DataRobot Blog

DECEMBER 6, 2022

With a goal to help data science teams learn about the application of AI and ML, DataRobot shares helpful, educational blogs based on work with the world’s most strategic companies. Explainable AI for Transparent Decisions. Data Scientists of Varying Skillsets Learn AI – ML Through Technical Blogs.

Data Scientist 145

Data Scientist ML Explainability Artificial Intelligence 145

Flipboard

FEBRUARY 14, 2025

Hemant Madaan, an expert in AI/ML and CEO of JumpGrowth, explores the ethical implications of advanced language models. Artificial intelligence (AI) has become a cornerstone of modern business operations, driving efficiencies and delivering insights across various sectors. However, as AI systems

Explainable AI 128

Explainable AI Explainability Algorithm Artificial Intelligence 128

Marktechpost

OCTOBER 6, 2024

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? The post FakeShield: An Explainable AI Framework for Universal Image Forgery Detection and Localization Using Multimodal Large Language Models appeared first on MarkTechPost.

Large Language Models 113

Large Language Models Explainability Explainable AI AI 113

ML @ CMU

MARCH 31, 2023

Our work further motivates novel directions for developing and evaluating tools to support human-ML interactions. Model explanations have been touted as crucial information to facilitate human-ML interactions in many real-world applications where end users make decisions informed by ML predictions.

ML 98

ML Explainability Algorithm Machine Learning 98

IBM Journey to AI blog

OCTOBER 16, 2023

An AI governance framework ensures the ethical, responsible and transparent use of AI and machine learning (ML). It encompasses risk management and regulatory compliance and guides how AI is managed within an organization. The development and use of these models explain the enormous amount of recent AI breakthroughs.

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Metadata Explainability Automation Explainable AI 220

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:

Explainable AI 52

Explainable AI Explainability ML AI 52

TransOrg Analytics

FEBRUARY 11, 2025

As AI continues integrating into every aspect of society, the need for Explainable AI (XAI) becomes increasingly important. Understanding the AI Black Box Problem AI enables machines to mimic human intelligence by learning, reasoning, and making decisions. What is Explainable AI?

Explainable AI 52

Explainable AI Explainability Convolutional Neural Networks Neural Network 52

Unite.AI

APRIL 9, 2024

Composite AI is a cutting-edge approach to holistically tackling complex business problems. These techniques include Machine Learning (ML), deep learning , Natural Language Processing (NLP) , Computer Vision (CV) , descriptive statistics, and knowledge graphs.

Composite AI 176

Composite AI Explainability Neural Network Explainable AI 176

Marktechpost

JULY 6, 2023

As a result of recent technological advances in machine learning (ML), ML models are now being used in a variety of fields to improve performance and eliminate the need for human labor. This is why when Explainable AI models can give a clear idea of why a decision was made but not how it arrived at that decision.

Explainable AI 52

Explainable AI Explainability Neural Network Machine Learning 52

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.

Explainable AI 52

Explainable AI Explainability Deep Learning Neural Network 52

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

Explainability Explainable AI Natural Language Processing Prompt Engineer 105

IBM Journey to AI blog

MAY 9, 2023

The solution: IBM watsonx.governance Coming soon, watsonx.governance is an overarching framework that uses a set of automated processes, methodologies and tools to help manage an organization’s AI use.

Metadata 227

Metadata Explainability Automation Responsible AI 227

IBM Journey to AI blog

AUGUST 11, 2023

Machine learning (ML), a subset of artificial intelligence (AI), is an important piece of data-driven innovation. Today, 35% of companies report using AI in their business, which includes ML, and an additional 42% reported they are exploring AI, according to the IBM Global AI Adoption Index 2022.

Data Science 170

Data Science DevOps Machine Learning Data Scientist 170

Mlearning.ai

FEBRUARY 6, 2023

There are plenty of techniques to help reduce overfitting in ML models. Additionally, multiple different models could be trained to identify AI-Generated Text in different subject matters, reducing the need for generalization. This is why we need Explainable AI (XAI).

