AI Ethics
Accountability
Accountability is the obligation to accept responsibility for one’s actions and decisions, ensuring transparency and answerability.
AI Ethics
AI Alignment
AI Alignment is the process of ensuring that artificial intelligence systems act in ways that are consistent with human values, goals, and ethical standards.
AI Ethics
AI Governance
AI Governance is the framework of policies, processes, and controls designed to ensure the ethical, transparent, and responsible development and deployment of artificial intelligence systems.
AI Ethics
AI Regulation
AI Regulation is the set of laws, guidelines, and policies designed to govern the development, deployment, and use of artificial intelligence technologies.
AI Ethics
Algorithmic Bias
Algorithmic bias is the systematic and unfair discrimination embedded in computer algorithms that affects decision-making processes.
AI Ethics
Algorithmic Impact Assessment
Algorithmic Impact Assessment is a systematic process to evaluate the potential effects and risks of an algorithm before its deployment.
AI Ethics
Alignment Problem
Alignment Problem is the challenge of ensuring that artificial intelligence systems act according to human values and intentions.
AI Ethics
Asilomar AI Principles
The Asilomar AI Principles are a set of guidelines designed to promote the safe, ethical, and beneficial development of artificial intelligence.
AI Ethics
Autonomy (Ethics)
Autonomy (Ethics) is the principle that individuals have the right and capacity to make informed, independent decisions about their own lives.
AI Ethics
Beijing Academy of AI Principles
The Beijing Academy of AI Principles is a set of ethical guidelines developed to promote responsible and trustworthy artificial intelligence development and deployment.
AI Ethics
Beneficence
Beneficence is the ethical principle of doing good and promoting the well-being of others through positive actions.
AI Ethics
Certification
Certification is the formal process by which an individual, product, or organization is verified to meet specific standards or criteria set by an authoritative body.
AI Ethics
Compliance
Compliance is the act of adhering to laws, regulations, standards, and ethical practices applicable to a business or individual.
AI Ethics
Counterfactual Explanations
Counterfactual explanations are descriptions of how changing specific input features would alter the outcome of a decision-making model.
AI Ethics
Counterfactual Fairness
Counterfactual fairness is a fairness criterion in machine learning that ensures decisions remain unchanged in hypothetical scenarios where a protected attribute is altered.
AI Ethics
Data Exploitation
Data Exploitation is the process of extracting valuable insights and actionable information from raw data to support decision-making and business growth.
AI Ethics
Data Sheets
Data sheets are detailed documents that provide technical specifications, features, and performance information about a product or component.
AI Ethics
Demographic Parity
Demographic parity is a fairness criterion in machine learning where outcomes are independent of sensitive demographic attributes such as race or gender.
AI Ethics
Digital Divide
Digital Divide is the gap between individuals or communities that have access to modern information and communication technologies and those that do not.
AI Ethics
Disinformation
Disinformation is false or misleading information deliberately spread to deceive or manipulate an audience.
AI Ethics
Disparate Impact
Disparate Impact is a legal and social concept describing practices that affect one group more harshly than others, even if unintentionally.
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