Interpretable and Auditable AI Systems

We are building a new class of interpretable AI systems and foundation models that humans can reliably debug, trust, and understand.

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Understand which part of the prompt is responsible for the output.

Understand what factors are responsible for the output.

Understand which training data is responsible for the output.

The Problem

Current AI systems and foundation models:

produce explanations and justifications that are unreliable and unrelated to their output.

cannot be reliably debugged and fixed.

are difficult to control and align with current approaches.

Our Solution

LLMs & Foundation models engineered to be interpretable

Produces human-understandable factors for any output it generates

Produces reliable context citations.

Specifies which training input data strongly influences the model’s generated output.

About Our Team

Learn more
00+
Years of Experience

on interpretable machine learning with PhDs from MIT, UMD, & MILA.

00+
Research Papers

on interpretability published at top ML conferences

Developed the first

interpretable generative diffusion model & LLM
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Proven best practices

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