The importance of Synthetic Data Generation (SDG) has increased significantly in domains where data quality is poor or access is limited due to privacy and regulatory constraints. One such domain is recruitment, where publicly available datasets are scarce due to the sensitive nature of information typically found in curricula vitae, such as gender, disability status, or age. This lack of accessible, representative data presents a significant obstacle to the development of fair and transparent machine learning models, particularly ranking algorithms that require large volumes of data to effectively learn how to recommend candidates. In the absence of such data, these models are prone to poor generalisation and may fail to perform reliably in real-world scenarios. Recent advances in Causal Generative Models (CGMs) offer a promising solution. CGMs enable the generation of synthetic datasets that preserve the underlying causal relationships within the data, providing greater control over fairness and interpretability in the data generation process. In this study, we present a specialised SDG method involving two CGMs: one modelling job offers and the other modelling curricula. Each model is structured according to a causal graph informed by domain expertise. We use these models to generate synthetic datasets and evaluate the fairness of candidate rankings under controlled scenarios that introduce specific biases.

Good Job I Have a Job Offer I Can't Refuse. The Company It Comes From Has a Terrible Reputation for Women. My company unexpectedly outsourced my entire department to a firm that uses AI for our jobs, even though I don't work a job that can really be done by machine learning. I have some savings but can't go without health insurance: my daughter and I both have the same complex chronic condition. I was briefly on public insurance in the past and it was a nightmare of waitlists leading to a cascade of hospital stays.

Advances in natural language processing and large language models are driving a major transformation in Human Capital Management, with a growing interest in building smart systems based on language technologies for talent acquisition, upskilling strategies, and workforce planning. However, the adoption and progress of these technologies critically depend on the development of reliable and fair models, properly evaluated on public data and open benchmarks, which have so far been unavailable in this domain. To address this gap, we present TalentCLEF 2025, the first evaluation campaign focused on skill and job title intelligence. The lab consists of two tasks: Task A - Multilingual Job Title Matching, covering English, Spanish, German, and Chinese; and Task B - Job Title-Based Skill Prediction, in English. Both corpora were built from real job applications, carefully anonymized, and manually annotated to reflect the complexity and diversity of real-world labor market data, including linguistic variability and gender-marked expressions. The evaluations included monolingual and cross-lingual scenarios and covered the evaluation of gender bias. TalentCLEF attracted 76 registered teams with more than 280 submissions. Most systems relied on information retrieval techniques built with multilingual encoder-based models fine-tuned with contrastive learning, and several of them incorporated large language models for data augmentation or re-ranking. The results show that the training strategies have a larger effect than the size of the model alone. TalentCLEF provides the first public benchmark in this field and encourages the development of robust, fair, and transferable language technologies for the labor market.

While NLP models often seek to capture cognitive states via language, the validity of predicted states is determined by comparing them to annotations created without access the cognitive states of the authors. In behavioral sciences, cognitive states are instead measured via experiments. Here, we introduce an experiment-based framework for evaluating language-based cognitive style models against human behavior. We explore the phenomenon of decision making, and its relationship to the linguistic style of an individual talking about a recent decision they made. The participants then follow a classical decision-making experiment that captures their cognitive style, determined by how preferences change during a decision exercise. We find that language features, intended to capture cognitive style, can predict participants' decision style with moderate-to-high accuracy (AUC ~ 0.8), demonstrating that cognitive style can be partly captured and revealed by discourse patterns.

On his videos, Sergey appears fresh-faced and smiling enthusiastically. Speaking in Russian and sometimes in slightly broken English, he goes through coding challenges step-by-step. His YouTube channel has more than 3,500 subscribers, interested in learning programming languages Python and Unity, or who want to hear more about neural networks, which underlie many artificial intelligence tools. Mr Mandik said Sergey showed not only remarkable developer skills but also "equally unique" skills in teaching. "For me, he is kind of a Mozart."

