Ethics and AI Governance

Principles, policies, processes, and artifacts for the responsible use of artificial intelligence.

Fairness Transparency Privacy Accountability

Processes: DPIA, red‑teaming, incident response.

Principles: fairness, transparency, security.

Subgroup parity metrics, incident frequency.

live demo · related simulation● LIVE

User feedback/complaints/appeals.

Risk/domain classification, RACI roles.

Assessment of quality/security/fairness.

Frequently asked questions

How to avoid biases? Representative data?

To avoid biases, use representative datasets, employ parity metrics, and conduct feature and threshold audits.

How to ensure transparency? Cards, explanation?

To ensure transparency, utilize explainable AI (XAI) ‘cards,’ provide clear explanations of decisions, and establish channels for appeals.

What about privacy? DPIA, minimization, masking?

Regarding privacy, implement Data Protection Impact Assessments (DPIAs), minimize data collection, utilize data masking techniques, and adhere to data retention policies.

Who is responsible? Model/data owners, ?

Model and data owners bear responsibility alongside an ethical review board and legal/security professionals.

▶ Try it live

Everything above runs in your browser — open Hash Function Avalanche Visualizer and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.

▶ Open Hash Function Avalanche Visualizer simulation