Towards AI

MARCH 17, 2024

It is also garnering massive popularity in organizations and enterprises, with every corner of every business implementing LLMs, Stable Diffusion, and the next trendy AI product. Alongside this, there is a second boom in XAI or Explainable AI. We will then explore some techniques for building glass-box or explainable models.

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Marktechpost

AUGUST 7, 2024

The Role of Explainable AI in In Vitro Diagnostics Under European Regulations: AI is increasingly critical in healthcare, especially in vitro diagnostics (IVD). The European IVDR recognizes software, including AI and ML algorithms, as part of IVDs.

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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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Marktechpost

OCTOBER 6, 2024

To address these challenges, researchers are exploring Multimodal Large Language Models (M-LLMs) for more explainable IFDL, enabling clearer identification and localization of manipulated regions. Although these methods achieve satisfactory performance, they need more explainability and help to generalize across different datasets.

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Unite.AI

DECEMBER 27, 2024

While massive, overly influential datasets can enhance model performance , they often include redundant or noisy information that dilutes effectiveness. With only the most informative data points for labeling being selected, active learning minimizes resource expenditure while maximizing dataset relevance.

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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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Unite.AI

JUNE 26, 2023

When the patient independently uses an AI tool, an accident can be their fault. AI gone wrong isn’t always due to a technical error. For instance, maybe a doctor thoroughly explains an AI tool to their patient, but they ignore safety instructions or input incorrect data.

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Unite.AI

JANUARY 1, 2025

For example, an AI model trained on biased or flawed data could disproportionately reject loan applications from certain demographic groups, potentially exposing banks to reputational risks, lawsuits, regulatory action, or a mix of the three. The average cost of a data breach in financial services is $4.45

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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

NOVEMBER 22, 2024

This “black box” nature of AI raises concerns about fairness, reliability, and trust—especially in fields that rely heavily on transparent and accountable systems. It helps explain how AI models, especially LLMs, process information and make decisions.

Large Language Models 113

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Unite.AI

AUGUST 25, 2023

Can focusing on Explainable AI (XAI) ever address this? To engineers, explainable AI is currently thought of as a group of technological constraints and practices, aimed at making the models more transparent to people working on them. They need explainability to be able to push back in their own defense.

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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

AUGUST 9, 2024

Predictive AI models enhance the speed and precision of predictive analytics and are typically used for business forecasting to project sales, estimate product or service demand, personalize customer experiences and optimize logistics. What’s the difference between generative AI and predictive AI?

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IBM Journey to AI blog

OCTOBER 12, 2023

But the implementation of AI is only one piece of the puzzle. Checkpoints can be created throughout the AI lifecycle to prevent or mitigate bias and drift. Documentation can also be generated and maintained with information such as a model’s data origins, training methods and behaviors.

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AWS Machine Learning Blog

NOVEMBER 15, 2024

At AWS, we are committed to developing AI responsibly , taking a people-centric approach that prioritizes education, science, and our customers, integrating responsible AI across the end-to-end AI lifecycle. What constitutes responsible AI is continually evolving.

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Unite.AI

DECEMBER 13, 2023

The future involves human-AI collaboration to tackle evolving trends and threats in 2024. Importance of Staying Updated on Trends Staying updated on AI trends is crucial because it keeps you informed about the latest advancements, ensuring you remain at the forefront of technological innovation.

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IBM Journey to AI blog

APRIL 11, 2023

Although some third-party vendor information may be proprietary, the evaluation team should still review these processes and establish safeguards for vendors. It is crucial that proprietary AI is transparent, and the team should work to include diversity, equity, and inclusion in the hiring process.

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Unite.AI

AUGUST 13, 2024

This allowed Microsoft’s representatives to attend board meetings and access confidential information. The close ties between the two companies and Microsoft's ability to access confidential information attracted scrutiny from regulators regarding fair competition and market practices. However, they would not possess voting rights.

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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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Marktechpost

JULY 22, 2024

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. This is a critical limitation as the demand for explainable AI grows.

