Healthcare AI Governance Library - Pacific AI

LangTest: A comprehensive evaluation library for custom LLM and NLP models

LangTest: A comprehensive evaluation library for custom LLM and NLP models

Identifying and mitigating bias in AI recruiting models, featuring data science and HR technology experts discussing fairness, transparency, and responsible AI practices in hiring and talent selection systems.

Identifying and Mitigating Bias in AI Models for Recruiting

Automated testing of bias, fairness, and robustness in generative AI solutions, highlighting responsible AI evaluation with expert insights on model reliability, risk detection, and governance-ready validation.

Automated Testing of Bias, Fairness, and Robustness of Generative 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.

Building Responsible Language Models with the LangTest Library

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.

The Ethical Implications of Medical LLMs in Healthcare

LangTest evaluation workflow for custom LLM and NLP models, showing automated testing pipelines, dataset augmentation, and before-and-after performance metrics for safety, robustness, and model quality assessment.

LangTest: Unveiling & Fixing Biases with End-to-End NLP Pipelines

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.

Automatically Testing for Demographic Bias in Clinical Treatment Plans Generated by Large Language Models

Robustness testing of named entity recognition (NER) models using LangTest, illustrating document processing, model evaluation dashboards, and reliability checks beyond accuracy in NLP systems.

Beyond Accuracy: Robustness Testing of Named Entity Recognition Models with LangTest

Automated data augmentation for NLP models, showing an AI assistant on a digital platform with performance metrics and data elements, highlighting improved model accuracy, robustness, and training efficiency.

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

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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