Unite.AI

MARCH 20, 2025

Even AI-powered customer service tools can show bias, offering different levels of assistance based on a customers name or speech pattern. Lack of Transparency and Explainability Many AI models operate as “black boxes,” making their decision-making processes unclear.

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Algorithm AI AI Explainability 162

AI News

JANUARY 31, 2025

EU AI Act has no borders The extraterritorial scope of the EU AI Act means non-EU organisations are assuredly not off the hook. As Marcus Evans, a partner at Norton Rose Fulbright , explains, the Act applies far beyond the EU’s borders. The AI Act will have a truly global application, says Evans.

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Data Quality Big Data AI AI 287

IBM Journey to AI blog

FEBRUARY 8, 2023

Gartner predicts that the market for artificial intelligence (AI) software will reach almost $134.8 Achieving Responsible AI As building and scaling AI models for your organization becomes more business critical, achieving Responsible AI (RAI) should be considered a highly relevant topic. billion by 2025.

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Responsible AI Explainability Explainable AI Automation 130

Towards AI

FEBRUARY 11, 2025

NLP process: Identify keywords: weather, today Understand intent: weather forecast request Generate a response AI response: Expect partly sunny skies with a light breeze today. Finally, respond how a person would. Here is an example: You: Whats the weather today?

Conversational AI 74

Conversational AI Explainability NLP Natural Language Processing 74

Unite.AI

MARCH 6, 2025

We develop AI governance frameworks that focus on fairness, accountability, and transparency in decision-making. Our approach includes using diverse training data to help mitigate bias and ensure AI models align with societal expectations. Human oversight in high-risk situations ensures the AI systems dont make critical errors.

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Automation AI Modeling Natural Language Processing Explainability 165

AWS Machine Learning Blog

NOVEMBER 29, 2023

These challenges include some that were common before generative AI, such as bias and explainability, and new ones unique to foundation models (FMs), including hallucination and toxicity. Guardrails drive consistency in how FMs on Amazon Bedrock respond to undesirable and harmful content within applications.

Responsible AI 131

Responsible AI Generative AI AI AI 131

Unite.AI

JANUARY 29, 2025

An AI-native data abstraction layer acts as a controlled gateway, ensuring your LLMs only access relevant information and follow proper security protocols. Explainability and Trust AI outputs can often feel like black boxesuseful, but hard to trust. AI governance manages three things.

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LLM Explainability ChatGPT AI 165

AI News

JULY 16, 2024

“Sizeable productivity growth has eluded UK workplaces for over 15 years – but responsible AI has the potential to shift the paradigm,” explained Daniel Pell, VP and country manager for UK&I at Workday. ” Despite the optimistic outlook, the path to AI adoption is not without obstacles.

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Big Data Responsible AI AI AI 338

Unite.AI

FEBRUARY 21, 2025

In industries like banking, where precision is paramount, AI must be deployed within a framework that ensures human oversight remains at the core of decision-making processes. To maintain accountability, AI solutions must be transparent.

Generative AI 182

Generative AI AI AI Automation 182

Unite.AI

NOVEMBER 4, 2024

Ex-Human was born from the desire to push the boundaries of AI even further, making it more adaptive, engaging, and capable of transforming how people interact with digital characters across various industries. Ex-human uses AI avatars to engage millions of users. What’s the key to achieving such high levels of user interaction?

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Conversational AI Explainability Chatbots Responsible AI 246

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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Composite AI Explainability Black Box AI Neural Network 182

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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Explainability Explainable AI Natural Language Processing Prompt Engineer 105

AI News

OCTOBER 22, 2024

The legislation establishes a first-of-its-kind regulatory framework for AI systems, employing a risk-based approach that categorises AI applications based on their potential impact on safety, human rights, and societal wellbeing.

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Big Data AI AI Explainability 316

Unite.AI

JANUARY 2, 2025

Transparency allows AI decisions to be explained, understood, and verified. Developers can identify and correct biases when AI systems are explainable, creating fairer outcomes. A 2024 report commissioned by Workday highlights the critical role of transparency in building trust in AI systems.

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AI Development AI Developer Explainability AI 130

Google Research AI blog

MARCH 16, 2023

The Impact Lab team, part of Google’s Responsible AI Team , employs a range of interdisciplinary methodologies to ensure critical and rich analysis of the potential implications of technology development. We examine systemic social issues and generate useful artifacts for responsible AI development.

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Unite.AI

AUGUST 25, 2023

One of the most significant issues highlighted is how the definition of responsible AI is always shifting, as societal values often do not remain consistent over time. Can focusing on Explainable AI (XAI) ever address this? For someone who is being falsely accused, explainability has a whole different meaning and urgency.

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Explainability Explainable AI AI AI 246

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. The OECD reports over 700 regulatory initiatives in development across more than 60 countries.

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Automation AI AI Artificial Intelligence 147

IBM Journey to AI blog

MAY 9, 2023

Success in delivering scalable enterprise AI necessitates the use of tools and processes that are specifically made for building, deploying, monitoring and retraining AI models. Responsible AI use is critical, especially as more and more organizations share concerns about potential damage to their brand when implementing AI.

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Metadata Explainability Automation Responsible AI 227

Flipboard

FEBRUARY 19, 2025

Additionally, we discuss some of the responsible AI framework that customers should consider adopting as trust and responsible AI implementation remain crucial for successful AI adoption. But first, we explain technical architecture that makes Alfred such a powerful tool for Andurils workforce.

