Skip to content

The following is an excerpt from an article published in Built In, featuring quotes from Orion’s Chief Technology Officer, Rajul Rana.

The inner workings and decision-making processes of foundation models are often not well understood — even to the people actually making them — which makes it hard to determine how and why they arrive at certain conclusions.

“These are little black boxes,” Rajul Rana, chief technology officer at IT services company Orion Innovation, told Built In. “We know roughly how they work but [we] don’t know exactly why they generate certain outputs.”

This lack of interpretability can make it difficult to trust foundation models’ outputs or correct any errors, which can have massive consequences — especially since they are embedded in our everyday lives, from the facial recognition software used to unlock phones to the hiring algorithms companies use to screen job candidates.

Author

Rajul Rana

Chief Technology Officer

Orion Innovation

Services

services/ai

Related Insights

LLM Data and Privacy: Securing the Foundation of Responsible AI

blogs

LLM Data and Privacy: Securing the Foundation of Responsible AI

LLM Bias & Fairness: Building Inclusive and Trustworthy AI

blogs

LLM Bias & Fairness: Building Inclusive and Trustworthy AI

LLM Observability: Ensuring Accountability in Enterprise AI

blogs

LLM Observability: Ensuring Accountability in Enterprise AI