A Glossary of Core AI/ML Concepts for HR Tech

In today’s rapidly evolving talent landscape, Artificial Intelligence (AI) and Machine Learning (ML) are no longer just buzzwords—they are fundamental tools transforming how HR and recruiting professionals attract, assess, and retain talent. Understanding the core concepts behind these technologies is crucial for leveraging them effectively to automate processes, enhance decision-making, and create more efficient, equitable hiring pipelines. This glossary provides clear, concise definitions tailored to the HR tech context, helping you navigate the complexities and unlock the potential of AI and ML in your organization.

Artificial Intelligence (AI)

Artificial Intelligence refers to the simulation of human intelligence in machines that are programmed to think and learn like humans. In HR tech, AI encompasses a broad range of applications, from automating routine tasks like scheduling interviews and screening resumes to more complex functions such as predicting employee turnover or personalizing learning paths. For recruiters, AI can streamline the initial candidate screening process, identify best-fit candidates faster, and automate communication, freeing up valuable time for strategic interactions. It’s the overarching field enabling machines to perform tasks that typically require human intellect.

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. Unlike traditional programming where rules are explicitly coded, ML algorithms “learn” from historical data to improve performance over time. In HR, ML powers tools that can analyze vast amounts of candidate data to identify ideal profiles, predict job performance, or detect subtle biases in hiring patterns. For automation, ML can continuously refine the accuracy of resume parsing, candidate matching, and even predictive scheduling, making recruitment workflows smarter and more adaptive.

Natural Language Processing (NLP)

Natural Language Processing is an AI subfield that allows computers to understand, interpret, and generate human language. NLP is vital for HR tech, enabling systems to read and comprehend resumes, job descriptions, and interview transcripts. It can extract key skills, experience, and qualifications from unstructured text, automate the creation of job ads, or even analyze candidate sentiment during video interviews. For recruiters, NLP-powered tools can significantly reduce the manual effort of reviewing applications, summarize long documents, and ensure that job descriptions are inclusive and appealing to a diverse talent pool.

Generative AI

Generative AI refers to AI models capable of creating new content, such as text, images, or code, that is often indistinguishable from human-created content. In HR tech, Generative AI can assist in drafting personalized outreach emails to candidates, creating diverse and engaging job descriptions, or even generating interview questions based on specific role requirements. For automation, it offers the potential to rapidly produce content for candidate communications, internal HR documents, or training materials, significantly reducing the time and effort traditionally spent on content creation while maintaining a high level of personalization and relevance.

Predictive Analytics

Predictive analytics uses historical data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on past events. In HR, this means forecasting future staffing needs, predicting employee churn, identifying high-potential candidates, or even anticipating skill gaps. Recruiters can leverage predictive analytics to optimize sourcing strategies by identifying channels that yield the best candidates, forecast the success rate of new hires, or pinpoint the factors that contribute to longer retention, thereby making data-driven decisions that enhance talent acquisition and management.

Reinforcement Learning (RL)

Reinforcement Learning is an area of machine learning where an AI agent learns to make decisions by performing actions in an environment to maximize a cumulative reward. It learns through trial and error, similar to how humans learn from experience. While less common in mainstream HR tech today compared to other ML types, RL has potential in optimizing complex processes like workforce scheduling, dynamically adjusting recruitment campaigns based on real-time market feedback, or personalizing employee training paths to achieve specific skill development goals most efficiently.

Computer Vision (CV)

Computer Vision is a field of AI that enables computers to “see” and interpret visual information from images and videos, much like the human eye. In HR tech, CV can be applied in various ways, such as analyzing non-verbal cues in video interviews to assess engagement or confidence (though ethically sensitive), or verifying identity during remote onboarding processes. It can also be used in physical spaces to optimize office layouts or monitor safety protocols, offering a new dimension to how visual data can inform HR decisions and enhance operational efficiency.

