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If a bank cannot show where a data point came from, when it was last verified, and what changed sinc...
The AI black box problem occurs when a machine learning model delivers high-stakes decisions—like denying loans, flagging transactions, filtering job applicants, or guiding medical...
Across the UK, financial institutions are using machine learning models to make decisions that affec...
A new artificial-intelligence investment simulator could make stock-market algorithms less mysterious by showing users not only what an AI recommends, but also which signals influe...
Opening the Neural Network “black box”.Continue reading on Medium »
Motional and MIT researchers have built a system that lets self-driving cars explain their decisions in real-time, tackling the black-box problem in autonomous vehicle AI. The work...
The Nobel Prize-winning idea from 1953 that helps us understand what’s really going on inside AIContinue reading on Medium »
AI transparency is the practice of making an artificial intelligence system's data, model behavior...
The bug report was received as a customer complaint. An AI agent responsible for managing vendor onboarding had sent a rejection email to a supplier the company had been trying to...
How I turned influencer data into a practical MarTech decision-support systemContinue reading on Medium »
Learn the architecture and design decisions behind an explainable next-best-product recommendation system for banking, built with Amazon SageMaker AI and PyTorch. A multi-tower neu...
By Swagatam Sen, Founder & CEO, ControlOne Financial crime teams have been offered a choice for ...
Enterprise AI has a new infrastructure problem: companies are accumulating agents faster than they are developing systems to govern them.Gartner estimates that the average global F...
Learn how to use the Embabel Agent Framework in Java to gain observability over AI agent tool-call selection reasoning. The post LLM Tool Call Reasoning Using Embabel Agentic AI F...
Ask AI how to improve a factory, a clinic, a logistics company, or a retail business, and it will have plenty to say. It can list ideas, explain trends, draft plans, compare option...
What explainability means for language models, how ClinicalGPT currently approaches it, and what must be tested before an attribution…Continue reading on Medium »
AI has outgrown the role of an assistant, now moving money, approving transactions, and carrying out multi-step tasks with little to no human input in every loop. These agents now...
A developer-friendly deep dive into vectors, semantic similarity, and how modern AI systems retrieve meaning instead of simply matching…Continue reading on Artificial Intelligence...
A new method, called CW-Net, translates the reasoning process of an autonomous vehicle’s AI system into understandable concepts that explain its behavior.
Researchers devised a way to extract “reasoning traces” from Claude, GPT, and Gemini. What they found, they say, indicates that some Chinese AI may be trained on leading US models.
Tell someone, “I’m going to make pancakes,” and see how they interpret it in their head. In New York, they’ll picture a fluffy stack with maple syrup. In Amsterdam, a thin, buttery...
In this article, you will learn three concrete techniques for making machine learning model predictions interpretable, covering both global and local explanations across tree-based...
Ian says Anthropic's researcherss have uncovered something that could alter the way we think about artificial intelligence forever.
I spent years building data pipelines, mostly in Snowflake, in a regulated banking environment. For most of that time, lineage was straightforward: data moves through a transformat...
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