A Glossary of Key AI & Machine Learning Concepts for HR

In today’s rapidly evolving talent landscape, Artificial Intelligence (AI) and Machine Learning (ML) are no longer buzzwords but foundational technologies reshaping how HR and recruiting professionals operate. From optimizing talent acquisition to personalizing employee experiences, understanding the core concepts of AI and ML is crucial for strategic decision-making and innovation. This glossary provides clear, authoritative definitions of key terms, helping HR leaders navigate this technological frontier with confidence and leverage these tools for tangible business outcomes.

Artificial Intelligence (AI)

Artificial Intelligence refers to the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions), and self-correction. In HR, AI manifests in various forms, such as intelligent chatbots for candidate interaction, sophisticated algorithms for resume screening, and predictive models for identifying flight risk. It’s about enabling systems to perform tasks that typically require human cognition, thereby automating repetitive processes, enhancing decision-making, and freeing up HR professionals for more strategic work.

Machine Learning (ML)

Machine Learning is a subset of AI that enables systems to learn from data, identify patterns, and make decisions with minimal human intervention. Instead of being explicitly programmed for every task, ML algorithms improve their performance over time as they are exposed to more data. For HR, this means a system can learn to identify the characteristics of successful hires from historical data, predict future hiring needs, or even optimize interview schedules based on past efficiency. It’s the engine behind many AI applications, constantly refining its insights to provide more accurate and actionable intelligence for recruiting and talent management.

Natural Language Processing (NLP)

Natural Language Processing is an AI field focused on enabling computers to understand, interpret, and generate human language. NLP allows machines to read text, hear speech, interpret it, measure sentiment, and determine which parts are important. In HR, NLP is a game-changer for tasks like parsing resumes to extract relevant skills and experience, analyzing candidate responses in interviews or surveys, or even drafting job descriptions. It automates the review of vast amounts of unstructured textual data, allowing recruiters to quickly identify top candidates and HR to gauge employee sentiment from internal communications, significantly reducing manual effort and improving accuracy.

Predictive Analytics

Predictive Analytics uses statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data. By analyzing past patterns, these tools can forecast trends and behaviors. In an HR context, predictive analytics can be used to predict employee turnover by identifying common characteristics among past departees, forecast future talent needs based on business growth projections, or anticipate which candidates are most likely to succeed in a role. This proactive approach empowers HR leaders to make data-driven decisions that mitigate risks, optimize workforce planning, and improve retention strategies.

Generative AI

Generative AI refers to AI models capable of creating new content, such as text, images, code, or audio, that resembles real-world data it was trained on. Unlike traditional AI that analyzes existing data, generative AI *produces* novel outputs. For HR, this opens up exciting possibilities: automatically drafting personalized outreach emails to candidates, generating diverse interview questions, creating initial versions of job descriptions, or even developing engaging onboarding content. It’s a powerful tool for accelerating content creation and personalizing communication at scale, saving significant time for recruiters and HR managers.

Large Language Models (LLMs)

Large Language Models are a type of generative AI that has been trained on vast amounts of text data to understand, generate, and process human language. LLMs, like OpenAI’s GPT series or Google’s LaMDA, can perform a wide range of language-based tasks, including summarization, translation, text completion, and answering complex questions. In HR, LLMs are used to enhance candidate experience through intelligent chatbots, assist with drafting complex HR policies or internal communications, analyze survey responses for themes, and even summarize extensive candidate profiles, drastically improving efficiency in communication and content generation.

Algorithmic Bias

Algorithmic Bias occurs when an algorithm produces results that are systematically unfair or discriminatory, often due to biases present in the training data or the algorithm’s design. If an AI system is trained on historical hiring data that reflects past human biases (e.g., favoring certain demographics), it may perpetuate or even amplify those biases in its recommendations. In HR, addressing algorithmic bias is critical for ensuring equitable hiring practices and fair employee evaluations. Ethical AI design and diverse training datasets are essential to prevent systems from inadvertently discriminating against certain groups, ensuring a truly meritocratic process.

