Risk evaluation, accountability structures, and bias mitigation
Multi-dimensional safety metrics: robustness (adversarial inputs, distribution shifts), factuality (hallucination rates, TruthfulQA), toxicity propensity, jailbreak susceptibility, and out-of-scope use detection for responsible deployment.
We help you with safety and risk evaluation that evaluates models across critical dimensions. We measure robustness against adversarial attacks, track hallucination rates with factuality benchmarks, assess toxicity and jailbreak risks, and clearly define model limitations and out-of-scope use cases.
Shared responsibility models defining accountability at each level. CVE-style vulnerability disclosure (90-day windows), EU AI Act compliant incident reporting (15-day notifications), cross-functional governance committees, and audit trails for every model version.
We help you with governance and accountability that establishes clear accountability chains from model developers to deployers. We maintain formal incident reporting mechanisms, quarterly risk reviews by cross-functional committees, complete audit trails, and human-in-the-loop requirements for high-risk decisions.
Identification of biases across the lifecycle: Sample Bias (representative mismatch), Label Bias (systemic outcome errors), and Pipeline Bias (ingestion & feature engineering). Evaluation uses intervention-aware metrics (FPR/FDR for punitive; FNR/FOR for assistive) to ensure demographic parity.
We help you with bias, fairness and stratification that treats bias as a system‑wide property, not a one‑off data issue, by auditing every stage of development—from data generation through deployment. At the data layer, we detect Sample Bias: under‑ or over‑representation of demographic groups, geographies, or socio‑economic strata that can encode historical inequities into training data. At the outcome layer, we scrutinize Label Bias, particularly in domains where proxies for harm (e.g., arrests) are used instead of ground‑truth events (e.g., actual crimes), which can systematically disadvantage already‑over‑policed communities. At the modeling layer, we identify Pipeline Bias in feature engineering—such as the use of ZIP codes, education proxies, or behavioral signals that indirectly encode sensitive attributes and reinforce existing stratification. Crucially, we mandate that teams choose fairness‑aware evaluation metrics aligned with the intervention’s real‑world impact. For punitive or high‑stakes systems (e.g., risk‑assessment tools, fraud detection, or policing‑adjacent applications), we prioritize False Positive control to avoid wrongful penalties, stigmatization, or denial of opportunity for already‑marginalized groups. For assistive or opportunity‑expanding programs (e.g., welfare eligibility, scholarship screening, or credit‑access tools), we emphasize False Negative control to ensure that eligible individuals are not silently excluded from support. By enforcing these metric choices and monitoring performance across stratified demographic groups, we aim not only to reduce statistical bias but also to prevent the model from amplifying social stratification through feedback loops in deployment.
Continue exploring the OpenAGI transparency framework
We co-create enterprise AI architecture, develop cutting-edge agentic AI patterns, advance LLMOps methodologies, and engineer innovative testing frameworks for next-generation AI products with our research-centric approach.
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