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RTRicardo Trevisandeepencanvas.hashnode.devMar 17 · 5 min read

Stochastic Parrots

In purely human work relationships there is always uncertainty, along with contextual doses of tolerance. Omission, forgetfulness, conflation — these present themselves under a layer of contextual judgment and tend to be socially absorbed. This absor...

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SRShruti Rajeshblog.zysec.aiJan 20 · 4 min read

AI Police Reports: ACLU's Technical Solutions

The ACLU’s 2025 white paper on AI-generated police reports didn’t just raise concerns—it exposed a fundamental tension in modern law enforcement: how do we balance efficiency with accountability when algorithms draft the first version of justice? The...

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SRShruti Rajeshblog.zysec.aiJan 20 · 3 min read

Hallucination Zero-Tolerance for High-Stakes AI

As we delve into the realm of high-stakes AI, where the margin for error is nonexistent, a pressing question arises: How do we ensure that AI systems, particularly in healthcare, legal, and financial sectors, operate with zero tolerance for hallucina...

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JKJeet Khamarthe-llm-lens.hashnode.devAug 30, 2025 · 9 min read

Probing the Thoughts of Large Language Models

Large language models can write poetry, solve math problems, and speak many languages. However, the challenge is that no one fully understands how they accomplish these tasks. These models are trained on vast amounts of text, and during this process,...

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DSDinesh Sahooblogs.copsiitbhu.co.inDec 21, 2024 · 18 min read

Explainable AI: Evolution And Current Advancements

Artificial Intelligence (AI) is all around us—powering recommendations, self-driving cars, and even medical diagnoses. But have you ever stopped to wonder how AI makes these decisions? As AI gets more advanced, it becomes harder to understand its rea...

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GDGabi Dobocanblog.telepat.ioNov 20, 2024 · 5 min read

Getting To Know GECo: Breaking Down Graph Neural Networks with a New Explainability Tool

Graph Neural Networks (GNNs) have been making waves in data science, helping us pick apart complex data that’s connected in intricate ways. Think social networks or molecular structures—places where relationships matter as much as the data points the...

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MDMohsen Davarynejadthegradient.ioJan 21, 2024 · 4 min read

Overfitting in ML models

Overfitting happens when a model is more complex than it should be and starts to fit the noise in the data (or some degree of that) instead of the underlying pattern. Overfitting leads to poor model performance on new and unseen data. While there is ...

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DDataChefblog.datachef.coFeb 7, 2022 · 16 min read

Assessing Pre-training Bias with AWS SageMaker Clarify

Introduction In the previous article, we discussed why explaining machine learning (ML) models is as important as their performance and how it benefits all the stakeholders. Now, we are going to see what explainability and fairness of ML models requi...

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DDataChefblog.datachef.coNov 3, 2021 · 7 min read

Why Bother Explaining a Black-Box Model?

Introduction In recent years, the availability of large volumes of data and computational resources, combined with the optimization technology, has caused deep learning models to succeed in numerous representation learning and decision-making problem...

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#explainability

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Trending tags this week

Stochastic Parrots

AI Police Reports: ACLU's Technical Solutions

Hallucination Zero-Tolerance for High-Stakes AI

Probing the Thoughts of Large Language Models

Explainable AI: Evolution And Current Advancements

Getting To Know GECo: Breaking Down Graph Neural Networks with a New Explainability Tool

Overfitting in ML models

Assessing Pre-training Bias with AWS SageMaker Clarify

Why Bother Explaining a Black-Box Model?