AI 용어집
인공지능 완전 사전
Algorithmic Fairness
Principles and methods to ensure that AI systems treat all individuals fairly and without discrimination.
Transparency and Interpretability
Ability to understand and explain AI model decisions to ensure trust and accountability.
Data Bias
Identification and correction of distortions present in training datasets that can affect the fair performance of models.
Audit Algorithmique
Processus systématique d'évaluation des systèmes IA pour détecter les biais et vérifier leur conformité éthique.
Responsibility and Accountability
Mechanisms for assigning responsibility for automated decisions and ensuring recourse in case of error or harm.
Privacy and Data Protection
Techniques and policies to protect personal information in AI systems while maintaining their efficiency.
Procedural Justice
Study of the equities of algorithmic decision-making processes beyond just the final outcomes.
Ethics by Design
Proactive integration of ethical considerations from the design phase of AI systems.
Algorithmic Discrimination
Analysis of the mechanisms by which AI systems can perpetuate or amplify existing inequalities.
AI Governance
Regulatory and policy frameworks to oversee the responsible development and deployment of AI technologies.
Ethical Impact Assessment
Methodologies for anticipating and evaluating the ethical consequences of AI systems before their deployment.
Ethical Robustness
Ability of AI systems to maintain ethical behaviors in the face of adversarial attacks or unforeseen conditions.
Digital Rights and AI
Study of the implications of AI on the fundamental rights of individuals in the digital environment.
Ethical Monitoring
Continuous monitoring mechanisms to ensure the maintenance of ethical standards throughout the lifecycle of AI systems.
Model Bias
Identification and mitigation of biases introduced by the learning architectures and algorithms themselves.