Can I use it for LLMs?

Yes, the 2026 version includes modules for auditing LLM outputs for demographic parity and toxicity variance.

Audit-AI

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Overview

Audit-AI is a sophisticated technical framework and SaaS platform designed to measure and mitigate bias in machine learning models, specifically targeting high-stakes decision-making environments like HR, finance, and healthcare. As of 2026, the tool has positioned itself as a critical component of the AI Trust, Risk, and Security Management (AI TRiSM) stack, facilitating compliance with the EU AI Act and US algorithmic accountability standards. The architecture focuses on identifying disparate impact through rigorous statistical tests, including the Four-Fifths Rule, Fisher’s Exact Test, and Z-tests. Unlike standard monitoring tools, Audit-AI provides a deep-dive into protected class correlations, even when sensitive attributes are not explicitly present in the training data, by identifying 'proxy variables.' Its 2026 market position is defined by its ability to integrate directly into CI/CD pipelines as a 'Fairness Gate,' preventing biased models from ever reaching production. The platform supports both post-hoc auditing and in-training bias mitigation strategies, offering a dual-layer approach to ethical AI development.

Common tasks

Disparate impact analysis Bias mitigation Regulatory reporting Proxy variable identification Model fairness benchmarking AI risk assessment Model performance monitoring Generate compliance documentation

FAQ

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Does Audit-AI require access to raw sensitive data?

While it can use raw data, Audit-AI also supports hashed or anonymized identifiers and can perform proxy detection without explicit labels if a reference dataset is provided.

Is it compliant with the EU AI Act?

Yes, Audit-AI 2026 includes specific templates and metric thresholds that align with the high-risk AI system requirements of the EU AI Act.

Can it fix the bias automatically?

It provides mitigation suggestions and 'Fairness-Weighted' training wrappers, but the final decision to implement changes remains with the human-in-the-loop.

What is the 4/5ths rule mentioned in the reports?

It is a standard used by US regulatory bodies (EEOC) stating that the selection rate for a protected group should be at least 80% of the rate for the highest-performing group.

FAQ+

Does Audit-AI require access to raw sensitive data?

While it can use raw data, Audit-AI also supports hashed or anonymized identifiers and can perform proxy detection without explicit labels if a reference dataset is provided.