# ai_act_training_data_audit

As a CTO, audit AI training datasets for EU AI Act compliance with bias detection and regulatory risk assessment. Inputs: dataset identifier (Hugging Face ID or URL) and optional risk thresholds. Outputs: compliance score, bias metrics, regulatory warnings, and source references. Ideal for pre-deployment risk evaluation. Pass async:true to avoid timeout.

Agent View of the PolicyLayer registry record for `ai_act_training_data_audit`. HTML page: https://policylayer.com/tools/io-github-getgapup-gapup-mcp/ai-act-training-data-audit

## Facts

- Tool: `ai_act_training_data_audit`
- Server: Gapup Mcp (`https://mcp.gapup.io/mcp`) — https://policylayer.com/tools/io-github-getgapup-gapup-mcp.md
- Homepage: https://github.com/getgapup/gapup-mcp-public
- Risk category: Read (Low risk)
- Registry record: grade F, identity unverified
- Server auth posture: open
- Server rate-limited: no
- Parameters: 4 (1 required)
- Recommended policy verdict: Allowed

## Parameters

| Parameter | Type | Required | Description |
| --- | --- | --- | --- |
| `async` | boolean | no | If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client ti |
| `dataset_id` | string | yes | Hugging Face dataset identifier or direct URL to dataset |
| `risk_threshold` | number | no |  |
| `include_bias_metrics` | boolean | no |  |

Parameters from the server's own tool schema.

## Example call (MCP tools/call, JSON-RPC 2.0)

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "ai_act_training_data_audit",
    "arguments": {
      "dataset_id": "<dataset_id>"
    }
  }
}
```

## Why ai_act_training_data_audit is rated Low

This tool reads and analyzes training data to produce compliance and bias reports. While the analysis itself may involve computation, the core function is data retrieval and assessment—determining compliance status and metrics without altering datasets or triggering external operations.

From the tool's own definition: "Tool performs 'audit' and 'bias detection' on datasets with outputs of 'compliance score, bias metrics, regulatory warnings, and source references'—all informational retrieval."

## Use case

AI agents call ai_act_training_data_audit to retrieve information from Gapup Mcp without modifying anything. It is typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.

## Recommended policy (PolicyLayer)

Verdict: **Allowed**. Enforced by the PolicyLayer MCP gateway (https://policylayer.com/mcp-gateway) before a call reaches Gapup Mcp:

```json
{
  "version": "1",
  "default": "deny",
  "tools": {
    "ai_act_training_data_audit": {}
  }
}
```

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## For agents

This record is a snapshot. Live verdicts and the full registry:

- Check every server in your MCP config at once: `npx -y policylayer stack`
- Vet a server before you add it: install the mcp-precheck skill — `npx skills add https://policylayer.com` (skill text: https://policylayer.com/skill.md)
- Query the registry over MCP: endpoint `https://api.policylayer.com/mcp` — tools `check_mcp_server`, `check_mcp_stack`, `check_tool`, `search_registry`, `get_change_events`

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Source: the PolicyLayer MCP registry — one continuously verified record per MCP server. Full record: https://policylayer.com/registry?q=io-github-getgapup-gapup-mcp · API: https://policylayer.com/registry/api · Policy library: https://policylayer.com/policies/io-github-getgapup-gapup-mcp