Compliance on English AI Terms Dictionary

Data Minimization

Sat, 18 Jul 2026 10:20:32 +0000

Definition

Data minimization is a core privacy principle requiring organizations to limit data collection to what is adequate, relevant, and limited to what is necessary. In AI, this means designing models that do not require excessive personal information to function accurately. It reduces privacy risks, limits exposure during breaches, and ensures compliance with regulations like GDPR by preventing the accumulation of unnecessary sensitive data.

Summary

The principle of collecting and processing only the personal data that is strictly necessary for a specific purpose.

Responsible AI

Sat, 18 Jul 2026 10:14:07 +0000

Definition

Responsible AI encompasses principles and practices aimed at mitigating the risks associated with artificial intelligence. It involves auditing models for bias, ensuring explainability of decisions, protecting user data privacy, and establishing clear accountability for AI outcomes. The goal is to build trust and align AI technologies with human values and societal norms, preventing harm and promoting equitable benefits across diverse populations.

Summary

A framework ensuring AI systems are developed and deployed ethically, focusing on fairness, transparency, accountability, and safety.

Citation

Sat, 18 Jul 2026 09:49:31 +0000

Definition

As generative AI models produce content, the need for citation mechanisms has emerged to maintain academic integrity and legal compliance. This involves embedding references to original sources within AI-generated outputs, allowing users to verify claims and trace information back to its origin. Advanced systems are being developed to automatically generate bibliographies or highlight quoted segments, addressing issues of hallucination and copyright infringement in knowledge-intensive applications like research assistants.

Summary

Citation in AI refers to the practice of attributing source material or data used within generated text or models to ensure transparency and intellectual property compliance.

Audit

Sat, 18 Jul 2026 09:47:03 +0000

Definition

An AI audit involves a rigorous, structured review of machine learning models and their deployment pipelines. It assesses fairness, transparency, accountability, and security to identify potential biases or risks. Audits are critical for maintaining trust with stakeholders and regulators, ensuring that automated decisions do not violate legal or moral guidelines. This process often includes testing datasets, reviewing algorithmic logic, and evaluating impact on affected populations.

Summary

A systematic evaluation of AI systems to ensure compliance with ethical standards, regulatory requirements, and performance benchmarks.

Data Protection

Sat, 18 Jul 2026 09:40:26 +0000

Definition

Data Protection encompasses legal, technical, and organizational measures designed to secure personal and proprietary data against breaches and misuse. In AI, this includes implementing encryption, access controls, and anonymization techniques to comply with regulations like GDPR. It ensures that training data and user interactions remain private and secure, fostering trust and ethical responsibility in AI systems.

Summary

The process of safeguarding sensitive information from unauthorized access, corruption, or theft throughout its lifecycle.

Privacy

Sat, 18 Jul 2026 09:36:45 +0000

Definition

In artificial intelligence, privacy refers to the protection of sensitive user information from unauthorized access or misuse during data collection, model training, and inference phases. It involves implementing technical safeguards like differential privacy and federated learning to ensure that individual identities cannot be reverse-engineered from aggregated datasets or model outputs, thereby maintaining trust and regulatory compliance.

Summary

The right of individuals to control how their personal data is collected, used, and shared within AI systems.