Results for "accountability"
Algorithmic Accountability
IntermediateEnsuring decisions can be explained and traced.
Algorithmic accountability is about making sure that the decisions made by computer programs are fair and understandable. Just like how a judge explains their reasoning in court, algorithms should be able to show how they arrived at a decision. This is important because it helps prevent biases an...
Ensuring decisions can be explained and traced.
Processes and controls for data quality, access, lineage, retention, and compliance across the AI lifecycle.
Policies and practices for approving, monitoring, auditing, and documenting models in production.
Standardized documentation describing intended use, performance, limitations, data, and ethical considerations.
Structured dataset documentation covering collection, composition, recommended uses, biases, and maintenance.
Required human review for high-risk decisions.
Logged record of model inputs, outputs, and decisions.
Legal or policy requirement to explain AI decisions.
European regulation classifying AI systems by risk.
AI used in sensitive domains requiring compliance.
International AI risk standard.
Required descriptions of model behavior and limits.
Requirement to inform users about AI use.
Ability to inspect and verify AI decisions.
Requirement to provide explanations.
Privacy risk analysis under GDPR-like laws.
Mechanism to disable AI system.
AI used without governance approval.
Assigning AI costs to business units.
AI supporting legal research, drafting, and analysis.
Models estimating recidivism risk.
Legal right to fair treatment.
Requirement to reveal AI usage in legal decisions.
Ensuring models comply with lending fairness laws.