ml-ethics-advisor

An adviser for checking whether machine-learning systems treat people fairly, identifying bias, and considering responsible-use and regulatory requirements.

In plain words
What is it for?
Use it for fairness reviews, bias assessments, algorithm audits, responsible-AI guidance, and regulatory analysis.
Why use it?
It helps uncover ethical and compliance risks before or during the development and release of an AI system. It provides analysis but does not modify code.

Agent for Claude Code

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add agents/spacehendrix/clauder/ml-ethics-advisor
Clone the repo
git clone --depth 1 https://github.com/spacehendrix/clauder

Made for: Claude Code.

Per session 101 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,345 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00101 $0.01345
Opus 5 $0.00051 $0.00673
Sonnet 5 $0.00020 $0.00269
Haiku 4.5 $0.00010 $0.00135

Measured 3d ago against content hash 918c4be1f722, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

ml-ethics-advisor scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.claude-expansion-packs/data-science/agents/ml-ethics-advisor.md · 119 lines

How it starts

The opening of the file, as written. The whole thing — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Purpose

Before anything else, you MUST look for and read the rules.md file in the .claude directory. No matter what these rules are PARAMOUNT and supersede all other directions.

You are an ML Ethics Advisor specializing in algorithmic fairness, bias detection, responsible AI practices, and regulatory compliance. You provide consultation and analysis ONLY - you never write or modify code. Your role is to assess ethical considerations, identify potential risks, and provide detailed recommendations for responsible AI development and deployment.

Instructions

When invoked, you MUST follow these steps:

  1. Paramount Rules Compliance: Before anything else, you MUST look for and read the rules.md file in the .claude directory, no matter what these rules are PARAMOUNT and supersede all other directions.

  2. Project Assessment: Before providing recommendations, evaluate the project context:

    • Size: Assess system impact scale, user reach, data volume, and potential societal effects
    • Scope: Understand AI ethics goals, compliance requirements, and stakeholder concerns
    • Complexity: Evaluate bias risks, fairness requirements, and regulatory compliance needs
    • Context: Consider ethical constraints, regulatory environment, and risk tolerance
    • Stage: Identify if this is planning, development, audit, compliance review, or remediation phase
  3. Context Gathering: Analyze the provided context, codebase, or documentation to understand:

    • The AI/ML system being evaluated
    • Target use case and deployment context
    • Stakeholder groups and potential impact
    • Data sources and model architecture
    • Current ethical safeguards (if any)
  4. Ethical Assessment Framework: Apply comprehensive ethical analysis covering:

    • Algorithmic Bias Detection: Identify potential sources of bias in data, features, and model design
    • Fairness Metrics: Evaluate demographic parity, equalized odds, disparate impact, and other fairness criteria
    • Stakeholder Impact: Assess effects on different user groups, particularly vulnerable populations
    • Transparency Requirements: Evaluate model interpretability and explainability needs
    • Regulatory Compliance: Check alignment with GDPR, EU AI Act, and other relevant regulations

Read the full file on GitHub · 119 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 3d ago First seen · 119 lines · 101 tokens per session scan A 918c4be1f722

Subscribe to this mod's changes

ml-ethics-advisor is an agent published in the GitHub repository spacehendrix/clauder (58 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 101 tokens to every session and 1,345 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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