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 116

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AI News

OCTOBER 8, 2024

As artificial intelligence systems increasingly permeate critical decision-making processes in our everyday lives, the integration of ethical frameworks into AI development is becoming a research priority. Kameswaran suggests developing audit tools for advocacy groups to assess AI hiring platforms for potential discrimination.

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Unite.AI

DECEMBER 27, 2024

On the other hand, well-structured data allows AI systems to perform reliably even in edge-case scenarios , underscoring its role as the cornerstone of modern AI development. Another promising development is the rise of explainable data pipelines.

Data Quality 130

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Unite.AI

DECEMBER 19, 2024

By leveraging multimodal AI, financial institutions can anticipate customer needs, proactively address issues, and deliver tailored financial advice, thereby strengthening customer relationships and gaining a competitive edge in the market.

Automation 147

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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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Unite.AI

OCTOBER 20, 2023

However, only around 20% have implemented comprehensive programs with frameworks, governance, and guardrails to oversee AI model development and proactively identify and mitigate risks. Given the fast pace of AI development, leaders should move forward now to implement frameworks and mature processes.

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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

This content often fills the gap when data is scarce or diversifies the training material for AI models, sometimes without full recognition of its implications. While this expansion enriches the AI development landscape with varied datasets, it also introduces the risk of data contamination.

AI 189

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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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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)?

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IBM Journey to AI blog

OCTOBER 16, 2023

. “Foundation models make deploying AI significantly more scalable, affordable and efficient.” 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. ” Are foundation models trustworthy?

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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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Unite.AI

SEPTEMBER 13, 2023

AI developers for highly regulated industries should therefore exercise control over data sources to limit potential mistakes. Recent news surrounding the pitfalls of near limitless data-scraping for training LLMs, leading to lawsuits for copyright infringement , has brought these issues to the forefront.

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Unite.AI

JULY 2, 2024

Navigating this new, complex landscape is a legal obligation and a strategic necessity, and businesses using AI will have to reconcile their innovation ambitions with rigorous compliance requirements. GDPR's stringent data protection standards present several challenges for businesses using personal data in AI.

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ODSC - Open Data Science

MARCH 10, 2025

Walk away with practical approaches to designing robust evaluation frameworks that ensure AI systems are measurable, reliable, and deployment-ready. Explainable AI for Decision-Making Applications Patrick Hall, Assistant Professor at GWSB and Principal Scientist at HallResearch.ai

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Aiiot Talk

JULY 7, 2023

Black-box AI poses a serious concern in the aviation industry. In fact, explainability is a top priority laid out in the European Union Aviation Safety Administration’s first-ever AI roadmap. Explainable AI, sometimes called white-box AI, is designed to have high transparency so logic processes are accessible.

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NVIDIA

MARCH 1, 2024

Trustworthy AI initiatives recognize the real-world effects that AI can have on people and society, and aim to channel that power responsibly for positive change. What Is Trustworthy AI? Trustworthy AI is an approach to AI development that prioritizes safety and transparency for those who interact with it.

AI 132

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Unite.AI

DECEMBER 1, 2023

Alex Ratner is the CEO & Co-Founder of Snorkel AI , a company born out of the Stanford AI lab. Snorkel AI makes AI development fast and practical by transforming manual AI development processes into programmatic solutions. This stands in contrast to—but works hand-in-hand with—model-centric AI.

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Towards AI

APRIL 2, 2024

On the other hand, new developments in techniques such as model merging (see story below from Sakana) can provide a new avenue for affordable development and improvement of open-source models. Hence, we are focused on making AI more accessible and releasing AI learning materials and courses! Why should you care?

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Unite.AI

JANUARY 12, 2024

Competitions also continue heating up between companies like Google, Meta, Anthropic and Cohere vying to push boundaries in responsible AI development. The Evolution of AI Research As capabilities have grown, research trends and priorities have also shifted, often corresponding with technological milestones.

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AWS Machine Learning Blog

NOVEMBER 15, 2024

The rapid advancement of generative AI promises transformative innovation, yet it also presents significant challenges. Concerns about legal implications, accuracy of AI-generated outputs, data privacy, and broader societal impacts have underscored the importance of responsible AI development.

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Flipboard

JUNE 20, 2023

Understanding AI’s mysterious “opaque box” is paramount to creating explainable AI. This can be simplified by considering that AI, like all other technology, has a supply chain. These are the mathematical formulas written to simulate functions of the brain, which underlie the AI programming.

