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. Interpretability — Explaining the meaning of a model/model decisions to humans.

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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

Their conversation spans a range of topics, including AI bias, the observability of AI systems and the practical implications of AI in business. The AI Podcast · Explainable AI: Insights from Arthur AI’s Adam Wenchel – Ep. 02:31: Real-world use cases of LLMs and generative AI in enterprises.

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Towards AI

FEBRUARY 13, 2024

Introduction It’s been a while since I created this package ‘easy-explain’ and published it on Pypi. GradCam is a widely used Explainable AI method that has been extensively discussed in both forums and literature. LayerCAM aggregates information across multiple layers for a more detailed heatmap.

Explainability 105

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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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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

JUNE 26, 2023

In addition to strict, reasonable and thorough regulations, developers should take steps to prevent AI-gone-wrong scenarios. Explainable AI — also known as white box AI — may solve transparency and data bias concerns. Explainable AI models are emerging algorithms allowing developers and users to access the model’s logic.

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Unite.AI

MARCH 6, 2025

It excels in performing logic-based problems, processing multiple steps of information, and offering solutions that are typically difficult for traditional models to manage. This success, however, has come at a cost, one that could have serious implications for the future of AI development.

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Unite.AI

JANUARY 20, 2025

Ensures Compliance : In industries with strict regulations, transparency is a must for explaining AI decisions and staying compliant. Helps Users Understand : Transparency makes AI easier to work with. Tools like explainable AI (XAI) and interpretable models can help translate complex outputs into clear, understandable insights.

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Marktechpost

OCTOBER 6, 2024

The proposed MMTD-Set enhances traditional IFDL datasets by integrating text descriptions with visual tampering information. Don’t Forget to join our 50k+ ML SubReddit Interested in promoting your company, product, service, or event to over 1 Million AI developers and researchers? Let’s collaborate!

Large Language Models 113

Large Language Models Explainability Explainable AI AI 113

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

Large Language Models Neural Network AI AI 113

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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Generative AI Large Language Models Automation AI 112

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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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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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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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. You can't really reengineer the design logic from the source code.

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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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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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AWS Machine Learning Blog

NOVEMBER 15, 2024

These safeguards can be created for multiple use cases and implemented across multiple FMs, depending on your application and responsible AI requirements. Such words can include offensive terms or undesirable outputs, like product or competitor information.

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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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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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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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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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Large Language Models AI AI Explainable AI 214

Unite.AI

SEPTEMBER 13, 2023

Additionally, the continuously expanding datasets used by ML algorithms complicate explainability further. The larger the dataset, the more likely the system is to learn from both relevant and irrelevant information and spew “AI hallucinations” – falsehoods that deviate from external facts and contextual logic, however convincingly.

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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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Unite.AI

MARCH 18, 2024

On the other hand, it has also led to challenges, including the misuse of AI-generated content by individuals with harmful intentions. Beyond these widely recognized dangers, AI-generated contents pose a subtle yet profound challenge to the integrity of AI systems.

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DataRobot Blog

FEBRUARY 14, 2022

DataRobot offers end-to-end explainability to make sure models are transparent at all stages of their lifecycle. In this post, we’ll walk you through DataRobot’s Explainable AI features in both our AutoML and MLOps products and use them to evaluate a model both pre- and post-deployment. Learn More About Explainable AI.

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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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Explainability Explainable AI AI Modeling Artificial Intelligence 52

IBM Journey to AI blog

JANUARY 25, 2024

A very human challenge: Assessing risk before model procurement or development Emerging AI regulations and action plans are increasingly underscoring the importance of algorithmic impact assessment forms. A common refrain might be, “How could my model be unfair if it is not gathering personally identifiable information (PII)?”

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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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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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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)?

Explainability 105

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Unite.AI

APRIL 22, 2024

This article explores how the integration of AI and telehealth is ushering in a new era of medical practices, transforming accessibility and efficiency of healthcare delivery. Telehealth refers to the delivery of healthcare services and information via telecommunications and digital communication technologies. What is Telehealth?

Chatbots 182

Chatbots Artificial Intelligence Artificial Intelligence Natural Language Processing 182

Pickl AI

JANUARY 12, 2025

For instance, in retail, AI models can be generated using customer data to offer real-time personalised experiences and drive higher customer engagement, consequently resulting in more sales. Aggregated, these methods will illustrate how data-driven, explainable AI empowers businesses to improve efficiency and unlock new growth paths.

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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. But how trustworthy is that training data?

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IBM Journey to AI blog

SEPTEMBER 18, 2023

Siloed processes can become integrated by using intelligent workflows, which help enable seamless and automated exchange of financial, informational and physical supply chain data in one distributed network. Additionally, artificial intelligence (AI) plays an important role.

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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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