In-processing - In-processing is a family of fairness-mitigation approaches that change how a machine-learning model is trained.
Incident - An incident is an event or connected set of events that meets an organization's or framework's criteria for requiring investigation and response because...
Incident response - Incident response is the coordinated work of investigating an incident, limiting its effects, addressing the problem, and restoring an acceptable operating...
Incompatibility of fairness metrics - Incompatibility of fairness metrics means that some mathematical fairness conditions cannot all be satisfied simultaneously under particular data and...
Independence - Independence, in probability, means that knowing one event or variable does not change the probability information about another.
Individual Bias - Individual Bias is a persistent personal viewpoint, or a narrow set of viewpoints, that influences how someone considers information and makes judgments.
Individual fairness - Individual fairness is the principle that people who are similar in ways relevant to a particular task should receive similar treatment from a decision system.
Inference optimization - Inference optimization improves how a trained machine-learning model runs when producing outputs.
Information security - Information security is the protection of information and the systems that handle it against unauthorized access, disclosure, alteration, disruption, or...
Inherited Bias - Inherited Bias arises when the output of one machine-learning system becomes input for another and carries an existing bias into the downstream learning...
Input data - Input data is the information an AI system receives to produce a prediction or other inference.
Insider attack - An insider attack is a harmful action that exploits trusted access or an internal position to misuse an organization's information, systems, or resources.
Institutional Bias - Institutional Bias is a pattern in an institution's practices, rules, or norms that favors or disadvantages particular social groups.
Integrity - Integrity is the information-security property of protecting information and systems against improper modification or destruction.
Intended interpretation - An intended interpretation is the meaning that a knowledge-base designer wants the symbols in a formal representation to have.
Internal validity - Internal validity is the extent to which a study supports a trustworthy conclusion about the relationship it tests, especially a claim that one factor...
Interoperability - Interoperability is the ability of different systems or components to communicate and work together for an intended purpose.
Interpretability - Interpretability is the extent to which people can understand an AI model's behavior or the basis of its outputs in terms meaningful to them.
Interpretable model - An interpretable model is a model whose operation people can understand sufficiently for a particular purpose.
Interpretation - An interpretation, in formal logic, assigns meanings to the symbols of a language so that statements can be evaluated as true or false.
Interpretation Bias - Interpretation Bias is the influence of a person's views or assumptions on how they understand information.
Intervenability - Intervenability is the practical ability to step into an AI-supported process and change its course when needed.
Label shift - Label shift is a change in how common the target classes are between datasets or operating settings, while the distribution of inputs within each class...
Learning - Learning, in AI, is the process through which a system uses experience or data to develop or improve its ability to perform a task.
Least privilege - Least privilege is the security principle of giving a person, account, or software component only the access and capabilities needed for its function.
Lemmatization - Lemmatization is the process of mapping a word form to its dictionary-style base form, called a lemma.
Lift curve - A lift curve shows how effectively a model concentrates positive cases compared with random selection as the selected fraction of a dataset changes.
Linking Bias - Linking bias describes systematic differences in the networks inferred from people's recorded connections, interactions or activity.
Local interpretable model-agnostic explanations - Local Interpretable Model-agnostic Explanations, usually called LIME, is a method for explaining an individual model prediction by fitting a simpler...
Loss Of Situational Awareness Bias - Loss of situational awareness bias is a source-taxonomy term for a problem in which automation leaves people insufficiently aware of the current situation...
Machine ethics - Machine ethics is the field concerned with how artificial systems can represent ethical considerations and use them to guide their behavior.
Machine learning model metadata - Machine learning model metadata is information describing a model and its relevant characteristics.
Machine learning overlay - A machine learning overlay is an architectural arrangement that integrates ML functions with telecommunications network functions through standardized...
Machine learning profile - A machine learning profile, in ITU-T Y.3187, is the interpreted content of an ML intent together with relevant runtime updates.
Machine learning sandbox - A machine learning sandbox is a controlled environment for training or testing ML models and evaluating their effects.
Machine learning underlay network - A machine learning underlay network is the telecommunications network, together with its network functions, that interacts with an ML overlay.
Malware - Malware is software or related executable logic deliberately designed to perform harmful or unauthorized activity.
Measurement Bias - Measurement bias is systematic distortion in how a quantity is recorded or represented.
Membership inference attack - A membership inference attack attempts to determine whether a particular record was included in a machine-learning model's training data.
Mixed methods - Mixed methods is a research approach that deliberately integrates quantitative and qualitative evidence to answer a question.
Mode Confusion Bias - Mode confusion bias occurs when a person misunderstands a system's active operating mode and takes an action that would be appropriate in a different mode.
Model Governance - Model governance is the set of responsibilities, policies and decision processes used to oversee models throughout their lifecycle.
Model Interpretability - Model interpretability is the extent to which people can understand relevant aspects of a model's behavior and the meaning of its outputs.
Model poisoning - Model poisoning is a deliberate attack that corrupts a model through control over its parameters, updates or other model-level components.
Model risk management - Model risk management is the process of identifying, assessing, reducing and monitoring risks arising from models and their use.
Model Selection Bias - Model selection bias is distortion introduced when a model is chosen using data and the chosen result is interpreted without accounting for that selection.
Multi-level security - Multi-level security is an approach to processing information with different security classifications and categories while allowing authorized users...
Non-response bias - Non-response bias occurs when people who provide data differ in relevant ways from those invited or eligible to respond who do not.