Data Science

Data Science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. It combines elements of statistics, computer science, and domain expertise. In HR, data science enables professionals to delve deep into HR data (e.g., performance reviews, compensation, benefits, tenure) to uncover trends, predict outcomes, and optimize strategies. A data scientist might build models to identify factors contributing to high performance or low engagement, providing concrete, data-backed insights for improving workforce effectiveness and organizational health.

Supervised Learning

Supervised Learning is a type of machine learning where an algorithm learns from a labeled dataset, meaning each piece of data already has a known output or “answer.” The algorithm uses this input-output pair to learn a mapping function, allowing it to predict outcomes for new, unseen data. In HR, a supervised learning model might be trained on historical data of successful hires (inputs) and their job performance ratings (outputs) to predict the success of new candidates. This method is highly effective for tasks where clear historical outcomes exist, such as predicting employee turnover, assessing candidate fit, or categorizing applicant skills.

Unsupervised Learning

Unsupervised Learning is a type of machine learning where an algorithm explores unlabeled data to find hidden patterns or intrinsic structures without any prior knowledge of outputs. Unlike supervised learning, there are no “correct answers” in the training data. For HR, unsupervised learning can be used to segment employees into distinct groups based on behavioral patterns or skill sets, identify clusters of similar job applicants from unstructured data like resumes, or discover emerging trends in employee feedback. It’s valuable for exploratory data analysis, helping HR uncover unforeseen insights and correlations that might not be obvious with traditional analysis methods.

Deep Learning

Deep Learning is a specialized subfield of machine learning that uses artificial neural networks with multiple layers (hence “deep”) to learn representations of data with multiple levels of abstraction. Inspired by the human brain, deep learning models can automatically discover complex patterns in large datasets, often outperforming traditional machine learning methods in tasks like image recognition, speech recognition, and complex natural language processing. In HR, deep learning can power highly accurate facial recognition for secure access, analyze nuances in video interviews, or process complex language from open-ended feedback, leading to more sophisticated and nuanced insights.

Recommendation Systems

Recommendation Systems are a type of information filtering system that predicts the “rating” or “preference” a user would give to an item. They are designed to suggest relevant items to users. While commonly seen in e-commerce (e.g., “customers who bought this also bought…”), in HR, these systems can recommend internal job openings to employees based on their skills and career paths, suggest relevant training programs, or even match candidates with roles they might not have initially considered but are a strong fit for. This enhances internal mobility, improves employee development, and streamlines talent matching, creating more personalized experiences within the organization.

Feature Engineering

Feature Engineering is the process of selecting, transforming, and creating new variables (features) from raw data to improve the performance of machine learning models. It involves using domain knowledge to extract meaningful attributes that best represent the underlying problem to the model. In HR analytics, feature engineering could involve creating a “tenure risk score” from an employee’s hire date, promotion history, and compensation changes, or deriving a “cultural fit score” from survey responses. Effective feature engineering is crucial for building robust AI models that can accurately predict HR outcomes, as it directly impacts the quality of the insights generated.

AI Ethics

AI Ethics refers to the set of principles and guidelines for responsible development and deployment of artificial intelligence, focusing on issues like fairness, accountability, transparency, privacy, and human oversight. As AI increasingly impacts HR decisions, ethical considerations are paramount to avoid bias, ensure data security, protect employee rights, and maintain trust. For HR professionals, understanding AI ethics means scrutinizing the data used to train algorithms, ensuring transparency in how AI systems make decisions, and establishing clear human oversight mechanisms to prevent unintended discrimination or negative impacts on employees and candidates.

Automation

Automation is the use of technology to perform tasks with minimal human intervention. In the context of AI and ML, automation often refers to streamlining processes that were traditionally manual, making them faster, more accurate, and more scalable. For HR and recruiting, automation is transformative: automating resume screening, sending personalized follow-up emails, scheduling interviews, onboarding new hires, or processing routine queries through chatbots. Combined with AI, automation moves beyond simple rule-based tasks to intelligent, adaptive workflows that significantly reduce administrative burdens, allowing HR teams to focus on strategic initiatives and human-centric roles.

If you would like to read more, we recommend this article: AI-Powered Resume Parsing: Your Blueprint for Strategic Talent Acquisition