquire business intelligence. Traditional approaches for company profiling rely heavily on the availability of rich finance information about the company, such as finance reports and SEC filings, which may not be readily available for many private companies. However, the rapid prevalence of online employment services enables a new paradigm — to obtain the variety of company's information from their employees' online ratings and comments. This, in turn, raises the challenge to develop company profiles from an employee's perspective. To this end, in this paper, we propose a method named Company Profiling based Collaborative Topic Regression (CPCTR), for learning the latent structural patterns of companies. By formulating a joint optimization framework, CPCTR has the ability in collaboratively modeling both textual (e.g., reviews) and numerical information (e.g., salaries and ratings). Indeed, with the identified patterns, including the positive/negative opinions and the latent variable that influences salary, we can effectively carry out opinion analysis and salary prediction. Extensive experiments were conducted on a real-world data set to validate the effectiveness of CPCTR. The results show that our method provides a comprehensive understanding of company characteristics and delivers a more effective prediction of salaries than other baselines.