A Glossary of Key Terms in Types of AI Algorithms, Models, and Technologies Used in Candidate Screening

Understanding the intricate world of Artificial Intelligence (AI) is no longer just for tech specialists; it’s a strategic imperative for HR and recruiting professionals. As talent acquisition becomes increasingly data-driven, a clear grasp of the AI algorithms, models, and technologies shaping candidate screening processes is essential. This glossary demystifies the core concepts, providing a foundation for leveraging AI responsibly and effectively to identify, assess, and engage top talent, ensuring both efficiency and fairness in your hiring strategies.

Artificial Intelligence (AI)

Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. In the context of candidate screening, AI encompasses a broad range of technologies designed to automate or augment various stages of the recruitment funnel. This can include everything from intelligent chatbots that answer candidate queries to sophisticated algorithms that analyze resumes and video interviews. The goal is to enhance decision-making, reduce manual workload, and improve the candidate experience, ultimately leading to more efficient and potentially less biased hiring outcomes by processing vast amounts of data quickly and consistently.

Machine Learning (ML)

Machine Learning (ML) is a subset of AI that enables systems to learn from data, identify patterns, and make predictions or decisions with minimal human intervention. Instead of being explicitly programmed, ML algorithms are trained on large datasets to recognize relationships and correlations. For candidate screening, ML models are trained on historical hiring data – such as successful candidate profiles, performance reviews, or specific skill sets – to predict which new candidates are most likely to succeed. This technology underpins tools that score resumes, identify top-performing applicants, or even predict candidate retention rates, continuously improving their accuracy as more data becomes available.

Deep Learning

Deep Learning is an advanced form of Machine Learning that uses artificial neural networks with multiple layers (hence “deep”) to learn from vast amounts of data. Inspired by the structure and function of the human brain, deep learning excels at processing complex, unstructured data such as images, video, and natural language. In candidate screening, deep learning is particularly powerful for tasks like advanced resume parsing that goes beyond keywords to understand context and intent, analyzing video interview responses for verbal and non-verbal cues, or even interpreting candidate sentiment from written communications. Its ability to extract subtle features makes it invaluable for comprehensive candidate assessment.

Natural Language Processing (NLP)

Natural Language Processing (NLP) is a branch of AI that gives computers the ability to understand, interpret, and generate human language. In HR, NLP is foundational for automating and enhancing many text-based recruitment tasks. It powers resume analysis software that can extract relevant skills, experience, and qualifications from free-form text, enabling rapid screening and matching against job requirements. NLP also drives AI-powered chatbots for initial candidate interactions, helps analyze job descriptions to identify potentially biased language, and can even assess candidate responses in written assessments or open-ended interview questions for relevance and tone, streamlining communication and evaluation.

Computer Vision

Computer Vision is an AI field that enables computers to “see” and interpret visual information from the real world, much like humans do. While its application in HR is evolving and often raises ethical considerations, computer vision can be used in candidate screening to analyze non-verbal cues in video interviews. This might include assessing facial expressions, body language, or eye gaze patterns, though its use remains highly scrutinized for potential biases and validity. More benign applications could involve verifying identity during online assessments or ensuring a candidate is alone during a remote proctored exam. Its deployment requires careful ethical consideration and transparency.

Predictive Analytics

Predictive Analytics leverages statistical algorithms and machine learning techniques to analyze historical data and forecast future outcomes or trends. In the realm of candidate screening, predictive analytics can be used to identify candidates who are most likely to perform well, stay with the company longer, or fit into the company culture. By analyzing factors such as past job performance metrics, assessment scores, educational background, and even behavioral data, organizations can develop models that predict future success. This enables recruiters to prioritize candidates with the highest potential, optimize sourcing strategies, and reduce time-to-hire by focusing resources on the most promising applicants.

Algorithmic Bias

Algorithmic Bias refers to systematic and repeatable errors in a computer system that create unfair outcomes, such as favoring or disfavoring certain groups of individuals. In candidate screening, this often arises when AI models are trained on historical data that reflects existing human biases, leading the algorithm to perpetuate or even amplify those biases in its decisions. For instance, if past successful hires predominantly came from a certain demographic, the AI might inadvertently deprioritize candidates from other groups. Addressing algorithmic bias is crucial for ethical AI deployment in HR, requiring careful data curation, bias detection techniques, and continuous monitoring to ensure fair and equitable hiring practices.

