Independent AI Bias, Fairness, and Risk Assessment
Cyberonix is retained by technology and corporate leadership, audit committees, and pre-investment investors to evaluate a specific AI system for bias, fairness, and risk before deployment or as part of an ongoing audit cycle. An AI risk assessment at this level is system-level: a named model or AI-driven product is examined against fairness metrics calibrated to its decision context, robustness and adversarial-testing criteria scoped to its deployment surface, alignment and safety properties relevant to its capabilities, and the regulatory frameworks the system is subject to: the EU AI Act, the NIST AI Risk Management Framework, and sector-specific frameworks where they apply. Findings are grounded in the artifacts that produced the system (training data documentation, evaluation records, model behavior under defined test sets, deployment telemetry, and incident records) and not in vendor claims, marketing material, or what industry convention treats as adequate.
Fairness, Bias, and Disparate-Impact Analysis
Fairness evaluation is calibrated to the system’s decision context and the populations the deployment will affect, not applied as a uniform checklist.
Quantitative fairness evaluation is conducted under the metric families the matter calls for (demographic parity, equalized odds, equal opportunity, and calibration across subgroups), selected for the system’s decision context rather than applied uniformly. Subgroup analysis is conducted at the resolution the deployment requires, with intersectional subgroups examined where the use case has produced documented disparate-impact concerns or where the protected-class composition of the affected population gives those intersections analytical weight that single-axis analysis does not surface.
Disparate-impact analysis is conducted under the legal framework that governs the deployment: Title VII for employment AI (with the four-fifths rule applied as the EEOC’s rule of thumb for initial screening rather than a definitive test), ECOA for credit AI, and the sector-specific frameworks that apply in healthcare, housing, and consumer finance. The legal framework determines the analysis structure rather than the other way around. Where multiple frameworks apply, each is evaluated against the evidentiary standard it sets, and the results are reported separately so counsel can read each on its own terms.
The evaluation surfaces both algorithmic bias attributable to the model (architecture choices, objective function, training procedure) and label or representation bias attributable to the training data. Each finding is located in the artifact that supports it: a fairness-metric gap in the evaluation record, a representation gap in the training data documentation, a label-quality finding in the annotation procedure. The deliverable preserves that chain of evidence so the client can re-examine any specific point.
Robustness, Adversarial Testing, and Alignment Risk
Robustness, adversarial, and alignment evaluation characterizes how the system behaves outside the conditions its training and evaluation sets covered.
Robustness is evaluated under distribution shift, adversarial perturbation, and, for generative systems, prompt-injection, jailbreak, and instruction-conflict conditions. Red-team testing is scoped to the specific failure modes the deployment context creates rather than to a generic adversarial benchmark whose threat model does not match the system’s actual exposure. The choice of perturbations, prompt-injection vectors, and adversarial inputs follows the deployment surface and the population of adversaries the system can realistically expect to encounter.
For LLM-integrated systems, alignment and instruction-following are evaluated against the behavioral specifications the deployer has set, including evaluation of agentic capabilities where the system has tool-use or action-taking authority. Where the system can write to external state, the evaluation examines what bounded its actions in adversarial conditions and what would happen if those bounds failed. For predictive systems, error-rate analysis is conducted under realistic deployment conditions, including the long-tail inputs the test set may underrepresent and the regime-shift conditions that production data exhibits but evaluation data rarely does.
Findings are written so the failure mode, the conditions that produced it, and the artifact that supports the finding are traceable from the deliverable. Cyberonix reports a robustness gap with the input distribution, the perturbation envelope, and the evaluation record that surfaced it; an alignment finding is reported with the prompt, the response, and the specification it violated.
Regulatory Exposure and Audit Readiness
Regulatory exposure is mapped to the specific framework or frameworks the system is subject to, not to a generic checklist of obligations.
EU AI Act risk-class determination is conducted for systems serving EU markets, with conformance gap analysis against the obligations of the determined class (unacceptable-risk, high-risk, transparency-risk, or minimal-risk) and the documentation, testing, and post-market monitoring duties that follow from it. NIST AI Risk Management Framework assessment is conducted under the Map, Measure, and Manage functions against the system’s intended use and context, informed throughout by the cross-cutting Govern function, which the adjacent organization-level engagement examines in full depth.
Sector-specific frameworks are evaluated where the deployment context invokes them: HIPAA for healthcare AI, the NYC Automated Employment Decision Tools rule for hiring AI used on New York City candidates, FTC Section 5 exposure for consumer-facing AI making unfair or deceptive claims, CFPB guidance for credit AI under ECOA and adverse-action notice obligations, and FDA Software as a Medical Device guidance for AI-enabled medical devices. Each is cited only where the system’s deployment context invokes it; an unrelated framework is not added to the deliverable to fill space.
The analysis identifies where the existing evidentiary record meets the framework’s documentation standard and where it does not, and recommends what additional evaluation, documentation, or controls are needed to close the gap. The analytical record is preserved so that, where a matter later proceeds to litigation, the firm’s litigation practice carries the findings forward without redoing the underlying work.
Our Experts
The Cyberonix AI bias and risk assessment team is the same group of senior consultants who staff the firm’s litigation work. Each holds a faculty appointment at a research university in the United States, with recognitions including IEEE Fellow status, ACM Distinguished Member status, and named professorships at leading research institutions. Industry depth spans AI systems research and applied AI engineering: model training and evaluation, MLOps infrastructure, alignment and safety, and the regulatory frameworks that govern deployed AI. Engagements are staffed so the lead consultant’s research record and industry background align with the AI system under review.