The goal of this paper is to develop a multilingual classifier and conditional probability estimator of occupation codes for online job advertisements according in accordance with the International Standard Classification of Occupations (ISCO) extended with the Polish Classification of Occupations and Specializations (KZiS), which is analogous to the European Classification of Occupations. In this paper, we utilise a range of data sources, including a novel one, namely the Central Job Offers Database, which is a register of all vacancies submitted to Public Employment Offices. Their staff members code the vacancies according to the ISCO and KZiS. A hierarchical multi-class classifier has been developed based on the transformer architecture. The classifier begins by encoding the jobs found in advertisements to the widest 1-digit occupational group, and then narrows the assignment to a 6-digit occupation code. We show that incorporation of the hierarchical structure of occupations improves prediction accuracy by 1-2 percentage points, particularly for the hand-coded online job advertisements. Finally, a bilingual (Polish and English) and multilingual (24 languages) model is developed based on data translated using closed and open-source software. The open-source software is provided for the benefit of the official statistics community, with a particular focus on international comparability.

Contextual utility theory integrates context-sensitive factors into utility-based decision-making models. It stresses the importance of understanding individual decision-makers' preferences, values, and beliefs and the situational factors that affect them. Contextual utility theory benefits explainable AI. First, it can improve transparency and understanding of how AI systems affect decision-making. It can reveal AI model biases and limitations by considering personal preferences and context. Second, contextual utility theory can make AI systems more personalized and adaptable to users and stakeholders. AI systems can better meet user needs and values by incorporating demographic and cultural data. Finally, contextual utility theory promotes ethical AI development and social responsibility. AI developers can create ethical systems that benefit society by considering contextual factors like societal norms and values. This work, demonstrates how contextual utility theory can improve AI system transparency, personalization, and ethics, benefiting both users and developers.

GenieTalk.ai is the forerunner in designing the best AI-powered digital experiences, be it voice bots or chatbots, switching the world from rule-based, monotonous chatbots to interactive & intuitive human-in-the-room experiences with advanced conversational AI', customized solutions for enterprises to create seamless business operations & happy customer experiences. We help build solutions to cater to customers for industries across such as health, finance, insurance, e-commerce, education, automotive, contact centers, super app, etc. with the most advanced conversation AI to create a seamless user experience, committed to giving people a completely effortless way of doing things be it transaction over voice, having a conversation with support or getting information. We bring the world to experience a new age of automation & digitalization with meaningful conversations through conversational AI. Selected intern's day-to-day responsibilities include: Job offer: On successful conversion to a permanent employee, the candidate can expect a salary of Rs. 2 to 4 Lac/annum

Manual evaluation is becoming exceedingly complex and time-consuming, justifying the need for an automatic evaluation of these changes [1]. One way to study these changes in job ads is automatic skills recognition [2]. However, job offer data is not easy to access even in job offers web platforms, mainly due to intellectual property issues. Moreover, the annotation needed to achieve good skill recognition performances through supervised machine learning is another complex and costly task. Indeed, many datasets offering job descriptions are accessible online such as the mycareersfuture public dataset [3]. However, as [4] reported in their article, very few public annotated datasets exist, and none are in French. As contributions, in this article, we propose "French Insurance Job Offer (FIJO)", a free and public non-annotated and annotated dataset, to facilitate research in this domain. This dataset focuses on soft skills, which describe the way employees work alone and with others, instead of hard skills, which represent a more formal knowledge used at work [5].

Bias in machine learning has manifested injustice in several areas, such as medicine, hiring, and criminal justice. In response, computer scientists have developed myriad definitions of fairness to correct this bias in fielded algorithms. While some definitions are based on established legal and ethical norms, others are largely mathematical. It is unclear whether the general public agrees with these fairness definitions, and perhaps more importantly, whether they understand these definitions. We take initial steps toward bridging this gap between ML researchers and the public, by addressing the question: does a non-technical audience understand a basic definition of ML fairness? We develop a metric to measure comprehension of one such definition--demographic parity. We validate this metric using online surveys, and study the relationship between comprehension and sentiment, demographics, and the application at hand.