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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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DataRobot Blog

FEBRUARY 14, 2022

With any AI solution , you want it to be accurate. But just as important, you want it to be explainable. Explainability requirements continue after the model has been deployed and is making predictions. DataRobot offers end-to-end explainability to make sure models are transparent at all stages of their lifecycle.

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Dlabs.ai

AUGUST 16, 2022

This is the challenge that explainable AI solves. Explainable artificial intelligence shows how a model arrives at a conclusion. What is explainable AI? Explainable artificial intelligence (or XAI, for short) is a process that helps people understand an AI model’s output. Let’s begin.

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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

OCTOBER 16, 2023

Foundation models are widely used for ML tasks like classification and entity extraction, as well as generative AI tasks such as translation, summarization and creating realistic content. The development and use of these models explain the enormous amount of recent AI breakthroughs. Increase trust in AI outcomes.

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Pickl AI

JANUARY 12, 2025

Introduction Are you struggling to decide between data-driven practices and AI-driven strategies for your business? Besides, there is a balance between the precision of traditional data analysis and the innovative potential of explainable artificial intelligence.

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IBM Journey to AI blog

JANUARY 25, 2024

The goal of these forms is to capture critical information about AI models so that governance teams can assess and address their risks before deploying them. How are you making your model explainable? A common refrain might be, “How could my model be unfair if it is not gathering personally identifiable information (PII)?”

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IBM Journey to AI blog

AUGUST 17, 2023

That’s why the US Open will also use watsonx.governance to direct, manage and monitor its AI activities. This year, IBM AI Draw Analysis helps them make more data-informed predictions by providing a statistical factor (a draw ranking) for each player in the men’s and women’s singles events.

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Mlearning.ai

FEBRUARY 6, 2023

This is why we need Explainable AI (XAI). From uncovering patterns of human behavior to predicting the spread of ideas and information, the application of graph theory in social network analysis holds immense promise. The “Obvious” Solution One potential solution to the above conundrum is to identify word importance.

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Unite.AI

JULY 14, 2024

Issues with Transparency: Users were not informed exactly how their data would be used, creating a trust deficit. Additionally, Meta argued that it had timely informed users through various communication channels and that its AI practices seek to enhance user experience without compromising privacy.

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AWS Machine Learning Blog

MARCH 6, 2025

The Multi-Agent City Information System demonstrated in this post exemplifies the potential of agent-based architectures to create sophisticated, adaptable, and highly capable AI applications. LangGraph is essential to our solution by providing a well-organized method to define and manage the flow of information between agents.

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IBM Journey to AI blog

FEBRUARY 8, 2024

Not complying with the EU AI Act can be costly: 7.5 of a company’s total worldwide annual turnover (whichever is higher) for the supply of incorrect information. 15 million euros or 3% of a company’s total worldwide annual turnover (whichever is higher) for violations of the EU AI Act’s obligations.

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Mlearning.ai

DECEMBER 31, 2023

Principles of Explainable AI( Source ) Imagine a world where artificial intelligence (AI) not only makes decisions but also explains them as clearly as a human expert. This isn’t a scene from a sci-fi movie; it’s the emerging reality of Explainable AI (XAI). What is Explainable AI?

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IBM Journey to AI blog

MARCH 7, 2023

The specific approach we took required the use of both AI and edge computing to create a predictive model that could process years of anonymized data to help doctors make informed decisions. We wanted to be able to help them observe and monitor the thousands of data points available to make informed decisions.

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Pickl AI

OCTOBER 8, 2024

Summary: This blog discusses Explainable Artificial Intelligence (XAI) and its critical role in fostering trust in AI systems. One of the most effective ways to build this trust is through Explainable Artificial Intelligence (XAI). What is Explainable AI (XAI)? What is Explainable AI (XAI)?

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Marktechpost

JUNE 8, 2024

Google researchers introduced a novel framework, StylEx, that leverages generative AI to address the challenges in the field of medical imaging, especially focusing on the lack of explainability in AI models. This step confirms that the images contain relevant information for the task.

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