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.

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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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Explainability Explainable AI Natural Language Processing Prompt Engineer 105

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

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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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.

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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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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.

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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

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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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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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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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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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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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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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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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

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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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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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.

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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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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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Pickl AI

DECEMBER 10, 2024

Businesses face fines and reputational damage when AI decisions are deemed unethical or discriminatory. Socially, biased AI systems amplify inequalities, while data breaches erode trust in technology and institutions. Broader Ethical Implications Ethical AI development transcends individual failures.

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ODSC - Open Data Science

OCTOBER 3, 2024

AI Development Lifecycle: Learnings of What Changed with LLMs Noé Achache | Engineering Manager & Generative AI Lead | Sicara Using LLMs to build models and pipelines has made it incredibly easy to build proof of concepts, but much more challenging to evaluate the models.

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IBM Journey to AI blog

FEBRUARY 9, 2024

They make AI more explainable: the larger the model, the more difficult it is to pinpoint how and where it makes important decisions. Explainable AI is essential to understanding, improving and trusting the output of AI systems.

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IBM Journey to AI blog

FEBRUARY 28, 2024

IBM watsonx™ , an integrated AI, data and governance platform, embodies five fundamental pillars to help ensure trustworthy AI: fairness, privacy, explainability, transparency and robustness. This platform offers a seamless, efficient and responsible approach to AI development across various environments.

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AI Weekly

AUGUST 22, 2024

technologyreview.com Build your own AI-powered robot Hugging Face, the open-source AI powerhouse, has taken a significant step towards democratizing low-cost robotics with the release of a detailed tutorial that guides developers through the process of building and training their own AI-powered robots. pdf, Word, etc.)

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