Data Bias

Data bias occurs when the data used to train an AI or ML model is not representative of the real-world population or phenomenon it’s intended to model, leading the system to make unfair or inaccurate predictions. In HR tech, this is a critical concern, as biased training data (e.g., historical hiring records skewed by past human biases) can perpetuate or even amplify discrimination in resume screening, candidate ranking, or promotion recommendations. Addressing data bias requires careful data curation, rigorous testing, and ethical considerations to ensure AI systems promote fairness and equity in the workplace.

Ethical AI

Ethical AI refers to the principles and practices that guide the design, development, and deployment of AI systems to ensure they are fair, transparent, accountable, and beneficial to society. For HR tech, Ethical AI is paramount to prevent discrimination, protect privacy, and foster trust. It involves creating AI tools that explain their decisions, are regularly audited for bias, and empower human oversight. Adopting an Ethical AI framework is not just about compliance, but about building AI solutions that genuinely enhance human potential and create a more inclusive and productive work environment.

Algorithm

An algorithm is a set of well-defined instructions or rules designed to solve a problem or perform a computation. In the context of AI and ML, algorithms are the computational recipes that enable systems to learn from data, make predictions, classify information, or automate tasks. For HR, an algorithm might be used to rank job applicants based on specific criteria, match candidates to open roles, or predict employee flight risk. The performance and fairness of any AI system are directly tied to the design and implementation of its underlying algorithms.

Large Language Models (LLMs)

Large Language Models are advanced AI models trained on vast amounts of text data to understand, generate, and process human language with remarkable fluency and coherence. These models form the backbone of many Generative AI applications. In HR, LLMs can power sophisticated chatbots for candidate inquiries, automatically summarize long resumes or performance reviews, draft highly personalized email campaigns, or even assist in creating comprehensive training modules. Their ability to generate contextually relevant text significantly enhances automation for communication and content creation in recruiting and HR operations.

Deep Learning

Deep Learning is a specialized subfield of Machine Learning that uses artificial neural networks with multiple layers (hence “deep”) to learn from data. Inspired by the structure and function of the human brain, deep learning algorithms are particularly effective at identifying complex patterns in large datasets, such as those found in images, audio, and text. In HR, deep learning can power highly accurate resume parsing, advanced sentiment analysis from open-ended survey responses, or sophisticated prediction models for employee performance and retention, often outperforming traditional machine learning methods for complex, unstructured data.

Supervised Learning

Supervised Learning is a type of machine learning where an algorithm learns from labeled data—meaning each input data point is paired with its correct output. The algorithm uses this labeled data to discover a mapping function from inputs to outputs, which it then uses to make predictions on new, unseen data. In HR, an example would be training a model with historical data of successful hires (inputs) and their subsequent performance ratings (outputs) to predict the success of future candidates. This is a common approach for tasks like resume classification, candidate matching, and predicting employee turnover.

Unsupervised Learning

Unsupervised Learning is a type of machine learning where an algorithm learns from unlabeled data, meaning there are no pre-defined correct outputs. Instead, the algorithm identifies hidden patterns, structures, or groupings within the data on its own. In HR, unsupervised learning can be used to segment employee populations into distinct groups based on behavioral data, discover unknown correlations between different skill sets, or identify emerging trends in candidate profiles without prior knowledge of what those groups or trends might be. It’s particularly useful for exploratory data analysis and discovering novel insights.

Conversational AI

Conversational AI refers to technologies, like chatbots and voice assistants, that allow humans to interact with computers using natural language. These systems utilize NLP, ML, and often LLMs to understand user intent and generate human-like responses. In HR and recruiting, conversational AI applications include automated candidate screening chatbots that answer FAQs, guide applicants through processes, or conduct initial interviews. They can also assist employees with HR queries, provide onboarding information, or facilitate learning, significantly improving efficiency and providing instant support 24/7.

If you would like to read more, we recommend this article: Safeguarding Your Talent Pipeline: The HR Guide to CRM Data Backup and ‘Restore Preview’