Healthcare AI Governance Library - Pacific AI

Elevate Your NLP Models with Automated Data Augmentation for Enhanced Performance

Elevate Your NLP Models with Automated Data Augmentation for Enhanced Performance

Evaluating gender-occupational bias in AI language models using the WinoBias test, illustrated by a neural network interface connected to a human brain, representing bias detection and mitigation with the LangTest library.

Mitigating Gender-Occupational Stereotypes in AI: Evaluating Language Models with the Wino Bias Test through the Langtest Library

Magnifying glass reviewing structured AI documentation and compliance records, illustrating Pacific AI joining the Coalition for Health AI (CHAI) as a partner in the assurance provider certification process for trustworthy and transparent healthcare AI.

Detecting and Evaluating Sycophancy Bias: An Analysis of LLM and AI Solutions

Building responsible language models with the LangTest library, illustrating automated testing for bias, robustness, and safety in large language models to support trustworthy and governance-ready AI systems.

Unmasking Language Model Sensitivity in Negation and Toxicity Evaluations

Illustration of a policy tree with connected documents and analytics icons, representing how the Pacific AI Governance Policy Suite supports compliance with the HHS HTI-1 transparency rule through structured documentation, reporting, and AI governance controls.

Unveiling Bias in Language Models: Gender, Race, Disability, and Socioeconomic Perspectives

Automated testing framework for detecting demographic bias in clinical treatment plans generated by large language models, highlighting secure medical documents, validation checks, and responsible AI evaluation in healthcare.

Unmasking the Biases Within AI: How Gender, Ethnicity, Religion, and Economics Shape NLP and Beyond

Evaluating large language models for gender-occupational stereotypes using the Wino Bias Test, illustrated by a rocket launch symbolizing AI model assessment, bias detection, performance metrics, and responsible NLP evaluation.

Evaluating Large Language Models on Gender-Occupational Stereotypes Using the Wino Bias Test

Ethical implications of medical large language models in healthcare, showing clinical data flows, model decision layers, and governance controls to address transparency, safety, and responsible AI use.

Testing the Question Answering Capabilities of Large Language Models

Evaluating stereotype bias in large language models using LangTest, showing automated bias testing workflows, labeled datasets, evaluation pipelines, and responsible AI assessment for NLP models.

Evaluating Stereotype Bias with LangTest

Generative AI in Healthcare Survey 2024 infographic showing adoption of task-specific LLMs, open-source general-purpose LLMs, open-source task-specific models, and proprietary LLMs, highlighting trends discussed at the John Snow Labs NLP Summit.

John Snow Labs Announces Fifth Annual NLP Summit, the World’s Largest Gathering for the Applied NLP, LLM, and Generative AI Community

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