.svg)
Thanks for signing up! You have been successfully added to our mailing list.
Understand key AI governance terms and concepts with straightforward definitions to help you navigate the Enzai platform.
The process of comparing current AI governance practices against desired standards or regulations to identify areas needing improvement.
An AI model’s ability to perform well on new, unseen data by capturing underlying patterns rather than memorizing training examples.
AI techniques (e.g., GANs, transformers) that create new content—text, images, or other media—often raising novel governance and IP concerns.
A consolidated AI model trained on aggregated data from multiple sources, as opposed to localized or personalized models.
The set of policies, procedures, roles, and responsibilities that guide the ethical, legal, and effective development and deployment of AI systems.
A cross-functional group (e.g., legal, ethics, technical) tasked with overseeing AI governance policies and their execution within an organization.
A structured model outlining how AI governance components (risk management, accountability, oversight) fit together to ensure compliance and ethical use.
A staged framework that assesses how advanced an organization’s AI governance practices are, from ad-hoc to optimized.
A formal document that codifies rules, roles, and procedures for AI development and oversight within an organization.
A dashboard or report card that tracks key metrics (e.g., bias incidents, compliance audits) to measure AI governance effectiveness over time.
An optimization algorithm that iteratively adjusts model parameters in the direction that minimally decreases the loss function.
A data-privacy approach allowing individuals to grant or deny specific permissions for each type of data use, enhancing transparency and control.
A model whose internal logic is partially transparent (some components interpretable, others opaque), balancing performance and explainability.
The accurate, real-world data or labels used as a benchmark to train and evaluate AI model performance.
Predefined constraints or checks (technical and policy) embedded in AI systems to prevent unsafe or non-compliant behavior at runtime.
A non-binding recommendation or best-practice document issued by organizations (e.g., IEEE, EU) to shape responsible AI development and deployment.
When generative AI produces incorrect or fabricated information that appears plausible but has no basis in the training data.
Techniques (e.g., imputation, deletion, modeling) for addressing gaps in datasets to maintain model integrity and fairness.
Specialized chips (e.g., GPUs, TPUs) designed to speed up AI computations, with implications for energy use and supply chain risk.
Evaluating potential negative impacts (physical, psychological, societal) of AI systems and defining mitigation strategies.
Aligning AI policies, standards, and regulations across jurisdictions to reduce conflicts and enable interoperability.
Combining data of different types (text, image, sensor) or from multiple domains, which poses integration and governance challenges.
A usability inspection method where experts judge an AI system against established usability principles to identify potential issues.
AI applications whose failures could cause significant harm (e.g., medical diagnosis, autonomous vehicles), requiring heightened governance and oversight.
Mechanisms that allow designated individuals to monitor, intervene, or override AI system decisions to ensure ethical and legal compliance.
A process to evaluate how AI systems affect fundamental rights (privacy, expression, non-discrimination) and identify mitigation measures.
Involving human judgment within AI processes (training, validation, decision review) to improve accuracy and accountability.
AI systems combining multiple learning paradigms (e.g., symbolic and neural) to balance explainability and performance.
A configuration variable (e.g., learning rate, tree depth) set before model training that influences learning behavior and performance.
The process of searching for the optimal hyperparameter values (e.g., via grid search, Bayesian optimization) to maximize model performance.
The joint ISO/IEC committee on Artificial Intelligence standardization, developing international AI standards for governance, risk, and interoperability.
A dataset where one class or category significantly outnumbers others, which can lead AI models to bias toward the majority class unless mitigated.
A tamper-evident record-keeping mechanism (e.g., blockchain) ensuring that once data are written, they cannot be altered without detection—useful for AI audit trails.
A structured evaluation to identify, analyze, and mitigate potential ethical, legal, and societal impacts of an AI system before deployment.
Unconscious or unintentional biases embedded in training data or model design that can lead to discriminatory outcomes.
The design of reward structures and objectives so that AI systems’ goals remain consistent with human values and organizational priorities.
The set of assumptions a learning algorithm uses to generalize from observed data to unseen instances.
The component of an AI system (often in rule-based or expert systems) that applies a knowledge base to input data to draw conclusions.
The policies, procedures, and controls that ensure data quality, privacy, and usability across an organization’s data assets, including AI training datasets.
The right of individuals to control how their personal data are collected, used, stored, and shared by AI systems.
Managing and provisioning AI infrastructure (compute, storage, networking) through machine-readable configuration files, improving repeatability and auditability.
The ability of diverse AI systems and components to exchange, understand, and use information seamlessly, often via open standards or APIs.
The degree to which a human can understand the internal mechanics or decision rationale of an AI model.
Monitoring AI infrastructure and applications for malicious activity or policy violations, triggering alerts or automated responses.
In AI explainability, the matrix of all first-order partial derivatives of a model’s outputs w.r.t. its inputs, used to assess sensitivity and feature importance.
A type of prompt‐injection where users exploit vulnerabilities to bypass safeguards in generative AI models, potentially leading to unsafe or unauthorized outputs.
Legal principle where multiple parties (e.g., developers, deployers) share responsibility for AI‐related harms, influencing contract and governance structures.
