Black Box vs. Glass Box
Most AI systems are black boxes. Data goes in, results come out, and the process between them remains opaque—sometimes even to the developers who built the system.
This opacity is acceptable for many applications. Recommendation algorithms, image filters, and music playlists don't require traceability.
Legal technology is different. When AI calculates a disability rating or surfaces relevant case law, the reasoning matters. Transparency isn't a feature—it's a design requirement.
What Transparency Means in Practice
Transparent AI shows its work. For our PD ratings calculator, that means displaying:
Each step can be verified against the statutory framework. The output isn't just a number—it's a documented process.
The Six Pillars Framework
Glass Box products are built around six principles:
Source Traceability. Outputs link to the inputs that generated them.
Decision Explainability. The reasoning behind conclusions is documented.
Audit Capability. Complete logs of operations are maintained.
Human Oversight. Attorneys review outputs before use.
Compliance Architecture. Tools are designed for regulatory requirements.
Bias Transparency. Known limitations are disclosed.
Error Handling
Transparent systems acknowledge uncertainty. When our tools encounter edge cases or low-confidence situations, they indicate this clearly. Confidence scores and limitation disclosures are part of the output.
Why We Built This Way
Our name reflects our design philosophy. Glass Box means visible processes, documented reasoning, and verifiable outputs. Every tool we build follows these principles.
This approach shapes what we can offer: AI assistance with clear traceability, not black box automation.