Imagine a digital filter that reads resumes but accidentally ignores great candidates because of their name or background. This project builds a smarter system that spots these unfair patterns and fixes them. It works across several European languages, not just English, to ensure everyone gets a fair shot at a job.
AI tools used in hiring often mirror human biases, leading to unfair exclusion of candidates. This creates legal risks and prevents companies from accessing a diverse talent pool.
A proof-of-concept decision support system combining Natural Language Processing (NLP) and Case-Based Reasoning (CBR) to detect and fix bias in HR recruitment.
If you are an HR software vendor dealing with biased automated screening — this project developed a proof-of-concept combining NLP and Case-Based Reasoning that reduces discrimination in job applications. This helps your clients meet European social rights standards.
If you are a recruitment agency dealing with intersectional discrimination in candidate selection — this project developed a tool to identify and mitigate bias in text analysis. This ensures a fairer pipeline for candidates across 7 different European languages.
If you are a corporate HR department dealing with unfair AI-driven disciplinary or training decisions — this project developed a decision support system that mitigates unwanted bias. This protects the company from legal risks related to gender and equal opportunity goals.
Based on available project data, no pricing or cost information is provided as this is a research-funded project.
The project is developing a proof-of-concept intended for real use cases in HRM, though full industrial scale metrics are not yet detailed.
Based on available project data, specific licensing terms are not mentioned; however, it involves a consortium of 9 partners including 5 industry members.
The tool is designed to align with the European Pillar of Social Rights, specifically regarding gender equality and equal opportunity.
It is designed as a decision support system for HR recruitment use cases, utilizing Natural Language Processing and Case-Based Reasoning.
The consortium is heavily weighted toward commercial application, with a 56% industry ratio (5 industry partners, 4 of which are SMEs). This balance suggests a strong focus on practical implementation rather than pure theory, supported by 4 universities across 9 countries.
Contact NTNU (Norwegian University of Science and Technology)
Contact us to explore licensing the proof-of-concept for your HR tech stack.
