Unite.AI

SEPTEMBER 13, 2023

Increasingly though, large datasets and the muddled pathways by which AI models generate their outputs are obscuring the explainability that hospitals and healthcare providers require to trace and prevent potential inaccuracies. In this context, explainability refers to the ability to understand any given LLM’s logic pathways.

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Unite.AI

MARCH 20, 2025

Even AI-powered customer service tools can show bias, offering different levels of assistance based on a customers name or speech pattern. Lack of Transparency and Explainability Many AI models operate as “black boxes,” making their decision-making processes unclear.

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Unite.AI

JANUARY 1, 2025

AI is reshaping the world, from transforming healthcare to reforming education. Data is at the centre of this revolutionthe fuel that powers every AI model. Why It Matters As AI takes on more prominent roles in decision-making, data monocultures can have real-world consequences. Transparency also plays a significant role.

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Unite.AI

JANUARY 1, 2025

Similarly, in the United States, regulatory oversight from bodies such as the Federal Reserve and the Consumer Financial Protection Bureau (CFPB) means banks must navigate complex privacy rules when deploying AI models. A responsible approach to AI development is paramount to fully capitalize on AI, especially for banks.

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ODSC - Open Data Science

NOVEMBER 30, 2023

Healthcare systems are implementing AI, and patients and clinicians want to know how it works in detail. Explainable AI might be the solution everyone needs to develop a healthier, more trusting relationship with technology while expediting essential medical care in a highly demanding world. What Is Explainable AI?

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NVIDIA

MAY 1, 2024

enhances the performance of AI systems across various metrics like accuracy, explainability and fairness. In this episode of the NVIDIA AI Podcast , recorded live at GTC 2024, host Noah Kravitz sits down with Adam Wenchel, cofounder and CEO of Arthur, to discuss the challenges and opportunities of deploying generative AI.

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Flipboard

DECEMBER 3, 2024

When you visit a hospital, artificial intelligence (AI) models can assist doctors by analysing medical images or predicting patient outcomes based on …

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Unite.AI

DECEMBER 27, 2024

Heres the thing no one talks about: the most sophisticated AI model in the world is useless without the right fuel. Data-centric AI flips the traditional script. Instead of obsessing over squeezing incremental gains out of model architectures, its about making the data do the heavy lifting.

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AI News

MAY 29, 2024

In an interview ahead of the Intelligent Automation Conference , Ben Ball, Senior Director of Product Marketing at IBM , shed light on the tech giant’s latest AI endeavours and its groundbreaking new Concert product. IBM’s current focal point in AI research and development lies in applying it to technology operations.

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Unite.AI

JUNE 26, 2023

At the root of AI mistakes like these is the nature of AI models themselves. Most AI today use “black box” logic, meaning no one can see how the algorithm makes decisions. Black box AI lack transparency, leading to risks like logic bias , discrimination and inaccurate results.

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Unite.AI

JANUARY 20, 2025

Transparency = Good Business AI systems operate using vast datasets, intricate models, and algorithms that often lack visibility into their inner workings. This opacity can lead to outcomes that are difficult to explain, defend, or challengeraising concerns around bias, fairness, and accountability.

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IBM Journey to AI blog

AUGUST 9, 2024

Generative AI (gen AI) is artificial intelligence that responds to a user’s prompt or request with generated original content, such as audio, images, software code, text or video. Gen AI models are trained on massive volumes of raw data.

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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)? It’s particularly useful in natural language processing [3].

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Unite.AI

MARCH 5, 2025

He currently serves as the Chief Executive Officer of Carrington Labs , a leading provider of explainable AI-powered credit risk scoring and lending solutions. Can you explain how Carrington Labs' AI-powered risk scoring system differs from traditional credit scoring methods? anywhere near the model-creation process.

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Unite.AI

NOVEMBER 22, 2024

Artificial Intelligence (AI) is making its way into critical industries like healthcare, law, and employment, where its decisions have significant impacts. However, the complexity of advanced AI models, particularly large language models (LLMs), makes it difficult to understand how they arrive at those decisions.

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IBM Journey to AI blog

OCTOBER 12, 2023

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. Here’s what’s involved in making that happen.

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Unite.AI

MARCH 18, 2025

What are the key challenges AI teams face in sourcing large-scale public web data, and how does Bright Data address them? Scalability remains one of the biggest challenges for AI teams. Since AI models require massive amounts of data, efficient collection is no small task. This is not how things should be.

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Unite.AI

OCTOBER 20, 2023

AI models in production. Today, seven in 10 companies are experimenting with generative AI, meaning that the number of AI models in production will skyrocket over the coming years. As a result, industry discussions around responsible AI have taken on greater urgency. In 2022, companies had an average of 3.8

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Towards AI

JULY 24, 2024

Well, get ready because we’re about to embark on another exciting exploration of explainable AI, this time focusing on Generative AI. Before we dive into the world of explainability in GenAI, it’s worth noting that the tone of this article, like its predecessor, is intentionally casual and approachable.

