Definition

The obligation and responsibility of the creators, operators and regulators of an AI system to ensure the system operates in a manner that is ethical, fair, transparent and compliant with applicable rules and regulations (see also fairness and transparency). Accountability ensures that actions, decisions and outcomes of an AI system can be traced back to the entity responsible for it.

*Different definition than IAPP privacy training glossary.

Definition

A subfield of AI and machine learning where an algorithm can select some of the data it learns from. Instead of learning from all the data it is given, an active learning model requests additional data points that will help it learn the best.

Also called query learning.

Definition

A system of policies, practices and processes organizations implement to manage and oversee their use of AI technology and associated risks to ensure the AI aligns with an organization's objectives, is developed and used responsibly and ethically, and complies with applicable legal requirements.

Definition

A computational procedure or set of instructions and rules designed to perform a specific task, solve a particular problem, or produce a machine learning or AI model (see also machine learning model).

Definition

AI that is considered to have human-level intelligence and strong generalization capability to achieve goals and carry out a variety of tasks in different contexts and environments. AGI is still considered a theoretical field of research and contrasted with "narrow" AI, which is used for specific tasks or problems.

Acronym

AGI

Definition

Artificial intelligence is a broad term used to describe an engineered system where machines learn from experience, adjusting to new inputs (see also input data), and potentially performing tasks previously done by humans. More specifically, it is a field of computer science dedicated to simulating intelligent behavior in computers. It may include automated decision-making.

Acronym

AI

Definition

The process of making a decision by technological means without human involvement.

Definition

There are several types of bias within the AI field. Computational bias is a systematic error or deviation from the true value of a prediction that originates from a model's assumptions or the data itself (see also input data). Cognitive bias refers to inaccurate individual judgment or distorted thinking, while societal bias leads to systemic prejudice, favoritism, and/or discrimination in favor of or against an individual or group. Bias can impact outcomes and pose a risk to individual rights and liberties.

Definition

A machine learning method that aggregates multiple versions of a model (see also machine learning model) trained on random subsets of a data set. This method aims to make a model more stable and accurate.

Sometimes referred to as bagging.

Definition

A form of AI designed to simulate human-like conversations and interactions that uses natural language processing to understand and respond to text or other media. Because chatbots are often used for customer service and other personal help applications, chatbots often ingest users’ personal information.

Definition

A type of model (see also machine learning model) used in machine learning that is designed to take input data and sort it into different categories or classes.

Definition

An unsupervised machine learning method where patterns in the data are identified and evaluated, and data points are grouped accordingly into clusters based on their similarity.

Definition

A field of AI that enables computers to process and analyze images, videos and other visual inputs.

Definition

An analysis, often performed by a third-party body, on an AI system to determine whether requirements, such as establishing a risk management system, data governance, record-keeping, transparency and cybersecurity practices have been met.

Definition

The principle of ensuring that AI systems and their decision-making processes can be questioned or challenged. This ability to contest or challenge the outcomes, outputs and/or actions of AI systems can help promote transparency (see also transparency) and accountability (see also accountability) within AI governance (see also AI governance).

Also called redress.

Definition

A large collection of texts or data that a computer uses to find patterns, make predictions or generate specific outcomes. The corpus may include structured or unstructured data and cover a specific topic or a variety of topics.

Definition

A type of supervised learning model used in machine learning (see also machine learning model) that represents decisions and their potential consequences in a branching structure.

Definition

A subfield of AI and machine learning that uses artificial neural networks. Deep learning is especially useful in fields where raw data needs to be processed, like image recognition, natural language processing and speech recognition.

Definition

A type of model (see also machine learning model) used in machine learning that directly maps input features to class labels and analyzes for patterns that can help distinguish between different classes. It is often used for text classification tasks, like identifying the language of a piece of text. Examples are traditional neural networks, decision trees and random forest.

Definition

The measure of unpredictability or randomness in a set of data used in machine learning. A higher entropy signifies greater uncertainty in predicting outcomes.

Definition

A form of AI that draws inferences from a knowledge base to replicate the decision-making abilities of a human expert within a specific field, like a medical diagnosis.

Definition

The ability to describe or provide sufficient information about how an AI system generates a specific output or arrives at a decision in a specific context to a predetermined addressee. XAI is important in maintaining transparency and trust in AI.

Acronym

XAI

Definition

Data discovery process techniques that take place before training a machine learning model in order to gain preliminary insights into a data set, such as identifying patterns, outliers, and anomalies and finding relationships among variables.

