Design and manage responsible AI frameworks. Covers ethical risk management, bias mitigation, transparency, and compliance with NIST and ISO/IEC 42001.
Answer : A formal evaluation of an AI system's design, data, and outcomes to check for bias, safety, and compliance with ethical standards.
Internal and external audits are becoming a standard requirement under new laws like the EU AI Act.
Answer : Ensuring that an AI model produces equitable results for all groups, regardless of characteristics like race, gender, or age.
Requires ongoing monitoring to identify and fix discriminatory patterns in model outputs.
Answer : The EU AI Act.
The world's first comprehensive legal framework for AI, classifying systems based on their risk level.
Answer : Verifying that the copyright holders or individuals whose data is used for training have given clear and aware permission for that use.
Major legal battleground for Generative AI companies regarding artists' and writers' data.
Answer : Integrating data protection measures (like anonymization) at the very start of the development process rather than as an after-thought.
A core requirement of GDPR that applies directly to machine learning pipelines.