Analytics Vidhya

AUGUST 2, 2022

The post Explainable AI using OmniXAI appeared first on Analytics Vidhya. Introduction In the modern day, where there is a colossal amount of data at our disposal, using ML models to make decisions has become crucial in sectors like healthcare, finance, marketing, etc. Many ML models are black boxes since it is difficult to […].

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Analytics Vidhya

OCTOBER 24, 2020

Introduction This Article Covers the use of an Explainable AI framework(Lime, Shap). The post Unveiling the Black Box model using Explainable AI(Lime, Shap) Industry use case. This article was published as a part of the Data Science Blogathon. appeared first on Analytics Vidhya.

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Analytics Vidhya

MARCH 14, 2023

The quality of AI is what matters most and is one of the vital causes of the failure of any business or organization. According to a survey or study, AI […] The post What are Explainability AI Techniques? Why do We Need it? appeared first on Analytics Vidhya.

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Analytics Vidhya

MAY 26, 2022

Introduction The ability to explain decisions is increasingly becoming important across businesses. Explainable AI is no longer just an optional add-on when using ML algorithms for corporate decision making. The post Adding Explainability to Clustering appeared first on Analytics Vidhya.

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Unite.AI

DECEMBER 19, 2024

Thats why explainability is such a key issue. People want to know how AI systems work, why they make certain decisions, and what data they use. The more we can explain AI, the easier it is to trust and use it. Large Language Models (LLMs) are changing how we interact with AI. Imagine an AI predicting home prices.

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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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Marktechpost

APRIL 24, 2024

To ensure practicality, interpretable AI systems must offer insights into model mechanisms, visualize discrimination rules, or identify factors that could perturb the model. Explainable AI (XAI) aims to balance model explainability with high learning performance, fostering human understanding, trust, and effective management of AI partners.

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Towards AI

FEBRUARY 3, 2024

(Left) Photo by Pawel Czerwinski on Unsplash U+007C (Right) Unsplash Image adjusted by the showcased algorithm Introduction It’s been a while since I created this package ‘easy-explain’ and published on Pypi. A few weeks ago, I needed an explainability algorithm for a YoloV8 model. The truth is, I couldn’t find anything.

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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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Towards AI

FEBRUARY 13, 2024

Author(s): Stavros Theocharis Originally published on Towards AI. 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. So, let’s import the libraries.

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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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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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Towards AI

NOVEMBER 1, 2023

Last Updated on November 2, 2023 by Editorial Team Author(s): Ahmed Fessi Originally published on Towards 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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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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Marktechpost

OCTOBER 31, 2024

However, explainability is an issue as they are ‘black boxes,’ so to say, hiding their inner working. It elicits the need to design models that allow researchers to understand how AI predictions are achieved so they can trust them in decisions involving materials discovery. Check out the Paper.

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AI News

OCTOBER 8, 2024

While more flexible, it lacks transparency: “The problem with this approach is that we don’t really know what the system learns, and it’s very difficult to explain its decision,” Canavotto notes. Kameswaran suggests developing audit tools for advocacy groups to assess AI hiring platforms for potential discrimination.

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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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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

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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Flipboard

FEBRUARY 1, 2023

AI image generators have been causing a stir in the creative industry for quite some time now, but the main issue is how these tools are undertaking …

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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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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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AI Weekly

OCTOBER 3, 2024

At present, we’re in the midst of a furore about the much-abused term ‘AI’, and time will tell whether this particular storm will be seen as a teacup resident. plos.org Sponsor Personalize your newsletter about AI Choose only the topics you care about, get the latest insights vetted from the top experts online!

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IBM Journey to AI blog

AUGUST 9, 2024

Explainability and interpretability Most generative AI models lack explainability , as it’s often difficult or impossible to understand the decision-making processes behind their results. Conversely, predictive AI estimates are more explainable because they’re grounded on numbers and statistics.

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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

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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Jay Alammar

MAY 3, 2021

Introducing the Explainable AI Cheat Sheet , your high-level guide to the set of tools and methods that helps humans understand AI/ML models and their predictions. I introduce the cheat sheet in this brief video:

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Marktechpost

JULY 6, 2023

This is where Interpretable (IAI) and Explainable (XAI) Artificial Intelligence techniques come into play, and the need to understand their differences become more apparent. On the other hand, explainable AI models are very complicated deep learning models that are too complex for humans to understand without the aid of additional methods.

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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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Unite.AI

DECEMBER 17, 2024

Humans can validate automated decisions by, for example, interpreting the reasoning behind a flagged transaction, making it explainable and defensible to regulators. Financial institutions are also under increasing pressure to use Explainable AI (XAI) tools to make AI-driven decisions understandable to regulators and auditors.

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AI News

APRIL 25, 2024

The current incarnation of Pryon has aimed to confront AI’s ethical quandaries through responsible design focused on critical infrastructure and high-stakes use cases. “[We We wanted to] create something purposely hardened for more critical infrastructure, essential workers, and more serious pursuits,” Jablokov explained.

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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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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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IBM Journey to AI blog

OCTOBER 12, 2023

Explainable AI — Explainable AI is achieved when an organization can confidently and clearly state what data an AI model used to perform its tasks. Key to explainable AI is the ability to automatically compile information on a model to better explain its analytics decision-making.

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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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