Avoiding bias and understanding the real-world consequences of AI-supported decision-making are critical to address fairness and assign accountability. Existing approaches often focus either on technical aspects, such as datasets and models, or on high-level socio-ethical considerations - rarely capturing how these elements interact in practice. In this paper, we apply an information flow-based modeling framework to a real-world recruitment process that integrates automated candidate matching with human decision-making. Through semi-structured stakeholder interviews and iterative modeling, we construct a multi-level representation of the recruitment pipeline, capturing how information is transformed, filtered, and interpreted across both algorithmic and human components. We identify where biases may emerge, how they can propagate through the system, and what downstream impacts they may have on candidates. This case study illustrates how information flow modeling can support structured analysis of fairness risks, providing transparency across complex socio-technical systems.

--This paper introduces an innovative Applicant Tracking System (A TS) enhanced by a novel Robotic process automation (RPA) framework or as further referred to as MLAR. Traditional recruitment processes often encounter bottlenecks in resume screening and candidate shortlisting due to time and resource constraints. MLAR addresses these challenges employing Large Language Models (LLMs) in three distinct layers: extracting key characteristics from job postings in the first layer, parsing applicant resume to identify education, experience, skills in the second layer, and similarity matching in the third layer . These features are then matched through advanced semantic algorithms to identify the best candidates efficiently. Extensive performance benchmarking shows that MLAR outperforms the leading RPA platforms, including UiPath and Automation Anywhere, in high-volume resume-processing tasks. When processing 2,400 resumes, MLAR achieved an average processing time of 5.4 seconds per resume, reducing processing time by approximately 16.9% compared to Automation Anywhere and 17.1% compared to UiPath. These results highlight the potential of MLAR to transform recruitment workflows by providing an efficient, accurate, and scalable solution tailored to modern hiring needs.

The recruitment process is undergoing a significant transformation with the increasing use of machine learning and natural language processing techniques. While previous studies have focused on automating candidate selection, the role of multiple vacancies in this process remains understudied. This paper addresses this gap by proposing a novel pipeline that leverages Large Language Models and graph similarity measures to suggest ideal candidates for specific job openings. Our approach represents candidate profiles as multimodal embeddings, enabling the capture of nuanced relationships between job requirements and candidate attributes. The proposed approach has significant implications for the recruitment industry, enabling companies to streamline their hiring processes and identify top talent more efficiently. Our work contributes to the growing body of research on the application of machine learning in human resources, highlighting the potential of LLMs and graph-based methods in revolutionizing the recruitment landscape.

This paper investigates the application of artificial intelligence (AI) in early-stage recruitment interviews in order to reduce inherent bias, specifically sentiment bias. Traditional interviewers are often subject to several biases, including interviewer bias, social desirability effects, and even confirmation bias. In turn, this leads to non-inclusive hiring practices, and a less diverse workforce. This study further analyzes various AI interventions that are present in the marketplace today such as multimodal platforms and interactive candidate assessment tools in order to gauge the current market usage of AI in early-stage recruitment. However, this paper aims to use a unique AI system that was developed to transcribe and analyze interview dynamics, which emphasize skill and knowledge over emotional sentiments. Results indicate that AI effectively minimizes sentiment-driven biases by 41.2%, suggesting its revolutionizing power in companies' recruitment processes for improved equity and efficiency.

Rep. Jay Obernolte was selected to lead the House task force on AI. Fox News Digital speaks with the California Republican about his goals for the panel and his own thoughts about the rapidly advancing technology. Soon after Italian watchdog Garante took on ChatGPT with a temporary shutdown locally last year, it tried to strengthen its team by hiring four artificial intelligence (AI) experts. But Italy's data protection agency could not recruit the people it wanted, with a dozen candidates dropping out over issues including pay, highlighting a growing challenge facing regulators around the world. "The search process went worse than our low expectations," Garante board member Guido Scorza told Reuters, adding: "We will come up with something else, but so far we have lost."

No matter the sport, every team is trying to get an edge over the competition. The front office of any organization is always looking for innovative ways to make sure the product on the field reaches its peak. That's why Sevilla FC, one of La Liga's top soccer clubs, has teamed with IBM and its watsonx generative AI to develop a new way of evaluating players in the scouting department. Sevilla FC introduced Scout Advisor Tuesday. It's an innovative tool built by IBM's watsonx to revamp its recruitment process.

Vattenfall is a European energy company with approximately 20 000 employees. For more than 100 years we have electrified industries, supplied energy to people's homes and modernized our way of living through innovation and cooperation. We now want to make fossil-free living possible within one generation. To be able to reach this ambitious goal we are looking for talented individuals who, in addition to their passion for their own role, also have strong team spirit and want to contribute to supporting a meaningful corporate mission. You are convinced that your interest in data can drive you to make a decisive contribution to Vattenfall's journey to become a data-driven company?

Vattenfall is a European energy company with approximately 20 000 employees. For more than 100 years we have electrified industries, supplied energy to people's homes and modernized our way of living through innovation and cooperation. We now want to make fossil-free living possible within one generation. To be able to reach this ambitious goal we are looking for talented individuals who, in addition to their passion for their own role, also have strong team spirit and want to contribute to supporting a meaningful corporate mission. We offer power purchase agreements to operators of wind or solar parks, optimize 3rd party batteries and enter into origination deals within the B2B segment.

Artificial Intelligence (AI) has become ubiquitous in the past several years. There is not a part of our businesses, cultures, governments and consumer markets. The continuous research and innovation directed by tech giants are driving the adoption of advanced technologies in industry verticals, such as automotive, healthcare, retail, finance, and manufacturing, staffing and education. Technology has always been an essential element for these industries, but artificial intelligence has brought technology to the center of organizations. For instance, from self-driving vehicles to crucial life-saving medical gear, AI is being infused virtually into every apparatus and program.

The integration of digital technologies with a data-driven approach is transforming the way businesses operate and the value they aim to generate. The use of digital technologies such as cloud computing, artificial intelligence, and machine learning is enabling businesses to collect and analyze large amounts of data in real-time. Data analytics is not a buzzword any more. Business leaders will agree that data analytics is more than just numbers. It is a culture, a thought process that aims to leverage business intelligence.