Definition

An attribute of an AI system that ensures equal and unbiased treatment of individuals or groups in its decisions and actions in a consistent, accurate manner. It means the AI system's decisions should not be affected by certain sensitive attributes like race, gender or religion.

*Different definition than IAPP privacy training glossary.

Definition

A machine learning method that allows models (see also machine learning model) to be trained on the local data of multiple edge devices or servers. Only the updates of the local model, not the training data itself, are sent to a central location where they get aggregated into a global model — a process that is iterated until the global model is fully trained.

Definition

A large-scale, pretrained model for AI capabilities, such as language (see also large language model), vision, robotics, reasoning, search or human interaction, that can function as the base for other applications. The model is trained on extensive and diverse data sets.

Definition

The ability of a model (see also machine learning model) to understand the underlying patterns and trends in its training data and apply what it has learned to make predictions or decisions about new, unseen data.

Definition

A field of AI that uses machine learning models trained on large data sets to create new content, such as written text, code, images, music, simulations and videos. These models are capable of generating novel outputs based on input data or user prompts.

Definition

A type of algorithm that makes the optimal choice to achieve an immediate objective at a particular step or decision point, based on the available information and without regard for the longer-term optimal solution.

Definition

Instances where a generative AI model creates content that either contradicts the source or creates factually incorrect output under the appearance of fact.

Definition

A type of machine learning process where a trained model (see also machine learning model) is used to make predictions or decisions based on input data.

Definition

Data provided to or directly acquired by a learning algorithm or model (see also machine learning model) for the purpose of producing an output. It forms the basis upon which the machine learning model will learn, make predictions and/or carry out tasks.

Definition

A form of AI that utilizes deep learning algorithms to create models (see also machine learning model) trained on massive text data sets to analyze and learn patterns and relationships among characters, words and phrases. There are generally two types of LLMs: generative models that make text predictions based on the probabilities of word sequences learned from its training data (see also generative AI) and discriminative models that make classification predictions based on probabilities of data features and weights learned from its training data (see also discriminative model). The term "large" generally refers to the model's capacity measured by the number of parameters.

Acronym

LLM

Definition

A subfield of AI involving algorithms that enable computer systems to iteratively learn from and then make decisions, inferences or predictions based on data (see also input data). These algorithms build a model from training data to perform a specific task on new data without being explicitly programmed to do so.

Machine learning implements various algorithms that learn and improve by experience in a problem-solving process that includes data cleansing, feature selection, training, testing and validation. Companies and government agencies deploy machine learning algorithms for tasks such as fraud detection, recommender systems, customer inquiries, natural language processing, health care, or transport and logistics.

Acronym

ML

Definition

A learned representation of underlying patterns and relationships in data, created by applying an AI algorithm to a training data set. The model can then be used to make predictions or perform tasks on new, unseen data.

Definition

A type of model used in machine learning (see also machine learning model) that can process more than one type of input or output data, or 'modality,' at the same time. For example, a multi-modal model can take both an image and text caption as input and then produce a unimodal output in the form of a score indicating how well the text caption describes the image. These models are highly versatile and useful in a variety of tasks, like image captioning and speech recognition.

Definition

A subfield of AI that helps computers understand, interpret and manipulate human language by transforming information into content. It enables machines to read text or spoken language, interpret its meaning, measure sentiment, and determine which parts are important for understanding.

Definition

A type of model (see also machine learning model) used in machine learning that mimics the way neurons in the brain interact with multiple processing layers, including at least one hidden layer. This layered approach enables neural networks to model complex nonlinear relationships and patterns within data. Artificial neural networks have a range of applications, such as image recognition and medical diagnosis.

Definition

A concept in machine learning in which a model (see also machine learning model) becomes too specific to the training data and cannot generalize to unseen data, which means it can fail to make accurate predictions on new data sets.

Definition

The process of effectively monitoring and supervising an AI system to minimize risks, ensure regulatory compliance and uphold responsible practices. Oversight is important for effective AI governance, and mechanisms may include certification processes, conformity assessments and regulatory authorities responsible for enforcement.

Definition

Steps performed after a machine learning model has been run to adjust the output of that model. This can include adjusting a model's outputs and/or using a holdout data set—data not used in the training of the model—to create a function that is run on the model's predictions to improve fairness or meet business requirements.

