Navigating the world of AI hiring? Here is your definitive guide to the jargon, technology, and science driving the future of work.
An artificial intelligence system designed to conduct automated job interviews. It uses Natural Language Processing (NLP) to understand candidate responses and ask contextual follow-up questions.
Software used by recruiters to collect, sort, and manage job applications. Modern ATS integration allows AI tools to push scores directly into candidate profiles.
A theoretical form of AI that possesses the ability to understand, learn, and apply knowledge across a wide variety of tasks, similar to human intelligence.
A screening method where candidates record video answers to pre-set questions. AI analyzes these for content and tone, allowing recruiters to review them at any time.
The process of using machine learning to parse and rank resumes based on job descriptions, eliminating manual review time.
Techniques used in AI development to reduce human prejudice. This includes training on diverse datasets and redacting PII (Personally Identifiable Information) before analysis.
The overall perception a job seeker has of an employer based on their interaction during the hiring process. Fast, transparent AI interviews often improve this metric.
The process of attributing code segments to specific authors or timeframes. In interviews, it helps track how a candidate's solution evolved over time.
A technique used by recommendation systems (and some hiring AIs) to predict a user's preference (or a candidate's fit) based on data from many similar users.
A field of AI that enables computers to interpret and make decisions based on visual data. Used in proctoring to detect suspicious behavior during remote tests.
Organizational frameworks which seek to promote fair treatment and full participation of all people. AI tools can support DEI by removing unconscious bias.
Artificial intelligence where the results of the solution can be understood by humans. Essential in hiring to justify why a candidate was scored a certain way.
AI that can generate new content, including text, code, audio, and images. Used in generating unique interview questions or coding challenges on the fly.
A phenomenon where an AI generates incorrect or nonsensical information confidently. Rigorous prompt engineering is required to prevent this in hiring contexts.
A model where human interaction is required to train, tune, or test the AI. In hiring, this often means a human recruiter makes the final decision based on AI recommendations.
The scientific study of human behavior in the workplace. Expert Hire uses I-O principles to design valid and reliable assessments.
A deep learning algorithm that can recognize, summarize, translate, predict, and generate text. The backbone of modern conversational AI interviewers.
A branch of AI that helps computers understand, interpret, and manipulate human language. Key for analyzing candidate's spoken responses.
The use of data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes, such as a candidate's future job performance.
Monitoring a candidate during a test to prevent cheating. AI proctoring tracks eye movement, tab switching, and background audio.
A technique that optimizes the output of an LLM by referencing an authoritative knowledge base before generating a response. Ensures interview questions are technically accurate.
The process of ensuring the emotional tone behind a series of words is captured. Used to gauge candidate confidence and enthusiasm.
A standardized method of comparing job candidates where they are asked the same questions in the same order. AI excels at enforcing this consistency.
The process of breaking down text into smaller units (tokens). This is the first step in how an LLM processes a candidate's resume or transcript.
The ability of a model to solve a task despite not having received any specific training examples of that task. Allows AI to assess novel roles without massive prior datasets.