A horizontal bias audit framework for high-stakes AI systems — hiring, lending, healthcare, justice, and beyond. Built at the intersection of cybersecurity, machine learning, and algorithmic accountability.
AI systems now make consequential decisions about who gets hired, who gets a loan, who gets medical care, and who gets parole. Bias in these systems doesn't just exist — it scales. A biased human recruiter sees hundreds of resumes a year. A biased model sees millions, faster, and with the false veneer of mathematical objectivity.
FairAudit AI is built as a horizontal engine with domain-specific modules. Each vertical has its own protected attributes, regulatory landscape, and fairness considerations — but the underlying audit logic is shared.
Resume screening, video interview AI, skills assessment scoring, automated rejection systems.
Credit scoring models, loan approval algorithms, insurance underwriting, fraud detection systems.
Diagnostic algorithms, treatment recommendations, patient risk scoring, triage systems.
Recidivism prediction, predictive policing, bail and parole decision support tools.
Admissions algorithms, essay scoring, plagiarism detection, proctoring software, student risk models.
Tenant screening, mortgage approval, property valuation algorithms, rental pricing engines.
Recommendation engines, search ranking, content moderation, ad targeting algorithms.
Benefits eligibility, child welfare risk, immigration scoring, fraud detection in public programs.
LLM stereotyping audits, image generation bias, RAG pipeline content fairness, chatbot output review.
Automated performance reviews, productivity monitoring, promotion algorithms, sentiment analysis.
Bias audits aren't a single test — they're a structured investigation. FairAudit AI runs each system through a defensible methodology designed to satisfy regulators, inform engineers, and protect end users.
Inspect the training data for representation gaps, label noise, and historical bias signatures. Identify protected attributes and known proxies before any model is touched.
Run a suite of fairness tests: demographic parity, equalized odds, predictive parity, calibration. Apply the EEOC four-fifths rule. Compute disparate impact across single and intersectional groups.
Surface feature importance and counterfactual explanations. Identify which inputs are driving disparities and whether protected attributes are encoded through correlated proxies.
Generate a plain-English audit report mapping findings to relevant regulations. Translate statistical results into language compliance teams, executives, and affected communities can act on.
Recommend specific mitigations — reweighting, threshold adjustment, feature removal, post-processing. Deploy continuous monitoring to catch drift before it becomes a violation.
Map findings to internal policy and external regulation. Produce audit artifacts for board review, regulator submission, and public transparency reports where required.
Built on open-source foundations with proven enterprise adoption. Designed to integrate with existing ML pipelines, not replace them.
A staged go-to-market strategy. Begin with high-touch audit engagements that build credibility and surface real client problems. Productize the repeatable patterns into SaaS and training offerings as the market matures.
Hands-on bias audits for mid-market companies. Deliverable is a regulator-ready report plus remediation roadmap. Low capital, high learning.
SaaS dashboard for ongoing fairness monitoring. Drift detection, alerting, automated quarterly reports. Recurring revenue, scalable margins.
Bias literacy training for HR, compliance, and engineering teams. Half-day to two-day workshops. Builds market awareness and pipeline.
Pre-purchase audits of third-party AI tools on behalf of enterprise buyers. Procurement teams need this; nobody offers it well yet.
FairAudit AI is in active development. Open to design partners, early clients, and collaborators working at the intersection of AI ethics, compliance, and machine learning engineering.