Apply AI ethics frameworks (fairness, accountability, transparency, privacy) to evaluate AI systems for algorithmic bias, explainability gaps, and value alignment failures.
SkillScore82Maintained
Automatic
◆AI Engineer pack
Last verified 8/27/2026
What it changes
Passed 18 of 22 test cases with the skill, and 10 of 22 without it.
Without skill45%
With skill82%
Checks it passed that the model alone missed
Uses the header format ## AI Ethics Assessment: [System/Context]
The output begins with a level 2 header matching ## AI Ethics Assessment:
Evaluates individual fairness alongside demographic parity and equalized odds
and 7 more in the full report
8 fixed10 passed either way4 still failing0 broke
Pass-rate lift
+36 pp
Tokens
+13%
Agent turns
0%
Response time
-12%
Model gemini-3.6-flashMeasured 8/27/2026v118 of 22 cases passed with skill
Tokens, turns and time describe resource use, not a dollar price.
See it in action
One of 8 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 8
Prompt
We are launching an automated credit decisioning platform for consumer loans and require a formal ethical analysis. Format your output with a System Profile section detailing the operational context and affected demographics, a markdown Fairness Assessment table evaluating key parity and odds metrics alongside risk levels, a Transparency and Explainability table for applicants and compliance officers, an Accountability Structure defining developer versus deployer obligations and redress procedures, and a numbered list of mitigation recommendations covering pre-processing, in-processing, post-processing, and post-deployment monitoring.
What was checked, without and with the skill
Checked
Without skill
With skill
The output begins with a level 2 header matching ## AI Ethics Assessment:
Missed
Passed
The Transparency table defines gaps specifically for affected individuals and regulators
Passed
Passed
The Accountability section explicitly separates Developer responsibility from Deployer responsibility
Passed
Passed
case-03 · gemini-3.6-flash · 8/27/2026 · answers are capped and redacted
What's inside
The instructions your model reads when the skill loads.
SKILL.md108 linessingle file
AI Ethics
Overview
AI ethics examines the moral dimensions of artificial intelligence systems, centered on four pillars: fairness, accountability, transparency, and privacy (FATE). As AI systems increasingly make consequential decisions, they inherit and amplify the biases embedded in training data and design choices. Ethical AI requires proactive identification of bias, explainability mechanisms, clear accountability structures, and privacy protections.
When to Use
Auditing an AI system for fairness before or after deployment
Designing bias mitigation strategies for machine learning pipelines
Evaluating explainability requirements for different stakeholder audiences
Assessing regulatory compliance (EU AI Act, GDPR, sector-specific requirements)
When NOT to Use
When the question is purely about model performance without ethical dimensions
When analyzing non-AI automation or rule-based systems with full transparency
When the focus is on AI technical architecture without deployment context
Assumptions
IRON LAW: AI systems encode the VALUES of their designers and training
data — there is no value-neutral AI, and "optimizing for accuracy"
without fairness constraints reproduces existing inequalities.
Key assumptions:
All datasets reflect historical decisions and biases — "ground truth" is socially constructed
Fairness has multiple, mathematically incompatible definitions — choosing one is a value judgment
Transparency and explainability are not the same — a system can be transparent (open code) but not explainable (no one understands why it decided X)
Accountability requires clear chains of responsibility from developer to deployer to affected party
Methodology
Step 1: Map the AI System and Stakeholders
Identify the AI system's function, decision domain, affected populations, and the power asymmetry between system operators and subjects.