Recently, while talking with HR experts and recruiters, I've noticed increasing discussion about the reliability of AI algorithms in recruitment. At first glance, artificial intelligence seems capable of quickly and efficiently handling most recruiting tasks. However, in practice, we encounter "blind spots," leading to irrelevant candidate recommendations.
How do AI candidate recommendations work?
Modern AI recruiting tools rely on machine learning technologies. Simply put, algorithms study large amounts of data from past hires, analyzing resumes, interview outcomes, tests, and other available information to identify patterns. For example, if the algorithm sees that candidates with certain skills or experience have frequently succeeded, it begins to "prefer" similar specialists.
Most AI systems follow this logic:
Data Collection and Analysis: Systems gather resume texts, job descriptions, candidate questionnaires, and even information from social media or professional networks.
Keyword Matching: The simplest and most common method involves matching keywords and phrases from the candidate's resume to the job description. Older ATS (Applicant Tracking Systems) often miss essential skills or experiences if candidates use unconventional wording.
Natural Language Processing (NLP): Advanced tools try to understand the meaning of texts by considering context, not just individual words. This approach helps AI more accurately assess candidate qualifications. However, simpler or outdated NLP models often misunderstand professional jargon or language nuances.
Neural Networks and Machine Learning: The most advanced systems use neural networks capable of deeper analysis and comparison of vast amounts of data. These algorithms can even attempt to predict candidate success by analyzing past company decisions and employee career paths. Yet, even neural networks cannot fully evaluate emotional and interpersonal aspects, leading to errors.
How to identify the type of AI tool you are using?
If the tool primarily bases recommendations on keyword matching, it's likely an ATS or a simple scoring model.
If the system analyzes text tone and provides deeper insights into candidate skills, it likely employs NLP technology.
If the tool provides deep analysis and candidate predictions, it probably uses machine learning and neural networks.
Why do "blind spots" occur?
Historical Patterns
If a company historically hires similar types of employees—based on age, gender, experience, or education—the AI algorithm unconsciously continues recommending similar candidates. As a result, the company can get stuck in outdated stereotypes.
Bias Inheritance
AI inevitably absorbs historical biases from training data. For instance, if men were historically favored for leadership roles, the algorithm may continue recommending men, overlooking qualified female candidates.
Limitations in Assessing Soft Skills
Critical soft skills, such as communication, emotional intelligence, adaptability, and leadership, remain challenging for algorithms to measure accurately. Despite advances, AI cannot fully understand and analyze a candidate's personality.
Lack of Contextual Understanding
AI struggles when transferring candidates between different corporate cultures. A candidate who excelled in one company might face challenges in a different environment—something AI often fails to predict.
Ethical and Legal Risks
Using AI in recruitment poses ethical and legal risks. Algorithms may unintentionally discriminate against certain groups based on gender, age, nationality, or other factors, violating laws and corporate ethics. Companies have already faced lawsuits due to discriminatory practices linked to AI decisions, making it essential to regularly monitor and review algorithms.
How to avoid AI "blind spots"?
Data Diversity
Ensuring diversity in historical data used to train AI is crucial. Include successful examples of hires beyond standard templates.
Regular Validation and Updates
Algorithms must regularly be retrained and validated ethically and legally to stay current and reflect labor market changes.
Combining AI and Human Expertise
AI should only be an auxiliary tool. Experienced recruiters must always have the final say, especially in:
Personal interviews, where reading non-verbal cues is vital.
Assessing team compatibility, requiring empathy and deep understanding of the corporate environment.
Evaluating a candidate’s potential and growth prospects based on intuition and personal experience.
Validating Recommendations
Implement procedures for double-checking AI recommendations, particularly for key positions, significantly reducing error risks.
Conclusion
AI is a powerful tool capable of optimizing recruitment, but it is essential to recognize its limitations. Only by combining AI capabilities with human expertise can companies avoid "blind spots" and ensure high-quality hiring.
