MCP For Claude Marketplace
AI Bias Audit & Risk Assessment

AI Bias Audit & Risk Assessment MCP Connector for Claude

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Calculate AI bias risk scores, legal liability, and remediation timelines.

4 tools Official Updated Oct 1, 2026 Official Vinkius Partner
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This MCP server provides critical tools for assessing the risk profile of Artificial Intelligence systems. It allows agents to calculate a comprehensive bias risk score by analyzing demographic performance variance and use case sensitivity. Additionally, it estimates potential legal liability exposure across different jurisdictions like the USA and Europe, and projects the necessary remediation effort and timelines based on available budgets. Use calculate_bias_risk_score to determine organizational risk, estimate_legal_liability for financial impact, project_remediation_effort for mitigation planning, and get_audit_summary for high-level compliance status.

ai-biasrisk-assessmentlegal-complianceauditethics

4 tools expose this connector's capabilities to your AI agent.

calculate_bias_risk_score

Determines the total organizational risk score of an AI system

estimate_legal_liability

Calculates the potential financial impact of legal penalties

get_audit_summary

Provides a high-level overview of the current bias status

project_remediation_effort

Predicts the time and resource requirements to mitigate identified bias

See how to talk to your AI agent using AI Bias Audit & Risk Assessment.

What is the risk score for an AI used in hiring with a demographic variance of 0.15?

The calculated risk score is High, as the demographic variance of 0.15 in a high-sensitivity hiring use case results in significant organizational risk.

Estimate the legal liability for a medium-sensitivity model in Europe with 0.1 variance.

The estimated legal liability exposure is $250,000, falling under the standard regulatory tier for European deployments.

How long will it take to fix a bias with 0.2 variance if I have a $50,000 budget?

The estimated remediation timeline is 12 weeks, with a moderate complexity rating. The current budget is considered adequate for this task.

The score is determined by adjusting the measured demographic variance against the sensitivity tier of the AI application using `calculate_bias_risk_score`.

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