Responsible AI

Responsible AI practices

Policy, risk taxonomy, and governance forums: We define policies and risk taxonomies for AI systems. Governance forums review use cases, approve deployments, and oversee ongoing operations. Risk is assessed and mitigated before deployment. These practices ensure AI is managed systematically and responsibly.

Evaluation suites (quality, safety, robustness, cost): We evaluate AI systems for quality, safety, robustness, and cost before deployment. Evaluation includes accuracy, fairness, bias, toxicity, jailbreak resistance, and adversarial testing. We track metrics over time and compare against baselines. These practices ensure AI systems meet quality and safety standards.

Monitoring and rollback plans for AI systems: We monitor AI systems in production for drift, degradation, and anomalies. Metrics include accuracy, latency, cost, and business outcomes. Rollback plans ensure we can revert changes quickly if issues arise. These practices ensure AI systems remain reliable and safe over time.

Human oversight, auditability and documentation: We maintain human oversight of AI systems. Human-in-the-loop checkpoints ensure critical decisions are reviewed. Audit trails record decisions and outcomes. Documentation explains how systems work and how they are evaluated. These practices ensure AI systems remain aligned with business values and ethical standards.

Fairness and bias

We test for fairness and bias across protected characteristics and use cases. Evaluation includes demographic parity, equalized odds, and calibration metrics. We identify and mitigate bias before deployment. Monitoring detects bias drift over time. These practices ensure AI systems treat all users fairly.

Data quality is foundational: biased data produces biased models. We assess data for representativeness, completeness, and bias. We use balanced datasets and data augmentation where appropriate. We document data sources and limitations. These practices ensure models learn from fair data.

Safety and security

We test for safety and security vulnerabilities including prompt injection, model extraction, data poisoning, and adversarial examples. Evaluation includes jailbreak resistance, output filtering, and input validation. We implement guardrails and circuit breakers to prevent harmful outputs. These practices ensure AI systems are safe and secure.

Security controls protect AI systems from attacks. We use authentication, authorization, encryption, and audit logging. We test with adversary simulation and penetration testing. These practices ensure AI systems are protected from malicious actors.

Transparency and explainability

We document how AI systems work, how they are evaluated, and how they are monitored. Explanations help users understand decisions. We provide transparency reports and evaluation results. These practices ensure AI systems are understandable and accountable.

Explainability varies by use case: high-stakes decisions require more explanation than low-stakes ones. We use appropriate techniques for each use case. We document limitations and uncertainties. These practices ensure users understand AI decisions appropriately.

Realistic expectations

We align messaging to adoption realities to avoid over-promising. OECD research shows AI productivity gains are uneven and require disciplined execution. We set realistic expectations and communicate limitations clearly. These practices ensure clients understand what AI can and cannot do.

Value comes from sound engineering, not hype. We focus on use cases where AI adds real value. We measure outcomes honestly and report results transparently. These practices ensure AI investments deliver real value.

Contact us

For responsible AI questions, please use our contact form. We are committed to responsible AI practices and welcome feedback.