Notes on building trustworthy AI.
Field notes from the practitioners building AI governance for regulated enterprises. Long-form writing on accountability, evaluation, and the workflow that makes responsible AI possible.
Non-Determinism: Why it is a Feature, not a Bug, in LLMs. Plus, what Thinking Machines Lab’s quest for consistent output mean for Responsible AI.
If you have ever asked an LLM the same question twice and received different answers, you have experienced non-determinism. While this might seem like a bug, it is actually a fundamental characteristic of these powerful models. To understand why, let’s contrast it with a more traditional, deterministic system. Determinism vs. Non-Determinism: A Personal Story As […]
Read the full postUnpacking AI Accountability
In traditional software development, accountability is relatively straightforward. A bug in a program can often be traced back to a specific line of code or a developer’s oversight. The responsibility is clear. AI, as we know it now, introduces the “black box” problem. The model’s decisions are based on patterns learned from vast datasets. This […]
Read the full postHumanizing AI.
Conversations with the academics, authors, and practitioners shaping how AI gets governed. Hosted by Cesar Koirala.