# Data Ethics

> Practice data ethics — privacy, fairness, consent, dual-use risk, and responsible communication of analytical findings. Use when working with sensitive data, people-affecting models, or high-impact analytics.

- Skill: `itsual/data-ethics` (Agent Skill)
- Install (CLI): `npx skillmds@latest add itsual/data-ethics`
- Raw SKILL.md: https://api.skillmd.com/api/skills/itsual/data-ethics/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Data & Analytics
- Author: itsual (https://skillmd.com/u/itsual)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/itsual/data-ethics

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# Data Ethics

## Overview

Data ethics asks what should be done with data and models, not only what can be done. It covers privacy, fairness, transparency, and potential harm.

## When to Use

- Projects with personal or sensitive data
- Models affecting access to opportunities (credit, jobs, services)
- Publishing or sharing analytical results externally
- Designing data collection practices

## Core Practices

- Minimize data collected and retained for the purpose
- Assess fairness and disparate impact where decisions affect people
- Obtain and respect appropriate consent and lawful basis
- Avoid re-identification risks in “anonymized” releases
- Communicate uncertainty and limits honestly
- Escalate dual-use or harm concerns early

## Principles

- Legal compliance is necessary but not sufficient for ethics
- Aggregate results can still harm groups
- “The data said so” does not remove human responsibility
- Document decisions about sensitive trade-offs

## Verification

- [ ] Purpose limitation and minimization are considered
- [ ] People-impacting uses have fairness/privacy review
- [ ] Communications do not overclaim certainty