A-Z Index
Browse concept cards whose titles begin with P. This is useful when you want an alphabetical view of the library rather than browsing by governance topic or category.
Personal Governance Judgement and Responsibility refers to the individual accountability of AI practitioners and stakeholders in making ethical decisions regarding AI systems. This...
Planning for Sustainable Compliance at Scale refers to the strategic approach organizations must adopt to ensure that their AI systems adhere to regulatory requirements and ethical...
Policy Process Control and Evidence Layers refer to the structured methodologies and frameworks that ensure AI systems comply with established policies and regulations throughout t...
Principle-based AI policies focus on broad ethical guidelines and values, allowing organizations flexibility in implementation, while rule-based policies provide specific, detailed...
Principles of Effective AI Governance Frameworks refer to the foundational guidelines that ensure AI systems are developed and deployed responsibly, ethically, and transparently. T...
Prioritising Remediation Actions involves systematically identifying and addressing risks and issues within AI systems based on their severity and potential impact. In AI governanc...
Proactive vs Reactive Compliance Postures refer to the strategic approaches organizations adopt in ensuring adherence to AI regulations and ethical standards. A proactive posture i...
Proportionality in AI Governance refers to the principle that the measures taken in regulating AI should be appropriate and not excessive in relation to the risks posed by the tech...
Providing assurance to multiple regulators involves demonstrating compliance with various regulatory frameworks governing AI systems. This is crucial in AI governance as it ensures...
Providing Defensible Expert Recommendations involves the systematic process of synthesizing expert knowledge and data to formulate actionable guidance in AI governance. This concep...
The purpose of AI governance is to establish frameworks, policies, and practices that ensure the responsible development and deployment of artificial intelligence technologies. It...
The purpose of internal AI policies is to establish a framework that governs the development, deployment, and use of AI technologies within an organization. These policies are cruc...
Personal data in cross-border AI systems refers to the handling, processing, and transfer of personal information across national borders within AI applications. This concept is cr...
Personal data refers to any information that relates to an identified or identifiable individual, such as names, email addresses, and biometric data. Non-personal data, on the othe...
Preparing Governance for Regulatory Uncertainty involves establishing frameworks and practices that enable organizations to adapt to evolving AI regulations and policies. This conc...
The processing of personal data refers to any operation performed on personal data, including collection, storage, use, and sharing. In AI governance, this concept is crucial as it...
Prohibited AI Practices refer to specific actions or applications of artificial intelligence that are deemed unethical, harmful, or illegal under regulatory frameworks. These pract...
Prohibited AI Practices refer to specific activities and applications of artificial intelligence that are deemed unacceptable under regulatory frameworks, such as the EU AI Act. Th...
The EU AI Act aims to establish a regulatory framework for artificial intelligence within the European Union, focusing on ensuring that AI systems are safe, ethical, and respect fu...
The General Data Protection Regulation (GDPR) is a comprehensive data protection law in the European Union that governs how personal data is collected, processed, and stored. In th...
Purpose Limitation is a principle in AI governance that mandates data collected for a specific purpose should not be used for unrelated purposes without consent. This principle is...
Planning for Risk Evolution and Accumulation involves anticipating and managing the dynamic nature of risks associated with AI systems over time. This concept is crucial in AI gove...
Portfolio-Level AI Risk Management refers to the systematic assessment and management of risks associated with multiple AI projects within an organization. This approach is crucial...
Prioritising Risks Under Resource Constraints refers to the strategic approach of identifying, assessing, and managing risks associated with AI systems when limited resources (fina...
Protected attributes refer to characteristics such as race, gender, age, or disability that should not unfairly influence AI decision-making processes. Sensitive inference involves...
AI Impact Assessments (AIAs) are systematic evaluations that analyze the potential effects of AI systems on individuals, society, and the environment. They are crucial in AI govern...
Preparing for Future Enforcement Scenarios involves developing frameworks and strategies to effectively enforce AI regulations and standards as technology evolves. This concept is...
Preparing Governance for Scrutiny You Cannot Predict refers to the proactive establishment of governance frameworks that can withstand unforeseen challenges and scrutiny in AI syst...
The purpose of transparency in AI governance is to ensure that the processes, decisions, and underlying algorithms of AI systems are open and understandable to stakeholders, includ...
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