Fairness testing
Group and subgroup metrics, threshold analysis, proxy variable review and documentation of accepted trade-offs.
AI Risk, Ethics & FATE
Ethics that produces evidence, not declarations
FATE — Fairness, Accountability, Transparency and Ethics — used as a working lens: measurable criteria, documented decisions, human oversight and reviewable outcomes.
What does FATE mean in artificial intelligence?
FATE stands for Fairness, Accountability, Transparency and Ethics. Fairness concerns disparate outcomes across groups and the metrics chosen to detect them. Accountability defines who answers for a model's decisions and how that is recorded. Transparency covers what is disclosed about data, purpose, limitations and logic to users, auditors and affected people. Ethics covers the acceptability of the use case itself, including cases where the correct decision is not to deploy.
What we assess
Accountability
Decision rights, sign-off records, escalation and the chain from model output to human responsibility.
Transparency
Model cards, dataset documentation, user-facing disclosure and explanation suitable for the audience.
Bias forensics
Forensic investigation of alleged algorithmic discrimination, with reproducible method and technical report.
Frequently asked questions
Can bias be eliminated from an AI system?
No. Bias can be measured, reduced and disclosed, but not eliminated, because fairness definitions conflict mathematically and any model reflects the data and choices behind it. Responsible practice means selecting appropriate metrics, testing regularly, documenting trade-offs and maintaining human oversight where impact on people is material.
When is algorithmic bias forensics needed?
Typically after a complaint, regulatory inquiry, litigation or internal alert suggesting that an automated decision produced discriminatory outcomes. The work reconstructs how the decision was produced, tests for disparate impact and documents findings in a form usable by legal and regulatory processes.
Test your models against FATE criteria
A structured fairness, transparency and accountability review of your highest-impact AI use cases.