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Testing Large Language Model (LLM) Vulnerabilities Using Adversarial Attacks
by Venkatesh Yadav July 19, 2023 Generative AI H2O LLM Studio Large language models LLM Limitations LLM Robustness LLM Safety Responsible AI

Adversarial analysis seeks to explain a machine learning model by understanding locally what changes need to be made to the input to change a model’s outcome. Depending on the context, adversarial results could be used as attacks, in which a change is made to trick a model into reaching a different outcome. Or they could […]

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A Brief Overview of AI Governance for Responsible Machine Learning Systems
by h2oai November 30, 2022 AI Governance Machine Learning Responsible AI

Our paper “A Brief Overview of AI Governance for Responsible Machine Learning Systems” was recently accepted to the Trustworthy and Socially Responsible Machine Learning (TSRML) workshop at NeurIPS 2022 (New Orleans). In this paper, we discuss the framework and value of AI Governance for organizations of all sizes, across all industries and domains. Our paper […]

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Using AI to unearth the unconscious bias in job descriptions
by h2oai January 19, 2021 Responsible AI Wave

“Diversity is the collective strength of any successful organization Unconscious Bias in Job Descriptions Unconscious bias is a term that affects us all in one way or the other. It is defined as the prejudice or unsupported judgments in favor of or against one thing, person, or group as compared to another, in a way […]

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H2O Driverless AI 1.9.1: Continuing to Push the Boundaries for Responsible AI
by Bruna Smith January 18, 2021 H2O Driverless AI Responsible AI

At, we have been busy. Not only do we have our most significant new software launch coming up (details here), but we also are thrilled to announce the latest release of our flagship enterprise platform H2O Driverless AI 1.9.1. With that said, let’s jump into what is new: Faster Python scoring pipelines with embedded […]

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The Importance of Explainable AI
The Importance of Explainable AI
by Bruna Smith October 30, 2020 Community Machine Learning Interpretability Responsible AI

This blog post was written by Nick Patience, Co-Founder & Research Director, AI Applications & Platforms at 451 Research, a part of S&P Global Market Intelligence From its inception in the mid-twentieth century, AI technology has come a long way. What was once purely the topic of science fiction and academic discussion is now a […]

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Building an AI Aware Organization
by h2oai October 26, 2020 Business Explainable AI Machine Learning Machine Learning Interpretability Responsible AI

Responsible AI is paramount when we think about models that impact humans, either directly or indirectly. All the models that are making decisions about people, be that about creditworthiness, insurance claims, HR functions, and even self-driving cars, have a huge impact on humans.  We recently hosted James Orton, Parul Pandey, and Sudalai Rajkumar for a […]

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The Challenges and Benefits of AutoML
by Bruna Smith October 14, 2020 AutoML H2O Driverless AI Machine Learning Responsible AI

Machine Learning and Artificial Intelligence have revolutionized how organizations are utilizing their data. AutoML or Automatic Machine Learning automates and improves the end-to-end data science process. This includes everything from cleaning the data, engineering features, tuning the model, explaining the model, and deploying it into production. AutoML accelerates your AI initiatives and can help make […]

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3 Ways to Ensure Responsible AI Tools are Effective
by Bruna Smith October 7, 2020 Explainable AI H2O Driverless AI Machine Learning Machine Learning Interpretability Responsible AI

Since we began our journey making tools for explainable AI (XAI) in late 2016, we’ve learned many lessons, and often the hard way. Through headlines, we’ve seen others grapple with the difficulties of deploying AI systems too. Whether it’s: a healthcare resource allocation system that likely discriminated against millions of black people data privacy violations […]

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5 Key Considerations for Machine Learning in Fair Lending
by Bruna Smith September 21, 2020 Financial Services Machine Learning Machine Learning Interpretability Responsible AI Shapley

This month, we hosted a virtual panel with industry leaders and explainable AI experts from Discover, BLDS, and to discuss the considerations in using machine learning to expand access to credit fairly and transparently and the challenges of governance and regulatory compliance. The event was moderated by Sri Ambati, Founder and CEO at […]

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From GLM to GBM – Part 2
by h2oai July 9, 2020 Data Science Explainable AI GBM GLM Machine Learning Interpretability Responsible AI Shapley

How an Economics Nobel Prize could revolutionize insurance and lending Part 2: The Business Value of a Better Model Introduction In Part 1, we proposed better revenue and managing regulatory requirements with machine learning (ML). We made the first part of the argument by showing how gradient boosting machines (GBM), a type of ML, can […]

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