Unite.AI

AUGUST 25, 2023

One of the most significant issues highlighted is how the definition of responsible AI is always shifting, as societal values often do not remain consistent over time. Can focusing on Explainable AI (XAI) ever address this? ” How does our expectation of a frictionless experience potentially lead to dangerous AI?

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Unite.AI

MARCH 18, 2025

Certain large companies have control over a vast amount of data, which creates an uneven playing field wherein only a select few have access to information necessary to train AI models and drive innovation. That way, AI development is not concentrated in the hands of just a few major players. This is not how things should be.

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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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IBM Journey to AI blog

FEBRUARY 28, 2024

More specifically, the recent launch of IBM watsonx.governance helps public sector teams automate and address these areas, enabling them to direct, manage and monitor their organization’s AI activities.

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TransOrg Analytics

FEBRUARY 11, 2025

These are just a few ways Artificial Intelligence (AI) silently influences our daily lives. As AI continues integrating into every aspect of society, the need for Explainable AI (XAI) becomes increasingly important. What is Explainable AI? Why is Explainable AI Important?

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IBM Journey to AI blog

OCTOBER 16, 2023

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. Foundation models offer a breakthrough in AI capabilities to enable scalable and efficient deployment across various domains.

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Towards AI

OCTOBER 19, 2024

Yet, for all their sophistication, they often can’t explain their choices — this lack of transparency isn’t just frustrating — it’s increasingly problematic as AI becomes more integrated into critical areas of our lives. What is Explainability AI (XAI)?

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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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IBM Journey to AI blog

MARCH 4, 2024

Adopting an open technologies-based hybrid cloud platform enables an AI+ enterprise to make informed decisions without limiting its business. The scale and impact of next-generation AI emphasize the importance of governance and risk controls.

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ODSC - Open Data Science

JUNE 7, 2023

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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NYU Center for Data Science

JUNE 16, 2023

Motivated by applications in healthcare and criminal justice, Umang studies how to create algorithmic decision-making systems endowed with the ability to explain their behavior and adapt to a stakeholder’s expertise to improve human-machine team performance. His work has been covered in press (e.g., UK Parliament POSTnote , NIST ).

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Unite.AI

OCTOBER 25, 2024

The platform revolutionizes the quoting process for businesses by utilizing advanced AI technologies to automate what has traditionally been a labor-intensive and error-prone task. This automation begins with Data Extraction, employing OCR and AI to efficiently process customer emails and extract relevant information.

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NVIDIA

MARCH 1, 2024

In addition to complying with privacy and consumer protection laws, trustworthy AI models are tested for safety, security and mitigation of unwanted bias. Principles of Trustworthy AI Trustworthy AI principles are foundational to NVIDIA’s end-to-end AI development.

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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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Pickl AI

FEBRUARY 17, 2025

This capability allows businesses to make informed decisions based on data-driven insights, enhancing strategic planning and risk management. As organisations accumulate more data, ML algorithms can scale accordingly, ensuring that decision-making is based on comprehensive and up-to-date information.

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Unite.AI

JULY 5, 2024

Establishing strong information governance frameworks ensures data quality, security and regulatory compliance. Accountability and Transparency: Accountability in Gen AI-driven decisions involve multiple stakeholders, including developers, healthcare providers, and end users. Robust data management is another critical element.

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Marktechpost

DECEMBER 11, 2024

LG AI Research conducted extensive reviews to address potential legal risks like copyright infringement and personal information protection to ensure data compliance. Long-context benchmarks assessed the models capability to process and retrieve information from extended textual inputs, which is critical for RAG applications.

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Pickl AI

OCTOBER 8, 2024

This blog will explore the concept of XAI, its importance in fostering trust in AI systems, its benefits, challenges, techniques, and real-world applications. What is Explainable AI (XAI)? Explainable AI refers to methods and techniques that enable human users to comprehend and interpret the decisions made by AI systems.

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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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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. If you were Sarah, which algorithm would you choose?

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ODSC - Open Data Science

FEBRUARY 18, 2025

Prompt Engineers: Also known as AI Interaction Specialists, these experts craft and refine the prompts used to interact with and guide AI models, ensuring they generate high-quality, contextually relevant content and responses. Explainable AI (XAI) techniques are crucial for building trust and ensuring accountability.

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AI News

APRIL 25, 2024

He outlined a litany of potential pitfalls that must be carefully navigated—from AI hallucinations and emissions of falsehoods, to data privacy violations and intellectual property leaks from training on proprietary information. Pryon also emphasises explainable AI and verifiable attribution of knowledge sources.

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Pickl AI

SEPTEMBER 17, 2024

AI in Security Automation and Incident Response AI is revolutionising security automation and incident response by enabling faster, more efficient, and more accurate responses to cyber threats.

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The MLOps Blog

JUNE 27, 2023

Can you debug system information? Metadata management : Robust metadata management capabilities enable you to associate relevant information, such as dataset descriptions, annotations, preprocessing steps, and licensing details, with the datasets, facilitating better organization and understanding of the data. Can you compare images?

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Pickl AI

JULY 14, 2023

Even in the time of pandemic, AI has enabled in providing technical solutions to the people in terms of information inflow. Therefore, AI has been evolving since years now and is currently at its peak of development. Additionally, businesses have been able to encroach for a focused approach in their operations.

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Pickl AI

DECEMBER 10, 2024

As the global AI market, valued at $196.63 from 2024 to 2030, implementing trustworthy AI is imperative. This blog explores how AI TRiSM ensures responsible AI adoption. Key Takeaways AI TRiSM embeds fairness, transparency, and accountability in AI systems, ensuring ethical decision-making.

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IBM Journey to AI blog

OCTOBER 30, 2024

Moreover, LRRs and other industry frameworks, such as the National Institute of Standards and Technology (NIST), Information Technology Infrastructure Library (ITIL), and Control Objectives for Information and Related Technologies (COBIT), are constantly evolving. Furthermore, watsonx.ai

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