We investigate the existence and persistence of a specific type of gender bias in some of the popular LLMs and contribute a new benchmark dataset, RealWorldQuestioning (released on HuggingFace ), developed from real-world questions across four key domains in business and health contexts: education, jobs, personal financial management, and general health. We define and study entropy bias, which we define as a discrepancy in the amount of information generated by an LLM in response to real questions users have asked. We tested this using four different LLMs and evaluated the generated responses both qualitatively and quantitatively by using ChatGPT-4o (as "LLM-as-judge"). Our analyses (metric-based comparisons and "LLM-as-judge" evaluation) suggest that there is no significant bias in LLM responses for men and women at a category level. However, at a finer granularity (the individual question level), there are substantial differences in LLM responses for men and women in the majority of cases, which "cancel" each other out often due to some responses being better for males and vice versa. This is still a concern since typical users of these tools often ask a specific question (only) as opposed to several varied ones in each of these common yet important areas of life. We suggest a simple debiasing approach that iteratively merges the responses for the two genders to produce a final result. Our approach demonstrates that a simple, prompt-based debiasing strategy can effectively debias LLM outputs, thus producing responses with higher information content than both gendered variants in 78% of the cases, and consistently achieving a balanced integration in the remaining cases.

Accurate career path prediction can support many stakeholders, like job seekers, recruiters, HR, and project managers. However, publicly available data and tools for career path prediction are scarce. In this work, we introduce KARRIEREWEGE, a comprehensive, publicly available dataset containing over 500k career paths, significantly surpassing the size of previously available datasets. We link the dataset to the ESCO taxonomy to offer a valuable resource for predicting career trajectories. To tackle the problem of free-text inputs typically found in resumes, we enhance it by synthesizing job titles and descriptions resulting in KARRIEREWEGE+. This allows for accurate predictions from unstructured data, closely aligning with real-world application challenges. We benchmark existing state-of-the-art (SOTA) models on our dataset and a prior benchmark and observe improved performance and robustness, particularly for free-text use cases, due to the synthesized data.

A career is a crucial aspect for any person to fulfill their desires through hard work. During their studies, students cannot find the best career suggestions unless they receive meaningful guidance tailored to their skills. Therefore, we developed an AI-assisted model for early prediction to provide better career suggestions. Although the task is difficult, proper guidance can make it easier. Effective career guidance requires understanding a student's academic skills, interests, and skill-related activities. In this research, we collected essential information from Computer Science (CS) and Software Engineering (SWE) students to train a machine learning (ML) model that predicts career paths based on students' career-related information. To adequately train the models, we applied Natural Language Processing (NLP) techniques and completed dataset pre-processing. For comparative analysis, we utilized multiple classification ML algorithms and deep learning (DL) algorithms. This study contributes valuable insights to educational advising by providing specific career suggestions based on the unique features of CS and SWE students. Additionally, the research helps individual CS and SWE students find suitable jobs that match their skills, interests, and skill-related activities.

While Microsoft co-founder Bill Gates remains optimistic about the social benefits of artificial intelligence, now even the billionaire mogul fears that AI could take his job. The candid quip came during a recent podcast conversation with OpenAI CEO Sam Altman, whose company is responsible for the AI-powered chatbot ChatGPT. Over the years, Gates has maintained that the three best career paths for recent graduates are those in alternative energy, health biosciences, and advancing artificial intelligence itself -- but notably'billionaire philanthropist' is not on that list. 'I could even lose my job,' Gates said on his podcast, 'Unconfuse Me with Bill Gates.' 'When the machine says to me, "Bill, go play pickleball, I've got malaria eradication. You're just a slow thinker,"' he worried, 'then it is a philosophically confusing thing.'

This study explores the potential of reinforcement learning algorithms to enhance career planning processes. Leveraging data from Randstad The Netherlands, the study simulates the Dutch job market and develops strategies to optimize employees' long-term income. By formulating career planning as a Markov Decision Process (MDP) and utilizing machine learning algorithms such as Sarsa, Q-Learning, and A2C, we learn optimal policies that recommend career paths with high-income occupations and industries. The results demonstrate significant improvements in employees' income trajectories, with RL models, particularly Q-Learning and Sarsa, achieving an average increase of 5% compared to observed career paths. The study acknowledges limitations, including narrow job filtering, simplifications in the environment formulation, and assumptions regarding employment continuity and zero application costs. Future research can explore additional objectives beyond income optimization and address these limitations to further enhance career planning processes.

When we think of artificial intelligence, we might think of robots like the ones in "Ex Machina," who are scarily smarter, closer to humans, and more perceptive than we think. The reality is that artificial intelligence is an innovative, growing field that offers creative opportunities for those who want to revolutionize the way we use technology. So, what is artificial intelligence, and what does a career in the field look like? Artificial intelligence (AI) is a branch of computer science concerning machines that can synthesize and process information to problem-solve. The concept first came into public view with Alan Turing's 1950 paper, "Computing Machinery and Intelligence," which explored whether we could train machines to think like humans.

Talent acquisition and management has evolved significantly in recent years and it will continue to change in the future. Digital solutions are empowering businesses with new and more effective ways to find and retain the best talent. Modern tools allow companies to fill open positions in record time and with minimal unnecessary fuss. A major contributor to this evolution is the introduction and adoption of AI-based tools. Used properly, automation can help businesses and employees work in a way that is more efficient and can ultimately make working more productive and fulfilling.

Abakar Saidov is co-founder and CEO of Beamery, a leader in talent lifecycle management. In the wake of the "Great Reshuffle," companies continue to reevaluate their approach to recruitment and retention. In order to drive efficiency and remain effective at scale, business leaders are increasingly turning to new technologies for support. One of the most valuable technologies supporting talent management strategies today is artificial intelligence (AI). It has the potential to revolutionize the way in which businesses interact with the wider talent landscape, helping HR teams and recruiters fill much-needed positions and identify the skill sets in most demand.

The best 5G startups are making groundbreaking innovations in wireless technology and communications. This is attributable to the importance of 5G in the world today so much that IHS Markit projects that 5G would increase the global GDP by $13.2 trillion by 2035. The benefits of 5G range from a dramatically fast network to lower latency, resulting in innovative solutions such as better connectivity for individual users and businesses. Here is a closer look at the top 10 5G startup companies. Some of these technologies include artificial intelligence (AI), the Internet of Things (IoT), and augmented reality (AR).

Recently, Artificial Intelligence has become the most buzzword and trending topic and has made a huge impact in the world of technology. Moreover, it has become a complicated and technical concept to understand. Therefore, you must have a proper understanding and knowledge of the fundamentals of this domain. Machine learning has become more popular without even noticing. Therefore, learning and understanding Artificial Intelligence have become something most developers and programmers strive to accomplish.