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Opening the Neural Network “black box”.Continue reading on Medium »
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...
AI transparency is the practice of making an artificial intelligence system's data, model behavior...
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 »
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...
MIT researchers and collaborators found that AI explainability tools in the health sector can produce sharply different results depending on who uses them. When applied to skin dis...
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...
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...
How I turned influencer data into a practical MarTech decision-support systemContinue reading on Medium »
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...
What explainability means for language models, how ClinicalGPT currently approaches it, and what must be tested before an attribution…Continue reading on Medium »
A new method, called CW-Net, translates the reasoning process of an autonomous vehicle’s AI system into understandable concepts that explain its behavior.
Enterprise risk problems often start as data-platform issues. Challenges include fragmented signals, inconsistent definitions, missing context, weak lineage, and untimely alerts. W...
In one fraud-review scenario I worked through, an AI assistant looked reliable during demos because it explained risk signals clearly and gave reviewers useful summaries. The issue...
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...
As artificial intelligence (AI) becomes deeply embedded in financial services, traditional success m...
By Swagatam Sen, Founder & CEO, ControlOne Financial crime teams have been offered a choice for ...
Assistant Professor Pat Pataranutaporn describes a new interface that lets everyday users glimpse inside an AI's neural network before their chatbot ever says a word.
Seekr SVP Derek Britton has said AI outputs should trace back to a rule, record or source He cited TSA’s automated scanning as an example of AI freeing up staff time The FedCiv Sum...
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...
“The new PRD are the evals,” Xavi Amatriain, Expedia Group’s first chief AI and data officer, told the VB Transform 2026 audience last week in Menlo Park. “So basically, you encode...
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