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Unite.AI

APRIL 9, 2024

The adoption of Artificial Intelligence (AI) has increased rapidly across domains such as healthcare, finance, and legal systems. However, this surge in AI usage has raised concerns about transparency and accountability. Composite AI is a cutting-edge approach to holistically tackling complex business problems.

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Unite.AI

FEBRUARY 25, 2025

As AI systems increasingly power mission-critical applications across industries such as finance, defense, healthcare, and autonomous systems, the demand for trustworthy, explainable, and mathematically rigorous reasoning has never been higher. Raising the Bar in AI Reasoning Denis Ignatovich, Co-founder and Co-CEO of Imandra Inc.,

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Towards AI

JULY 24, 2024

Well, get ready because we’re about to embark on another exciting exploration of explainable AI, this time focusing on Generative AI. Before we dive into the world of explainability in GenAI, it’s worth noting that the tone of this article, like its predecessor, is intentionally casual and approachable.

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AI News

MARCH 7, 2024

Indeed, as Anthropic prompt engineer Alex Albert pointed out, during the testing phase of Claude 3 Opus, the most potent LLM (large language model) variant, the model exhibited signs of awareness that it was being evaluated. In addition, editorial guidance on AI has been updated to note that ‘all AI usage has active human oversight.’

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Marktechpost

JUNE 8, 2024

Despite performing remarkably well on various tasks, these models are often unable to provide a clear understanding of how specific visual changes affect ML decisions. Specifically, they do not typically explain higher-level characteristics like texture, shape, or size that might underlie the model’s decisions.

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Dlabs.ai

AUGUST 16, 2022

One of the major hurdles to AI adoption is that people struggle to understand how AI models work. This is the challenge that explainable AI solves. Explainable artificial intelligence shows how a model arrives at a conclusion. What is explainable AI? Let’s begin.

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Towards AI

AUGUST 31, 2023

Last Updated on September 1, 2023 by Editorial Team Author(s): Louis Bouchard Originally published on Towards AI. An introduction to explainable AI This member-only story is on us. Powerful artificial intelligence models like DALLE or ChatGPT are super useful and fun to use. Upgrade to access all of Medium.

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IBM Journey to AI blog

JUNE 26, 2023

Possibilities are growing that include assisting in writing articles, essays or emails; accessing summarized research; generating and brainstorming ideas; dynamic search with personalized recommendations for retail and travel; and explaining complicated topics for education and training. What is watsonx.governance?

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IBM Journey to AI blog

MAY 9, 2023

A lack of confidence to operationalize AI Many organizations struggle when adopting AI. According to Gartner , 54% of models are stuck in pre-production because there is not an automated process to manage these pipelines and there is a need to ensure the AI models can be trusted.

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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

JANUARY 25, 2024

How might this insight affect evaluation of AI models? Model (in)accuracy To quote a common aphorism, all models are wrong. This holds true in the areas of statistics, science and AI. Models created with a lack of domain expertise can lead to erroneous outputs. How are you making your model explainable?

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Unite.AI

JANUARY 23, 2025

Consequently, the foundational design of AI systems often fails to include the diversity of global cultures and languages, leaving vast regions underrepresented. Bias in AI typically can be categorized into algorithmic bias and data-driven bias. Explainable AI tools make spotting and correcting biases in real time easier.

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Marktechpost

JULY 6, 2023

After all, for academics to design even more robust models and repair the flaws of present models concerning bias and other concerns, obtaining a greater knowledge of how ML models make predictions is crucial. In other words, it is safe to say that an IAI model provides its own explanation.

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AI Weekly

OCTOBER 12, 2023

pitneybowes.com In The News AMD to acquire AI software startup in effort to catch Nvidia AMD said on Tuesday it plans to buy an artificial intelligence startup called Nod.ai nature.com Ethics The world's first real AI rules are coming soon. [Get your FREE REPORT.] as part of an effort to bolster its software capabilities.

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IBM Journey to AI blog

OCTOBER 16, 2023

It encompasses risk management and regulatory compliance and guides how AI is managed within an organization. Foundation models: The power of curated datasets Foundation models , also known as “transformers,” are modern, large-scale AI models trained on large amounts of raw, unlabeled data.

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IBM Journey to AI blog

FEBRUARY 28, 2024

IBM watsonx™ , an integrated AI, data and governance platform, embodies five fundamental pillars to help ensure trustworthy AI: fairness, privacy, explainability, transparency and robustness. This platform offers a seamless, efficient and responsible approach to AI development across various environments.

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Marktechpost

JULY 22, 2024

Current AV models focus mainly on binary classification, which often lacks transparency. This lack of explainability is a gap in academic interest and a practical concern. Analyzing the decision-making process of AI models is essential for building trust and reliability, particularly in identifying and addressing hidden biases.

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Unite.AI

JUNE 18, 2024

GANs gave rise to DALL-E , an AI model that generates images based on textual descriptions. Looking further ahead, one critical area of focus is Explainable AI , which aims to make AI decisions transparent and understandable. On the other hand, VAEs are used primarily in unsupervised learning.

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