Building AI systems that jointly learn multiple tasks (e.g., speech recognition + translation), with governance needed for complexity and auditability.
Systematic errors in human or AI decision‐making processes caused by cognitive shortcuts or flawed data, requiring bias audits and mitigation.
The legal process by which courts evaluate the lawfulness of decisions made or assisted by AI, ensuring accountability and due process.
The legal authority over data, AI operations, and liability, which varies by geography and impacts compliance with regional regulations (e.g., GDPR, CCPA).
The use of AI to assist in jury selection or case analysis, raising ethical concerns around fairness, transparency, and legal oversight.
Quantitative measures (e.g., disparate impact, equal opportunity) used to assess fairness and nondiscrimination in AI decision‐making.
A quantifiable metric (e.g., model accuracy drift, bias remediation time) used to monitor and report on AI governance and compliance objectives.
A leading metric (e.g., frequency of out-of-scope predictions, rate of unexplainable decisions) that signals emerging AI risks before they materialize.
Compliance processes to verify the identity, risk profile and legitimacy of individuals or entities interacting with AI systems, especially in regulated industries.
A method of transferring insight from a larger “teacher” model into a smaller “student” model, balancing performance with resource and governance constraints.
A structured representation of entities and their relationships used to improve AI explainability, auditability and alignment with domain ontologies.
Practices and tools for capturing, organizing and sharing organizational knowledge (e.g., model documentation, audit logs) to ensure reproducibility and oversight.
The inadvertent inclusion of output information in training data labels, which can inflate performance metrics and conceal true model generalization issues.
A deep learning model trained on vast text corpora that can perform tasks like text generation, translation, and summarization, often requiring governance around bias and misuse.
A security principle where AI components and users are granted only the minimal access rights necessary to perform their functions, reducing risk of misuse.
The practice of ensuring AI systems adhere to applicable laws, regulations, and industry standards throughout their entire lifecycle.
A structured approach defining who is responsible for AI-related harms or failures, including developers, deployers, and operators.
The coordinated processes for development, deployment, monitoring, maintenance, and retirement of AI systems to ensure ongoing compliance and risk control.
Techniques used to verify that an input (e.g., biometric) originates from a live subject rather than a spoof or replay, enhancing system security and integrity.
Adapting AI systems to local languages, regulations, cultural norms, and data residency requirements in different jurisdictions.
The collection, storage, and analysis of system and application logs from AI workflows to support auditing, incident response, and model performance tracking.
A mathematical function that quantifies the difference between predicted outputs and true values, guiding model training and optimization.
The practice of capturing and maintaining descriptive data (e.g., data provenance, feature definitions, model parameters) to support traceability and audits.
Quantitative measures (e.g., accuracy drift, fairness scores, incident response time) used to monitor AI system health, risk, and compliance objectives.
Planned actions (e.g., bias remediation, retraining, feature re-engineering) to address identified AI risks and compliance gaps.
Techniques and documentation that make an AI model’s decision logic understandable to stakeholders and auditors.
The policies, roles, and controls that ensure AI models are developed, approved, and used in line with organizational standards and regulatory requirements.
Continuous tracking of an AI model’s performance, data drift, and operational metrics to detect degradation or emerging risks.
The process of updating an AI model with new or refreshed data to maintain performance and compliance as data distributions evolve.
The structured process of identifying, assessing, and mitigating risks arising from AI/ML models throughout their lifecycle.
The evaluation activities (e.g., testing against hold-out data, stress scenarios) that confirm an AI model meets its intended purpose and performance criteria.
Involving diverse groups (e.g., legal, ethics, operations, end users) in AI governance processes to ensure balanced risk oversight and alignment with business goals.
A voluntary guidance from the U.S. National Institute of Standards and Technology outlining best practices for mitigating risks across AI system lifecycles.
Techniques and tools that enable machines to interpret, generate, and analyze human language in text or speech form.
Measures and controls (e.g., segmentation, firewalls, intrusion detection) to protect AI infrastructure and data pipelines from unauthorized access or tampering.
Automated methods for designing and optimizing neural network structures to improve model performance while balancing complexity and resource constraints.
Deliberate introduction of random perturbations into training data or model parameters to enhance robustness and guard against adversarial manipulation.
Techniques for identifying inputs or scenarios that differ significantly from training data, triggering review or safe-mode operation to prevent unexpected failures.
The capability to infer an AI system’s internal state and behavior through collection and analysis of logs, metrics, and outputs for effective monitoring and troubleshooting.
Continuous tracking of AI system performance, data drift, bias metrics, and security events to detect and address emerging risks over time.
The ability of AI systems and their supporting infrastructure to anticipate, withstand, recover from, and adapt to disruptions or adverse events.
The automated coordination of AI workflows and services—data ingestion, model training, deployment—ensuring compliance with policies and resource governance.
Techniques to identify data points or model predictions that deviate significantly from expected patterns, triggering review or mitigation actions.
A modeling issue where an AI system learns noise or idiosyncrasies in training data, reducing its ability to generalize to new, unseen data.
The management of user and system access rights to AI data and functions, ensuring least-privilege and preventing unauthorized use.
A limited-scope trial of an AI system in a controlled environment to assess performance, risks, and governance controls before full-scale deployment.