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ai-ethics

Responsible AI development and ethical considerations. Use when evaluating AI bias, implementing fairness measures, conducting ethical assessments, or ensuring AI systems align with human values.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

aiskillstore/marketplace

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SKILL.md5.7 KB
skill-report.json10.3 KB

SKILL.md(원문)

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AI Ethics

Comprehensive AI ethics skill covering bias detection, fairness assessment, responsible AI development, and regulatory compliance.

When to Use This Skill

Evaluating AI models for bias
Implementing fairness measures
Conducting ethical impact assessments
Ensuring regulatory compliance (EU AI Act, etc.)
Designing human-in-the-loop systems
Creating AI transparency documentation
Developing AI governance frameworks

Ethical Principles

Core AI Ethics Principles

Principle	Description
Fairness	AI should not discriminate against individuals or groups
Transparency	AI decisions should be explainable
Privacy	Personal data must be protected
Accountability	Clear responsibility for AI outcomes
Safety	AI should not cause harm
Human Agency	Humans should maintain control

Stakeholder Considerations

Users: How does this affect people using the system?
Subjects: How does this affect people the AI makes decisions about?
Society: What are broader societal implications?
Environment: What is the environmental impact?

Bias Detection & Mitigation

Types of AI Bias

Bias Type	Source	Example
Historical	Training data reflects past discrimination	Hiring models favoring male candidates
Representation	Underrepresented groups in training data	Face recognition failing on darker skin
Measurement	Proxy variables for protected attributes	ZIP code correlating with race
Aggregation	One model for diverse populations	Medical model trained only on one ethnicity
Evaluation	Biased evaluation metrics	Accuracy hiding disparate impact

Fairness Metrics

Group Fairness:

Demographic Parity: Equal positive rates across groups
Equalized Odds: Equal TPR and FPR across groups
Predictive Parity: Equal precision across groups

Individual Fairness:

Similar individuals should receive similar predictions
Counterfactual fairness: Would outcome change if protected attribute differed?

Bias Mitigation Strategies

Pre-processing:

Resampling/reweighting training data
Removing biased features
Data augmentation for underrepresented groups

In-processing:

Fairness constraints in loss function
Adversarial debiasing
Fair representation learning

Post-processing:

Threshold adjustment per group
Calibration
Reject option classification

Explainability & Transparency

Explanation Types

Type	Audience	Purpose
Global	Developers	Understand overall model behavior
Local	End users	Explain specific decisions
Counterfactual	Affected parties	What would need to change for different outcome

Explainability Techniques

SHAP: Feature importance values
LIME: Local interpretable explanations
Attention maps: For neural networks
Decision trees: Inherently interpretable
Feature importance: Global model understanding

Model Cards

Document for each model:

Model purpose and intended use
Training data description
Performance metrics by subgroup
Limitations and ethical considerations
Version and update history

AI Governance

AI Risk Assessment

Risk Categories (EU AI Act):

Risk Level	Examples	Requirements
Unacceptable	Social scoring, manipulation	Prohibited
High	Healthcare, employment, credit	Strict requirements
Limited	Chatbots	Transparency obligations
Minimal	Spam filters	No requirements

Governance Framework

Policy: Define ethical principles and boundaries
Process: Review and approval workflows
People: Roles and responsibilities (ethics board)
Technology: Tools for monitoring and enforcement

Documentation Requirements

Data provenance and lineage
Model training documentation
Testing and validation results
Deployment and monitoring plans
Incident response procedures

Human Oversight

Human-in-the-Loop Patterns

Pattern	Use Case	Example
Human-in-the-Loop	High-stakes decisions	Medical diagnosis confirmation
Human-on-the-Loop	Monitoring with intervention	Content moderation escalation
Human-out-of-Loop	Low-risk, high-volume	Spam filtering

Designing for Human Control

Clear escalation paths
Override capabilities
Confidence thresholds for automation
Audit trails
Feedback mechanisms

Privacy Considerations

Data Minimization

Collect only necessary data
Anonymize when possible
Aggregate rather than individual data
Delete data when no longer needed

Privacy-Preserving Techniques

Differential privacy
Federated learning
Secure multi-party computation
Homomorphic encryption

Environmental Impact

Considerations

Training compute requirements
Inference energy consumption
Hardware lifecycle
Data center energy sources

Mitigation

Efficient architectures
Model distillation
Transfer learning
Green hosting providers

Reference Files

references/bias_assessment.md - Detailed bias evaluation methodology
references/regulatory_compliance.md - AI regulation requirements

Integration with Other Skills

machine-learning - For model development
testing - For bias testing
documentation - For model cards

