FairHire: AI-Driven Resume Profiling and Technical Interview Automation
In today’s competitive job market, technical hiring processes often face challenges related to biases and inefficiencies, leading to inconsistent and sometimes unfair candidate evaluations. FairHire is designed to address these challenges by creating a fairer, more objective, and technologically advanced interview process. The platform begins by analyzing candidate resumes using Natural Language Processing (NLP) algorithms to extract critical skills and qualifications, which inform the creation of tailored, role-specific interview questions. By leveraging large language models (LLMs), FairHire adapts questions dynamically, generating contextually relevant and progressively detailed inquiries based on the candidate’s responses. This approach ensures a nuanced and thorough evaluation, closely aligned with both the candidate’s background and the job’s requirements. During the interview, FairHire captures video and audio responses in real time, applying machine learning and NLP techniques to score each answer on criteria such as relevance, accuracy, and depth, thereby minimizing human biases and ensuring a consistent, data-driven assessment. The platform aggregates individual response scores to provide a comprehensive evaluation of technical skills, enabling recruiters to make well-informed hiring decisions efficiently. By combining technological innovation with a commitment to fairness, FairHire aims to transform technical recruitment, promoting inclusivity, quality, and consistency in candidate evaluation.
Authors
- André Rolim Baby (ORCID: https://orcid.org/0000-0001-9197-3024)
- R B Gomes Jose
- Vishnu Narayan Vipin (ORCID: https://orcid.org/0000-0003-0837-9986)
- Sheikh Ahmad Zaki Shaikh Salim (ORCID: https://orcid.org/0000-0001-6411-9965)
- Gamboa-Rosales N.K.
Publication Details
- Published
- 2025-04-03
- DOI
- https://doi.org/10.1109/ictest64710.2025.11042555
- Citations
- 1
- Primary Topic
- Ethics and Social Impacts of AI
- Type
- article
- Field-Weighted Citation Impact
- 2.37