AI Ethics ProverをChatGPT、Claude、Geminiに接続

• 01 / 02AIの倫理的主張を監査する方法
• 02 / 02AIリスクの数値化について

最速日

661ms

最遅日

1057ms

14日間の傾向

遅延傾向+8%

AI Ethics Prover Connector

準備完了です。MCPクライアントを選択し、セットアップ手順に従ってください。

コネクタリンクhttps://edge.vinkius.com/vk_preview_7qJ0qxhBYTGcM0mD5Tm7UjlCl1VpFyO7pPzQ2B4P/mcpコピー

Claude Desktop

以下の手順で数秒で接続できます。

1. 1In Claude Desktop, open Settings → Connectors.
2. 2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
3. 3Click Add and start a new chat — AI Ethics Prover capabilities are ready to use.

設定 · claude_desktop_config.jsonコピー

{
  "mcpServers": {
    "ai-ethics-prover-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_7qJ0qxhBYTGcM0mD5Tm7UjlCl1VpFyO7pPzQ2B4P/mcp"
    }
  }
}

• Claude
• ChatGPT
• Cursor
• VS Code
• Windsurf
• Claude Code
• JetBrains
• Cline

各クライアントの手順ガイドはこちらです。 接続方法

Claude向けのガイド付きセットアップ？ AI Ethics ProverをClaudeや他のAIに設定する

このConnectorが対応するすべてのAIクライアントを見る ↑

• 01

  コンプライアンス担当者

  AI規制（例：EU AI Act）への準拠を目的とし、AIが持つ潜在的なバイアスや倫理的欠陥を事前に監査する必要があります。

• 02

  リスクマネージャー

  AIの誤用や差別的な判断が企業に与える法的、評判上のリスクを定量的に評価し、適切な緩和策を策定します。

• 03

  プロダクトマネージャー

  AI機能を市場に投入する際、単なる機能性だけでなく、倫理的側面から製品の信頼性と説明責任を確保する必要があります。

• 01

  What is 'ethics washing' in the context of AI?

  Ethics washing is when an AI system makes vague ethical claims, like saying 'we are fair' or 'we checked for bias,' without providing specific, measurable data. This MCP is designed to catch those unproven, superficial claims.

• 02

  Does using this MCP certify that my AI is ethical?

  No. This MCP is analytical support. It forces structured thinking and identifies gaps in your ethical review. It provides a rigorous audit, but it does not replace a qualified ethics review board.

• 03

  What specific data points must I provide for bias auditing?

  You must name the protected attribute, the detection metric (like statistical parity), the measured disparity, and the acceptable threshold. Simply saying 'we checked for bias' isn't enough.

• 04

  What happens if the capability finds a failure?

  The capability returns a specific error code (e.g., HARMS_UNQUANTIFIED) and tells you exactly which of the five axes failed. It then specifies what data you need to add to pass the audit.

• 05

  Is this capability useful for general AI development?

  It's essential for any AI system that makes decisions affecting people's lives, like lending, hiring, or healthcare. It forces you to treat ethics as an engineering requirement, not just a PR statement.

• 06

  Does this capability make ethical decisions for me?

  No. It enforces analytical rigor. forcing you to name stakeholders, quantify harms, audit biases with methodology, demonstrate transparency, and define recourse mechanisms. It does not prescribe ethical conclusions. The ethics are yours. The structure is enforced by the capability.

• 07

  What bias metrics does it support?

  Any metric. it is framework-agnostic. Statistical parity, equalized odds, disparate impact ratio, calibration. It does not compute bias. it validates that you named the metric, the protected attribute, the measured disparity, and the acceptable threshold. 'We checked for bias' without this structure is ethics washing.

• 08

  What counts as a recourse mechanism?

  A structured process: (1) accessible challenge channel, (2) response SLA in business days, (3) human reviewer. not automated, (4) appeal process if the initial review denies the challenge, (5) remediation action if the challenge succeeds. 'Contact support' fails every axis.

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