Safety Practices

At Explicity, safety is a core part of how we design, build, and deploy our AI systems. Our safety practices include research, safe model training, risk assessments, safety testing, human oversight, and continuous monitoring. We implement safety measures at every stage of the AI lifecycle, from data collection and model training to deployment and real-world use.

We use safety policies, content filters, and system-level guardrails to prevent harmful, unsafe, or policy-violating outputs. We also conduct red teaming, safety evaluations, and risk assessments to identify potential risks before deployment. If any safety issue is identified, we improve, retrain, or restrict the system until the issue is resolved.

After deployment, we continuously monitor system behavior, review feedback, and update our safety systems to address new risks. We also use controlled testing environments and simulations to safely test AI behavior in different scenarios.

Our safety practices are designed to ensure that Explicity's AI systems are safe, reliable, aligned with human values, and used responsibly. Our goal is to minimize harm, prevent misuse, protect users, and build trustworthy AI systems.

Authors

Explicity AI Research