ODSC - Open Data Science

FEBRUARY 27, 2024

Interactive Explainable AI Meg Kurdziolek, PhD | Staff UX Researcher | Intrinsic.ai Although current explainable AI techniques have made significant progress toward enabling end-users to understand the why behind a prediction, to effectively build trust with an AI system we need to take the next step and make XAI tools interactive.

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IBM Journey to AI blog

DECEMBER 4, 2023

In addition, the CPO AI Ethics Project Office supports all of these initiatives, serving as a liaison between governance roles, supporting implementation of technology ethics priorities, helping establish AI Ethics Board agendas and ensuring the board is kept up to date on industry trends and company strategy.

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IBM Journey to AI blog

OCTOBER 16, 2023

. “Foundation models make deploying AI significantly more scalable, affordable and efficient.” It’s essential for an enterprise to work with responsible, transparent and explainable AI, which can be challenging to come by in these early days of the technology. ” Are foundation models trustworthy?

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NYU Center for Data Science

JUNE 16, 2023

In addition to working on his advanced degree, Umang is a Research Associate on the Safe and Ethical AI Team at the Alan Turing Institute. He is an Advisor at the Responsible AI Institute and has served in mentoring roles as a Thesis Co-Supervisor and Teaching Assistant at the University of Cambridge. By Meryl Phair

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IBM Journey to AI blog

FEBRUARY 8, 2023

Gartner predicts that the market for artificial intelligence (AI) software will reach almost $134.8 Achieving Responsible AI As building and scaling AI models for your organization becomes more business critical, achieving Responsible AI (RAI) should be considered a highly relevant topic. billion by 2025.

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IBM Journey to AI blog

MARCH 4, 2024

The scale and impact of next-generation AI emphasize the importance of governance and risk controls. An AI+ enterprise mitigates potential harm by implementing robust measures to secure, monitor and explain AI models, as well as monitoring governance, risk and compliance controls across the hybrid cloud environment.

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NVIDIA

MARCH 1, 2024

Transparency in AI is a set of best practices, tools and design principles that helps users and other stakeholders understand how an AI model was trained and how it works. Explainable AI , or XAI, is a subset of transparency covering tools that inform stakeholders how an AI model makes certain predictions and decisions.

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ODSC - Open Data Science

JUNE 8, 2023

Keynotes Both in-person and virtually, we had some amazing keynote speakers that told the audience about their research, expertise, use cases, or state-of-the-art developments.

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Unite.AI

APRIL 9, 2024

As organizations strive for responsible and effective AI, Composite AI stands at the forefront, bridging the gap between complexity and clarity. The Need for Explainability The demand for Explainable AI arises from the opacity of AI systems, which creates a significant trust gap between users and these algorithms.

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Viso.ai

FEBRUARY 12, 2024

True to its name, Explainable AI refers to the tools and methods that explain AI systems and how they arrive at a certain output. Artificial Intelligence (AI) models assist across various domains, from regression-based forecasting models to complex object detection algorithms in deep learning.

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Towards AI

OCTOBER 7, 2023

Last Updated on October 9, 2023 by Editorial Team Author(s): Lye Jia Jun Originally published on Towards AI. Balancing Ethics and Innovation: An Introduction to the Guiding Principles of Responsible AI Sarah, a seasoned AI developer, found herself at a moral crossroads. The other safeguards personal data but lacks speed.

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Pickl AI

JULY 14, 2023

Robotics also witnessed advancements, with AI-powered robots becoming more capable in navigation, manipulation, and interaction with the physical world. Explainable AI and Ethical Considerations (2010s-present): As AI systems became more complex and influential, concerns about transparency, fairness, and accountability arose.

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IBM Journey to AI blog

OCTOBER 12, 2023

But the implementation of AI is only one piece of the puzzle. The tasks behind efficient, responsible AI lifecycle management The continuous application of AI and the ability to benefit from its ongoing use require the persistent management of a dynamic and intricate AI lifecycle—and doing so efficiently and responsibly.

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Unite.AI

JANUARY 12, 2024

Competitions also continue heating up between companies like Google, Meta, Anthropic and Cohere vying to push boundaries in responsible AI development. The Evolution of AI Research As capabilities have grown, research trends and priorities have also shifted, often corresponding with technological milestones.

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Snorkel AI

MARCH 19, 2024

Vertex AI, Google’s comprehensive AI platform, plays a pivotal role in ensuring a safe, reliable, secure, and responsible AI environment for production-level applications. Vertex AI provides a suite of tools and services that cater to the entire AI lifecycle, from data preparation to model deployment and monitoring.

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Snorkel AI

MARCH 19, 2024

Vertex AI, Google’s comprehensive AI platform, plays a pivotal role in ensuring a safe, reliable, secure, and responsible AI environment for production-level applications. Vertex AI provides a suite of tools and services that cater to the entire AI lifecycle, from data preparation to model deployment and monitoring.

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The MLOps Blog

JUNE 27, 2023

Recommended for you A Comprehensive Guide on How to Monitor Your Models in Production Responsible AI You can use responsible AI tools to deploy ML models through ethical, fair, and accountable techniques.

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IBM Journey to AI blog

FEBRUARY 28, 2024

Governments must comprehend and manage the full AI lifecycle effectively, and leaders should be able to easily explain what data was used to train and fine-tune models, as well as how the models reached their outcomes.

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Unite.AI

NOVEMBER 29, 2023

For example, Algorithmic Fact-Checking Solutions, including Explainable AI (XAI) , assume a central role by providing a comprehensive overview of AI-driven techniques. The Bottom Line In conclusion, AI watchdogs are indispensable in safeguarding elections and adapting to evolving disinformation tactics.

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