Explainable AI 52

Explainable AI Explainability ChatGPT Chatbots 52

ODSC - Open Data Science

MARCH 10, 2025

Building Multimodal AI Agents: Agentic RAG with Vision-Language Models Suman Debnath, Principal AI/ML Advocate at Amazon WebServices Building a truly intelligent AI assistant requires overcoming the limitations of native Retrieval-Augmented Generation (RAG) models, especially when handling diverse data types like text, tables, and images.

Data Scientist 52

Data Scientist Data Science LLM Machine Learning 52

Marktechpost

OCTOBER 15, 2024

XAI, or Explainable AI, brings about a paradigm shift in neural networks that emphasizes the need to explain the decision-making processes of neural networks, which are well-known black boxes. Don’t Forget to join our 50k+ ML SubReddit. If you like our work, you will love our newsletter.

Explainability 56

Explainability Explainable AI Python Neural Network 56

IBM Journey to AI blog

FEBRUARY 8, 2024

In addition, stakeholders from corporate boards to consumers are prioritizing trust, transparency, fairness and accountability when it comes to AI.

Artificial Intelligence 223

Artificial Intelligence Artificial Intelligence AI AI 223

ODSC - Open Data Science

MAY 30, 2023

Be sure to check out his talk, “ ML Applications in Asset Allocation and Portfolio Management ,” there! The year 2022 presented two significant turnarounds for tech: the first one is the immediate public visibility of generative AI due to ChatGPT. Editor’s note: Peter Schwendner, PhD is a speaker for ODSC Europe this June.

ML 52

ML Data Science Machine Learning Explainable AI 52

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?

Explainable AI 52

Explainable AI Explainability Neural Network Machine Learning 52

ODSC - Open Data Science

OCTOBER 21, 2024

Fortunately, there are many tools for ML evaluation and frameworks designed to support responsible AI development and evaluation. This topic is closely aligned with the Responsible AI track at ODSC West  — an event where experts gather to discuss innovations and challenges in AI.

Responsible AI 52

Responsible AI ML Data Scientist Machine Learning 52

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. Recent advancements in machine learning have been actively used to improve the domain of healthcare. Also, don’t forget to follow us on Twitter.

Machine Learning 128

Machine Learning AI Modeling Explainability Computer Vision 128

Pickl AI

FEBRUARY 17, 2025

This scenario highlights a common reality in the Machine Learning landscape: despite the hype surrounding ML capabilities, many projects fail to deliver expected results due to various challenges. Statistics reveal that 81% of companies struggle with AI-related issues ranging from technical obstacles to economic concerns.

Machine Learning 52

Machine Learning Algorithm Explainability Data Quality 52

Marktechpost

AUGUST 13, 2024

Integrating AI and Human Expertise for Sustainable Agriculture and Forestry: The global shift towards digital transformation is largely driven by advances in AI, particularly statistical ML. This approach is crucial in agriculture and forestry, where complex, real-world tasks benefit from human conceptual understanding.

Robotics 105

Robotics Explainability Explainable AI Categorization 105

Marktechpost

APRIL 7, 2024

A team of researchers has introduced Effector to address the need for explainable AI techniques in machine learning, especially in crucial domains like healthcare and finance. Effector is a Python library that aims to mitigate the limitations of existing methods by providing regional feature effect methods.

Machine Learning 130

Machine Learning Python Explainability Explainable AI 130

Unite.AI

SEPTEMBER 13, 2023

Without a way to see the ‘thought process’ that an AI algorithm takes, human operators lack a thorough means of investigating its reasoning and tracing potential inaccuracies. Additionally, the continuously expanding datasets used by ML algorithms complicate explainability further.

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Explainability Generative AI AI Tools Algorithm 273

Pickl AI

JANUARY 29, 2024

As we navigate this landscape, the interconnected world of Data Science, Machine Learning, and AI defines the era of 2024, emphasising the importance of these fields in shaping the future. ’ As we navigate the expansive tech landscape of 2024, understanding the nuances between Data Science vs Machine Learning vs ai.

