arXiv subjects

Nina Baranowska

Publications and source records attributed to Nina Baranowska.

5 recordsLinked to original sources

arXiv2026

Vertical Standardisation for High-Risk AI Systems under the EU AI Act: A Domain-Specific Framework for Algorithmic Hiring

According to the recent European legislation, high-risk AI systems will have to adapt in order to comply with requirements related to specific areas, like risk management, data quality and governance, logging and traceability, technical documentation, transparency, human oversight, and accuracy, as outlined in the European Artificial Intelligence (AI) Act. As the standardisation process for AI is expected to remain iterative and, so far, there are no European standards on AI fully covering the challenges of algorithmic hiring, we propose specific standardisation-oriented recommendations related to the relevant AI areas specified by the European Commission. For each of these areas, we set the context by describing the requirements that AI systems in high-risk domains, and especially in recruitment, should fulfil, as well as the activities that should be carried out to ensure their appropriate use and desired performance, in line with the requirements deriving from the AI Act. Unlike existing horizontal approaches to AI governance and standardisation, this paper contributes a vertical, domain-specific framework for algorithmic hiring, and especially ranking-based recruitment systems, by mapping the requirements of the AI Act to concrete standardisation recommendations, focusing on lifecycle discrimination risks, fairness-aware data governance, explainability, human oversight, and post-deployment monitoring in recruitment systems. Even though our recommendations were informed by the outcomes of the European project FINDHR, they are not tied to the project's technical artefacts and could be implemented using alternative methods, tools, or governance mechanisms.

cs.AI↗

arXiv2026

Co-designing for Compliance: Multi-party Computation Protocols for Post-Market Fairness Monitoring in Algorithmic Hiring

Post-market fairness monitoring is now mandated to ensure fairness and accountability for high-risk employment AI systems under emerging regulations such as the EU AI Act. However, effective fairness monitoring often requires access to sensitive personal data, which is subject to strict legal protections under data protection law. Multi-party computation (MPC) offers a promising technical foundation for compliant post-market fairness monitoring, enabling the secure computation of fairness metrics without revealing sensitive attributes. Despite growing technical interest, the operationalization of MPC-based fairness monitoring in real-world hiring contexts under concrete legal, industrial, and usability constraints remains unknown. This work addresses this gap through a co-design approach integrating technical, legal, and industrial expertise. We identify practical design requirements for MPC-based fairness monitoring, develop an end-to-end, legally compliant protocol spanning the full data lifecycle, and empirically validate it in a large-scale industrial setting. Our findings provide actionable design insights as well as legal and industrial implications for deploying MPC-based post-market fairness monitoring in algorithmic hiring systems.

cs.CY↗

arXiv2026

WorkRB: A Community-Driven Evaluation Framework for AI in the Work Domain

Today's evolving labor markets rely increasingly on recommender systems for hiring, talent management, and workforce analytics, with natural language processing (NLP) capabilities at the core. Yet, research in this area remains highly fragmented. Studies employ divergent ontologies (ESCO, O*NET, national taxonomies), heterogeneous task formulations, and diverse model families, making cross-study comparison and reproducibility exceedingly difficult. General-purpose benchmarks lack coverage of work-specific tasks, and the inherent sensitivity of employment data further limits open evaluation. We present \textbf{WorkRB} (Work Research Benchmark), the first open-source, community-driven benchmark tailored to work-domain AI. WorkRB organizes 13 diverse tasks from 7 task groups as unified recommendation and NLP tasks, including job/skill recommendation, candidate recommendation, similar item recommendation, and skill extraction and normalization. WorkRB enables both monolingual and cross-lingual evaluation settings through dynamic loading of multilingual ontologies. Developed within a multi-stakeholder ecosystem of academia, industry, and public institutions, WorkRB has a modular design for seamless contributions and enables integration of proprietary tasks without disclosing sensitive data. WorkRB is available under the Apache 2.0 license at https://github.com/techwolf-ai/WorkRB.

cs.CL↗

arXiv2025

Fairness and Bias in Algorithmic Hiring: a Multidisciplinary Survey

Employers are adopting algorithmic hiring technology throughout the recruitment pipeline. Algorithmic fairness is especially applicable in this domain due to its high stakes and structural inequalities. Unfortunately, most work in this space provides partial treatment, often constrained by two competing narratives, optimistically focused on replacing biased recruiter decisions or pessimistically pointing to the automation of discrimination. Whether, and more importantly what types of, algorithmic hiring can be less biased and more beneficial to society than low-tech alternatives currently remains unanswered, to the detriment of trustworthiness. This multidisciplinary survey caters to practitioners and researchers with a balanced and integrated coverage of systems, biases, measures, mitigation strategies, datasets, and legal aspects of algorithmic hiring and fairness. Our work supports a contextualized understanding and governance of this technology by highlighting current opportunities and limitations, providing recommendations for future work to ensure shared benefits for all stakeholders.

cs.CY↗

arXiv2024

Non-discrimination law in Europe: a primer for non-lawyers

This brief paper provides an introduction to non-discrimination law in Europe. It answers the questions: What are the key characteristics of non-discrimination law in Europe, and how do the different statutes relate to one another? Our main target group is computer scientists and users of artificial intelligence (AI) interested in an introduction to non-discrimination law in Europe. Notably, non-discrimination law in Europe differs significantly from non-discrimination law in other countries, such as the US. We aim to describe the law in such a way that non-lawyers and non-European lawyers can easily grasp its contents and challenges. The paper shows that the human right to non-discrimination, to some extent, protects individuals against private actors, such as companies. We introduce the EU-wide non-discrimination rules which are included in a number of EU directives, and also explain the difference between direct and indirect discrimination. Significantly, an organization can be fined for indirect discrimination even if the company, or its AI system, discriminated by accident. The last section broadens the horizon to include bias-relevant law and cases from the GDPR, the EU AI Act, and related statutes. Finally, we give reading tips for those inclined to learn more about non-discrimination law in Europe.

cs.CY↗