Supervised Learning

Supervised Learning is a type of machine learning where the algorithm learns from a dataset that has been “labeled,” meaning each input example is paired with its correct output. For instance, in candidate screening, a supervised learning model might be trained on resumes labeled as “hired” or “not hired,” along with corresponding performance data. The algorithm then learns the mapping from input features (e.g., skills, experience) to output labels (e.g., successful hire). This method is widely used for classification tasks like categorizing resumes, predicting candidate suitability based on past outcomes, or identifying specific skill sets, enabling the AI to make informed decisions on new, unseen candidate data.

Unsupervised Learning

Unsupervised Learning is a type of machine learning where the algorithm works with unlabeled data, seeking to find patterns or structures within the data on its own. Unlike supervised learning, there are no predefined “correct” answers for the algorithm to learn from. In candidate screening, unsupervised learning can be used to cluster similar candidates together based on their attributes (e.g., grouping candidates with similar skill sets or career paths without prior labels). This can reveal previously unrecognized candidate segments or help identify unique talent pools. It’s particularly useful for exploratory data analysis, discovering hidden insights, and personalizing candidate recommendations based on inherent similarities rather than predefined categories.

Neural Networks

Neural Networks are a series of algorithms modeled after the human brain, designed to recognize patterns and relationships in data. They are fundamental to deep learning and are composed of interconnected layers of “neurons” that process information. Each neuron receives inputs, performs a calculation, and passes the output to the next layer. In candidate screening, neural networks are powerful for tasks requiring complex pattern recognition, such as parsing highly variable resume formats, identifying subtle contextual cues in written responses, or processing audio/video data from interviews. Their ability to learn intricate, non-linear relationships makes them highly effective for advanced analytical and predictive tasks in talent acquisition.

Automated Resume Screening

Automated Resume Screening utilizes AI technologies, primarily Natural Language Processing (NLP) and Machine Learning, to efficiently review and filter large volumes of resumes. Instead of manual review, AI algorithms scan resumes for keywords, skills, experience, education, and other criteria specified in the job description. This process can quickly identify qualified candidates, rank them by suitability, and eliminate those who do not meet minimum requirements. Beyond simple keyword matching, advanced systems can infer skills, understand context, and even identify potential “red flags” or indications of strong cultural fit, significantly reducing the time-to-hire and allowing recruiters to focus on the most promising applicants.

Behavioral Assessment AI

Behavioral Assessment AI leverages artificial intelligence to evaluate a candidate’s personality traits, cognitive abilities, and potential cultural fit through various digital interactions. This can involve analyzing responses to online questionnaires, evaluating decision-making patterns in gamified assessments, or even interpreting language and tone from video interviews. The AI models identify consistent behavioral patterns and compare them against profiles of successful employees within the organization, or against established benchmarks for specific roles. The aim is to provide objective insights into a candidate’s soft skills and predispositions, moving beyond traditional resume data to predict job performance and team dynamics more accurately.

AI-Powered Chatbots

AI-powered Chatbots are conversational agents designed to simulate human interaction through text or voice. In candidate screening, these chatbots play a crucial role in enhancing the candidate experience and automating initial engagement. They can answer common candidate questions about the role or company, guide applicants through the application process, schedule interviews, and even conduct preliminary screening questions to assess basic qualifications. By providing instant responses and 24/7 availability, chatbots improve efficiency for both candidates and recruiters, reduce administrative burdens, and ensure that potential hires feel supported throughout their journey, leading to higher engagement and application completion rates.

Recommendation Systems

Recommendation Systems are AI algorithms designed to suggest relevant items to users based on their preferences, past behavior, or similarities to other users. In the context of candidate screening, these systems can be employed in several ways. For candidates, a recommendation system might suggest other suitable job openings within the company based on their profile and application history. For recruiters, it can recommend top-tier candidates who might be a good fit for a particular role, even if they haven’t directly applied, by analyzing data from internal talent pools or external databases. This proactive matching enhances talent discovery and ensures that promising candidates are not overlooked.

Explainable AI (XAI)

Explainable AI (XAI) refers to the development of AI models that can clarify their reasoning and decision-making processes in human-understandable terms. While many advanced AI systems, particularly deep learning models, can be opaque “black boxes,” XAI aims to provide transparency. In candidate screening, XAI is critically important for addressing concerns about fairness, bias, and legal compliance. It allows HR professionals to understand *why* an AI system ranked a candidate in a particular way, or why certain features were deemed important in a hiring decision. This transparency helps build trust, validate the system’s fairness, and allows human oversight to correct potential algorithmic errors or biases, ensuring ethical AI deployment.

If you would like to read more, we recommend this article: The Strategic Imperative of AI in Modern HR and Recruiting: Navigating the Future of Talent Acquisition and Management