Data Science 52

Data Science ML Machine Learning Artificial Intelligence 52

Marktechpost

MARCH 11, 2024

These matrices are leveraged to develop class-agnostic and class-specific tools for explainable AI of Mamba models. Join our 38k+ ML SubReddit , 41k+ Facebook Community, Discord Channel , and LinkedIn Gr oup. Don’t Forget to join our Telegram Channel You may also like our FREE AI Courses….

Machine Learning 135

Machine Learning Explainability Neural Network Computer Vision 135

How to Learn Machine Learning

APRIL 4, 2025

AI and Cybersecurity: Now, AI is a critical tool in cybersecurity, and AI-driven security systems can detect anomalies, predict breaches, and respond to threats in real-time. ML algorithms will analyze vast datasets and identify patterns which indicate potential cyberattacks, and reduce response times and prevent data breaches.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Automation Explainable AI 52

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.

Explainable AI 93

Explainable AI Data Science Responsible AI Machine Learning 93

AWS Machine Learning Blog

NOVEMBER 15, 2024

To help with fairness in AI applications that are built on top of Amazon Bedrock, application developers should explore model evaluation and human-in-the-loop validation for model outputs at different stages of the machine learning (ML) lifecycle. Maria Lehtinen is a solutions architect for public sector customers in the Nordics.

Responsible AI 104

Responsible AI Prompt Engineer Prompt Engineering AI 104

ODSC - Open Data Science

JANUARY 29, 2025

Explainable AI(XAI) Explainable AI emphasizes transparency and interpretability, enabling users to understand how AI models arrive at decisions. Techniques such as embodied AI, multimodal learning, knowledge graphs, reinforcement learning, and explainable AI are paving the way for more grounded and reliablesystems.

Robotics 40

Robotics Explainable AI Data Scientist Explainability 40

IBM Journey to AI blog

SEPTEMBER 20, 2023

Foundational models (FMs) are marking the beginning of a new era in machine learning (ML) and artificial intelligence (AI) , which is leading to faster development of AI that can be adapted to a wide range of downstream tasks and fine-tuned for an array of applications. IBM watsonx consists of the following: IBM watsonx.ai

Large Language Models 238

Large Language Models DevOps Data Science AI Modeling 238

AWS Machine Learning Blog

MARCH 6, 2025

About the Author Andre Boaventura is a Principal AI/ML Solutions Architect at AWS, specializing in generative AI and scalable machine learning solutions. Andre works closely with global system integrators (GSIs) and customers across industries to architect and implement cutting-edge AI/ML solutions to drive business value.

Generative AI 106

Generative AI Large Language Models Automation AI 106

ODSC - Open Data Science

JUNE 1, 2023

Real-Time ML with Spark and SBERT, AI Coding Assistants, Data Lake Vendors, and ODSC East Highlights Getting Up to Speed on Real-Time Machine Learning with Spark and SBERT Learn more about real-time machine learning by using this approach that uses Apache Spark and SBERT.

ML 52

ML Data Scientist Big Data Data Science 52

Marktechpost

MAY 8, 2024

Among the main advancements in AI, seven areas stand out for their potential to revolutionize different sectors: neuromorphic computing, quantum computing for AI, Explainable AI (XAI), AI-augmented design and Creativity, Autonomous Vehicles and Robotics, AI in Cybersecurity and AI for Environmental Sustainability.

Artificial Intelligence 130

Artificial Intelligence Artificial Intelligence Robotics Machine Learning 130

IBM Journey to AI blog

JULY 6, 2023

Machine learning (ML) is a subset of artificial intelligence (AI) that focuses on learning from what the data science comes up with. Some examples of data science use cases include: An international bank uses ML-powered credit risk models to deliver faster loans over a mobile app. What is machine learning?

Data Science 175

Data Science Machine Learning Big Data Data Mining 175

Marktechpost

AUGUST 24, 2024

EXplainable AI (XAI) has become a critical research domain since AI systems have progressed to being deployed in essential sectors such as health, finance, and criminal justice. Also, don’t forget to follow us on Twitter and join our Telegram Channel and LinkedIn Gr oup. If you like our work, you will love our newsletter.

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Python Explainability Explainable AI AI 107