Design for Trust, Build for Good
Safety is not an afterthought; we engineer it into AI systems. Compliance? That's just what happens when you build it right.
Catch issues upstream, and keep legal downstream.
Research Insights
A Guide to Differential Privacy for Data Scientists and AI Engineers
Differential privacy is a mathematical framework for protecting individual privacy while still allowing for useful data analysis. This guide answers key questions about its principles, mechanisms, and real-world applications.
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A Proposal for Justifiable AI Decisions
This report provides a comprehensive analysis of the JADS Framework, an architectural pattern designed to solve the problem of explainability and legitimacy in artificial intelligence (AI) systems.
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AI Explainability: Output vs. Decision
This report will conduct an exhaustive comparative analysis of two competing paradigms that define this conflict. The first, which will be termed Model-Output Explanation, represents the current mainstream approach.
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