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Responsible AI Natural Language Processing Explainability Large Language Models 81

IBM Journey to AI blog

OCTOBER 12, 2023

But the implementation of AI is only one piece of the puzzle. The tasks behind efficient, responsible AI lifecycle management The continuous application of AI and the ability to benefit from its ongoing use require the persistent management of a dynamic and intricate AI lifecycle—and doing so efficiently and responsibly.

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Data Scientist Metadata Explainability Responsible AI 234

IBM Journey to AI blog

SEPTEMBER 20, 2023

AI’s value is not limited to advances in industry and consumer products alone. When implemented in a responsible way—where the technology is fully governed, privacy is protected and decision making is transparent and explainable—AI has the power to usher in a new era of government services.

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Generative AI AI AI Explainability 307

Unite.AI

DECEMBER 13, 2023

AI transforms cybersecurity by boosting defense and offense. However, challenges include the rise of AI-driven attacks and privacy issues. Responsible AI use is crucial. The future involves human-AI collaboration to tackle evolving trends and threats in 2024.

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Explainability AI AI Responsible AI 290

NVIDIA

OCTOBER 18, 2023

Responsible AI is hot on its heels. Julia Stoyanovich, associate professor of computer science and engineering at NYU and director of the university’s Center for Responsible AI , wants to make the terms “AI” and “responsible AI” synonymous. Artificial intelligence is now a household term.

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AI News

MARCH 7, 2024

Stability AI, in previewing Stable Diffusion 3, noted that the company believed in safe, responsible AI practices. OpenAI is adopting a similar approach with Sora ; in January, the company announced an initiative to promote responsible AI usage among families and educators.

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Prompt Engineer Prompt Engineering OpenAI Large Language Models 334

Chatbots Life

MAY 23, 2023

Responsible AI — deployment framework I asked ChatGPT and Bard to share their thoughts on what policies governments have to put in place to ensure responsible AI implementations in their countries. They should also work to raise awareness of the importance of responsible AI among businesses and organizations.

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Responsible AI Chatbots AI AI 82

AI News

FEBRUARY 23, 2024

Stability AI said it is also working with experts to test Stable Diffusion 3 and ensure it mitigates potential harms, similar to OpenAI’s approach with Sora. “We We believe in safe, responsible AI practices. This means we have taken and continue to take reasonable steps to prevent the misuse of Stable Diffusion 3 by bad actors.

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Big Data OpenAI AI AI 324

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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Explainable AI AI AI Natural Language Processing 140

Unite.AI

FEBRUARY 17, 2025

Model Interpretation and Explainability: Many AI models, especially deep learning models, are often seen as black boxes. Good enterprise AI products proved full transparency, including what sources the models accessed and when, and why each recommendation was made. per year to 300k per year.

Automation 147

Automation Responsible AI Generative AI AI Modeling 147

Marktechpost

NOVEMBER 22, 2024

Arize helps ensure that AI models are reliable, accurate, and unbiased, promoting ethical and responsible AI development. It’s a valuable tool for building and deploying AI models that are fair and equitable. It offers a range of features, including agent creation, training, deployment, and monitoring.

Automation 109

Automation Explainability Data Analysis Machine Learning 109

AI News

JULY 16, 2024

“Sizeable productivity growth has eluded UK workplaces for over 15 years – but responsible AI has the potential to shift the paradigm,” explained Daniel Pell, VP and country manager for UK&I at Workday. ” Despite the optimistic outlook, the path to AI adoption is not without obstacles.

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Big Data Responsible AI AI AI 247

AI News

APRIL 24, 2024

. “Because it’s reading from textbook-like material…you make the task of the language model to read and understand this material much easier,” Bubeck explained.

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Big Data Large Language Models Generative AI AI Modeling 330

IBM Journey to AI blog

MAY 2, 2023

Therefore, for both the systems in use today and the systems coming online tomorrow, training must be part of a responsible approach to building AI. We don’t need a pause to prioritize responsible AI. The stakes are simply too high, and our society deserves nothing less.

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AI Development AI Developer AI AI 296

Unite.AI

DECEMBER 1, 2023

SHAP's strength lies in its consistency and ability to provide a global perspective – it not only explains individual predictions but also gives insights into the model as a whole. This method requires fewer resources at test time and has been shown to effectively explain model predictions, even in LLMs with billions of parameters.

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LLM Machine Learning Explainability Algorithm 264

Marktechpost

JANUARY 17, 2024

The NIST AI Risk Management Framework and AI Trustworthiness taxonomy have indicated that these operational characteristics are necessary for trustworthy AI. The goal is to provide a thorough resource that helps shape future practice guides and standards for evaluating and controlling the security of AI systems.

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Machine Learning Responsible AI Artificial Intelligence Artificial Intelligence 103

Unite.AI

MARCH 18, 2025

By observing ethical data collection, we succeed business-wise while contributing to the establishment of a transparent and responsible AI ecosystem. Another notable trend is the reliance on synthetic data used for data augmentation, wherein AI generates data that supplements datasets gathered from real-world scenarios.

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AI Modeling Automation Explainable AI AI Chatbots 147

IBM Journey to AI blog

JANUARY 15, 2024

To develop responsible AI, government leaders must carefully prepare their internal data to harness the full potential of both AI and generative AI. Setting responsible standards is a crucial government role, requiring the integration of responsibility from the start, rather than as an afterthought.

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Generative AI Responsible AI Neural Network Natural Language Processing 200

Unite.AI

MAY 27, 2024

This joint effort is essential to establish industry-wide standards, address ethical concerns, and ensure responsible AI deployment. One of the key challenges in AI is explainability. This is particularly important when AI is used for critical decisions, such as granting or rejecting loans.

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Machine Learning Explainability Algorithm AI 223