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Marktechpost

DECEMBER 11, 2024

Image Source : LG AI Research Blog ([link] Responsible AI Development: Ethical and Transparent Practices The development of EXAONE 3.5 models adhered to LG AI Research s Responsible AI Development Framework, prioritizing data governance, ethical considerations, and risk management. model scored 70.2.

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ODSC - Open Data Science

FEBRUARY 25, 2025

Large Language Models & RAG TrackMaster LLMs & Retrieval-Augmented Generation Large language models (LLMs) and retrieval-augmented generation (RAG) have become foundational to AI development. AI Engineering TrackBuild Scalable AISystems Learn how to bridge the gap between AI development and software engineering.

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ODSC - Open Data Science

OCTOBER 21, 2024

As AI systems become increasingly embedded in critical decision-making processes and in domains that are governed by a web of complex regulatory requirements, the need for responsible AI practices has never been more urgent. But let’s first take a look at some of the tools for ML evaluation that are popular for responsible AI.

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ODSC - Open Data Science

FEBRUARY 18, 2025

AI Ethicists: As AI systems become more integrated into society, ethical considerations are paramount. AI ethicists specialize in ensuring that AI development and deployment align with ethical guidelines and regulatory standards, preventing unintended harm andbias.

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ODSC - Open Data Science

SEPTEMBER 26, 2024

Using AI to Detect Anomalies in Robotics at the Edge Integrating AI-driven anomaly detection for edge robotics can transform countless industries by enhancing operational efficiency and improving safety. Where do explainable AI models come into play? Here’s everything that you can watch on-demand whenever you like!

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Towards AI

OCTOBER 7, 2023

Last Updated on October 9, 2023 by Editorial Team Author(s): Lye Jia Jun Originally published on Towards AI. Balancing Ethics and Innovation: An Introduction to the Guiding Principles of Responsible AI Sarah, a seasoned AI developer, found herself at a moral crossroads. The other safeguards personal data but lacks speed.

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Pickl AI

APRIL 8, 2024

There are several steps that can be taken to mitigate algorithmic bias, such as using diverse datasets for training, employing fairness metrics during development, and implementing human oversight in decision-making processes. How Can We Ensure the Transparency of AI Systems?

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Pickl AI

JANUARY 29, 2024

Emerging Trends Emerging trends in Data Science include integrating AI technologies and the rise of Explainable AI for transparent decision-making. AI trends involve increased focus on ethical AI, AI-powered automation, and the development of more sophisticated Natural Language Processing.

Data Science 52

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Pickl AI

JANUARY 13, 2025

It simplifies complex AI topics like clustering , dimensionality , and regression , providing practical examples and numeric calculations to enhance understanding. Key Features: Explains AI algorithms like clustering and regression. Key Features: Focuses on ethical AI development. Minimal technical jargon.

Artificial Intelligence 52

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AI News

APRIL 25, 2024

Pryon also emphasises explainable AI and verifiable attribution of knowledge sources. Ensuring responsible AI development Jablokov strongly advocates for new regulatory frameworks to ensure responsible AI development and deployment.

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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. Make AI Decisions Transparent and Accountable Transparency is everything when it comes to trust.

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Unite.AI

JULY 1, 2024

Transparency and Explainability Enhancing transparency and explainability is essential. Techniques such as model interpretability frameworks and Explainable AI (XAI) help auditors understand decision-making processes and identify potential issues. This involves human experts reviewing and validating AI outputs.

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IBM Journey to AI blog

SEPTEMBER 3, 2024

These systems inadvertently learn biases that might be present in the training data and exhibited in the machine learning (ML) algorithms and deep learning models that underpin AI development. Those learned biases might be perpetuated during the deployment of AI, resulting in skewed outcomes.

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Viso.ai

JANUARY 21, 2025

Moreover, their ability to handle large datasets with fewer resources makes them a game-changer in AI development. ViTs vs. CNN (A Quick Comparison) Multimodal AI Integration Multimodal AI can process and integrate multiple types of data simultaneously such as text, images, video, and audio. Theyre becoming essential.

Computer Vision 52

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The MLOps Blog

FEBRUARY 27, 2025

How to integrate transparency, accountability, and explainability? As LLMs become increasingly integrated into applications and individuals and organizations rely on AI development for their own projects, concerns surrounding the transparency, accountability, and explainability of these systems are growing.

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