AI Ethics

This document describes the ethical principles, controls, and operational practices that guide the design, evaluation, deployment, and ongoing monitoring of our AI services. It is intended for engineers, reviewers, product owners, and auditors.

Bias Mitigation

We have implemented a multi-faceted approach to bias mitigation:

• Diverse Training Data: Our AI is trained on a diverse dataset to minimize demographic bias.
• Regular Audits: We conduct regular audits of our AI to identify and address any potential biases.
• Fairness Metrics: We use a variety of fairness metrics to evaluate the performance of our AI and ensure that it is not making biased decisions.

Validation audits

Validation audits are applied across the lifecycle of the system — to training data, model outputs, and in production — and combine automated checks with prioritized human review.

• Training-data validation:

  • Verify provenance, labeling quality, and representation across demographic and situational strata.
  • Run data-quality checks (missing values, label consistency, duplicates) and statistical tests for sampling bias and distributional shifts.
  • Maintain dataset documentation and versioning so audits can trace back to specific data artifacts.

• Model-output validation:

  • Evaluate performance on holdout and subgroup-specific test sets; disaggregate metrics by sensitive attributes.
  • Use adversarial, counterfactual, and stress tests to surface systematic failure modes or disparate impacts.
  • Include regression and canary testing before promoting model updates.

• Human-in-the-loop (HITL) and prioritization:

  • Prioritize human review for high-risk decisions, borderline model confidence, and flagged subgroup failures.
  • Route uncertain or disputed cases to trained reviewers and incorporate corrections into the training pipeline.
  • Use active learning to focus annotation resources where audits indicate the greatest bias risk.

• Monitoring, tracking, and remediation:

  • Continuously monitor production outputs with dashboards and alerting for drift and fairness regressions.
  • Track identified issues in an audit log and issue tracker with severity, owner, and remediation status.
  • Define remediation workflows: label correction, targeted retraining, model constraint adjustments, or feature changes, with post-remediation validation.

This layered approach ensures biases are detectable, prioritized by risk, and actionable through clear human oversight and repeatable remediation steps.

Transparency

We are committed to transparency in our AI systems:

• Model Cards: We provide detailed model cards that explain how our AI models work.
• Explainable AI (XAI): We use XAI techniques to make our AI's decisions more interpretable and understandable.
• Public Disclosures: We publicly disclose the use of AI in our services and provide clear explanations of its capabilities and limitations.

Evaluation and Benchmarking

We evaluate and benchmark our models with a repeatable, auditable process that measures accuracy, robustness, fairness, efficiency, and real‑world utility.

Evaluation methodology

• Usde held-out, stratified test sets and cross‑validation to measure generalization (precision, recall, F1, calibration).
• Disaggregate metrics by sensitive attributes and subgroups to detect disparate performance.
• Perform error‑analysis (confusion matrices) and root‑cause investigations for systematic issues.
• Include human evaluation where subjective quality or safety matters (graded annotation, pairwise comparisons, and agreement statistics).

Benchmarks and baselines

• Evaluate against public, domain‑relevant benchmarks and internal, task‑specific datasets to reflect real use cases.
• Compare to clear baselines (previous model versions, simple heuristics, or industry standards) and report relative improvements.
• Report evaluation conditions (dataset versions, preprocessing, random seeds, hardware) to ensure fair comparisons.

Robustness, safety, and stress testing

• Run adversarial, counterfactual, and out‑of‑distribution tests to surface brittle behaviors.
• Test calibration, confidence estimation, and failure detection; use canary and regression tests before deployment.
• Assess safety and policy risks (toxicity, privacy leakage, hallucination) with targeted probes and expert review.

Efficiency and operational benchmarks

• Measure latency, throughput, memory, and cost across representative hardware and batch sizes.
• Benchmark tradeoffs between accuracy and efficiency (e.g., model size vs. inference time) to inform deployment choices.

Production evaluation and continuous monitoring

• A/B test and shadow‑deploy model updates to gather real user metrics and human feedback before full rollout.
• Continuously monitor production outputs for drift, fairness regressions, and key business KPIs; trigger alerts and rollback when thresholds are breached.
• Maintain audit logs linking production issues to dataset/model versions and remediation actions.

Reporting and reproducibility

• We publish summary results in model cards and technical notes: datasets, evaluation protocols, metrics, limitations, and error cases.
• We version datasets, training code, and evaluation scripts; we archive artifacts to enable independent reproduction and future audits.

This disciplined approach ensures evaluation and benchmarking remain comprehensive, comparable, and tied to operational risk and user impact.