리뷰

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같은 저장소의 스킬

academy-guide

무료

Stop and check this skill before finishing any reply to a question about how to use Claude or a Claude product — it recommends matching courses, tutorials, and use cases from Claude Academy (academy.claude.com), Anthropic's learning hub. Trigger on: "how do I", "how can I", "getting started with", "what can Claude do", "teach me", "learn to use"; questions about artifacts, projects, skills, plugins, connectors, MCP; requests about rolling Claude out to a team, class, or organization; and any ask for training materials, onboarding content, or learning resources. Use it when the user is learning how to use a feature or product — not when they are mid-task and just want the task done. This skill composes with other skills: after consulting product documentation to answer how a Claude feature works, also check here for a matching course or tutorial — a docs-grounded answer and an Academy recommendation belong together. Only recommend on a strong match; never invent Academy content.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

aiskillstore/marketplace4432026년 10월 23일 업데이트

acquisition-policy-workflow

무료

Trigger for: explaining current FAR, DFARS, or agency-supplement text; determining documented acquisition-policy status for an agency and FAR part; comparing codified text, RFO model text, and agency deviations; tracing acquisition rulemaking; finding procurement comment periods; analyzing public comments; refreshing prior policy analysis; or producing an Acquisition Policy Impact Brief. Use published federal sources and supplied policy documents without making legal or procurement-specific applicability determinations. Do not use for clause selection, market research execution, opportunities, pricing, grants, or cooperative agreements.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

aiskillstore/marketplace4432026년 10월 23일 업데이트

action-item-organizer

무료

Systematic framework for extracting actionable items from documents and organizing them into prioritized, trackable checklists. Use when converting reports, meeting notes, audits, or any document with embedded action items into structured TODO lists.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

aiskillstore/marketplace4432026년 10월 23일 업데이트

agent-browser

무료

Browser automation for AI agents via inference.sh. Navigate web pages, interact with elements using @e refs, take screenshots, record video. Capabilities: web scraping, form filling, clicking, typing, drag-drop, file upload, JavaScript execution. Use for: web automation, data extraction, testing, agent browsing, research. Triggers: browser, web automation, scrape, navigate, click, fill form, screenshot, browse web, playwright, headless browser, web agent, surf internet, record video

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

aiskillstore/marketplace4432026년 10월 23일 업데이트

agent-development

무료

This skill should be used when the user asks to "create an agent", "add an agent", "write a subagent", "agent frontmatter", "when to use description", "agent examples", "agent tools", "agent colors", "autonomous agent", or needs guidance on agent structure, system prompts, triggering conditions, or agent development best practices for Claude Code plugins.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

aiskillstore/marketplace4432026년 10월 23일 업데이트

agent-tools

무료

Run AI apps via inference.sh CLI - image generation, video creation, LLMs, search, 3D, Twitter automation. Models: FLUX, Veo, Gemini, Grok, Claude, Seedance, OmniHuman, Tavily, Exa, OpenRouter, and many more. Use when running AI apps, generating images/videos, calling LLMs, web search, or automating Twitter. Triggers: inference.sh, infsh, ai model, run ai, serverless ai, ai api, flux, veo, claude api, image generation, video generation, openrouter, tavily, exa search, twitter api, grok

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

aiskillstore/marketplace4432026년 10월 23일 업데이트

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이 스킬의 문제 신고

설치

명령으로 설치

npx skills add https://github.com/aiskillstore/marketplace --skill ai-ethics

터미널에 붙여 넣고 실행합니다(Node.js 필요). Claude Code, Codex 등 설치할 에이전트를 고를 수 있습니다.

에이전트에게 부탁하기

다음 Agent Skill을 설치해 주세요.
https://github.com/aiskillstore/marketplace/tree/main/skills/89jobrien/ai-ethics

먼저 SKILL.md와 첨부 파일의 내용을 확인해 수상한 처리(외부 전송, 인증 정보 읽기 등)가 없는지 확인한 뒤 Claude Code는 ~/.claude/skills/ai-ethics/, Codex는 ~/.agents/skills/ai-ethics/ 에 배치해 주세요.

이 문장을 그대로 Claude Code나 Codex에 보내면 내용을 확인한 뒤 설치해 줍니다.

GitHub에서 소스 보기

저장소: aiskillstore/marketplace
스타: 443
포크: 48
카테고리: 기타
원문 언어: 영어
최종 업데이트: 2026년 10월 23일

스킬은 에이전트를 위한 지시와 스크립트의 묶음입니다. cccskills는 내용의 안전성을 보증하지 않습니다. 설치하기 전에 내용을 확인하세요.