Definition

Steps taken to prepare data for a machine learning model, which can include cleaning the data, handling missing values, normalization, feature extraction and encoding categorical variables. Data preprocessing can play a crucial role in improving data quality, mitigating bias, addressing algorithmic fairness concerns, and enhancing the performance and reliability of machine learning algorithms.

Definition

A supervised machine learning (see also supervised learning) algorithm that builds multiple decision trees and merges them together to get a more accurate and stable prediction. Each decision tree is built with a random subset of the training data (see also bootstrap aggregating), hence the name "random forest.” Random forests are helpful to use with data sets that are missing values or very complex.

Definition

A machine learning method that trains a model to optimize its actions within a given environment to achieve a specific goal, guided by feedback mechanisms of rewards and penalties. This training is often conducted through trial-and-error interactions or simulated experiences that do not require external data. For example, an algorithm can be trained to earn a high score in a video game by having its efforts evaluated and rated according to success toward the goal.

Definition

An attribute of an AI system that ensures it behaves as expected and performs its intended function consistently and accurately, even with new data that it has not been trained on.

Definition

A multidisciplinary field that encompasses the design, construction, operation and programming of robots. Robotics allow AI systems and software to interact with the physical world.

Definition

An attribute of an AI system that ensures a resilient system that maintains its functionality and performs accurately in a variety of environments and circumstances, even when faced with changed inputs or an adversarial attack.

Definition

The development of AI systems that are designed to minimize potential harm to individuals, society, property and the environment.

Definition

A subset of machine learning where the model (see also machine learning model) is trained on input data with known desired outputs. These two groups of data are sometimes called predictors and targets, or independent and dependent variables, respectively. This type of learning is useful for training an AI to group data into specific categories or making predictions by understanding the relationship between two variables.

Definition

Data generated by a system or model (see also machine learning model) that can mimic and resemble the structure and statistical properties of real data. It is often used for testing or training machine learning models, particularly in cases where real-world data is limited, unavailable or too sensitive to use.

Definition

A subset of the data set used to provide an unbiased evaluation of a final model (see also machine learning model). It is used to test the performance of the machine learning model with new data at the very end of the model development process.

Definition

A subset of the data set that is used to train a model (see also machine learning model) until it can accurately predict outcomes, find patterns or identify structures within the training data.

Definition

A type of model (see also machine learning model) used in machine learning in which an algorithm learns to perform one task, such as recognizing cats, and then uses that learned knowledge as a basis when learning a different but related task, such as recognizing dogs.

Definition

The extent to which information regarding an AI system is made available to stakeholders, including if one is used and an explanation of how it works. It implies openness, comprehensibility and accountability in the way AI algorithms function and make decisions.

*Different definition than IAPP privacy training glossary.

Definition

In most cases used interchangeably with the terms responsible AI and ethical AI, which all refer to principle-based AI development and governance (see also AI governance), including the principles of security, safety, transparency, explainability, accountability, privacy, nondiscrimination/nonbias (see also bias), among others.

Definition

A test of a machine's ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human. Alan Turing (1912-1954) originally thought of the test to be an AI's ability to converse through a written text.

Definition

A concept in machine learning in which a model (see also machine learning model) fails to fully capture the complexity of the training data. This may result in poor predictive ability and/or inaccurate outputs. Factors leading to underfitting may include too few model parameters or epochs, having too high a regularization rate, or using an inappropriate or insufficient set of features in the training data.

Definition

A subset of machine learning where the model is trained by looking for patterns in an unclassified data set with minimal human supervision. The AI is provided with preexisting data sets and then analyzes those data sets for patterns. This type of learning is useful for training an AI for techniques such as clustering data (outlier detection, etc.) and dimensionality reduction (feature learning, principal component analysis, etc.).

Definition

A subset of the data set used to assess the performance of the model (see also machine learning model) during the training phase. Validation data is used to fine-tune the parameters of a model and prevent overfitting before the final evaluation using the test data set.

Definition

In the context of machine learning, a variable is a measurable attribute, characteristic or unit that can take on different values. Variables can be numerical/quantitative or categorical/qualitative.

Definition

A statistical measure that reflects how far a set of numbers are spread out from their average value in a data set. A high variance indicates that the data points are spread widely around the mean. A low variance indicates the data points are close to the mean. In machine learning, higher variance can lead to overfitting. The trade-off between variance and bias is a fundamental concept in machine learning. Model complexity tends to reduce bias but increase variance. Decreasing complexity reduces variance but increases bias.