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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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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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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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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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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IBM Journey to AI blog

MAY 9, 2023

Challenges around managing risk and reputation Customers, employees and shareholders expect organizations to use AI responsibly, and government entities are starting to demand it. 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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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

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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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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IBM Journey to AI blog

DECEMBER 4, 2023

In addition, the CPO AI Ethics Project Office supports all of these initiatives, serving as a liaison between governance roles, supporting implementation of technology ethics priorities, helping establish AI Ethics Board agendas and ensuring the board is kept up to date on industry trends and company strategy.

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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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TransOrg Analytics

FEBRUARY 11, 2025

These are just a few ways Artificial Intelligence (AI) silently influences our daily lives. As AI continues integrating into every aspect of society, the need for Explainable AI (XAI) becomes increasingly important. What is Explainable AI? Why is Explainable AI Important?

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IBM Journey to AI blog

FEBRUARY 28, 2024

Governments must comprehend and manage the full AI lifecycle effectively, and leaders should be able to easily explain what data was used to train and fine-tune models, as well as how the models reached their outcomes.

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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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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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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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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.

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IBM Journey to AI blog

MARCH 4, 2024

The scale and impact of next-generation AI emphasize the importance of governance and risk controls. An AI+ enterprise mitigates potential harm by implementing robust measures to secure, monitor and explain AI models, as well as monitoring governance, risk and compliance controls across the hybrid cloud environment.

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How to Learn Machine Learning

APRIL 17, 2025

From advanced generative AI to responsible AI governance, the landscape is evolving rapidly, demanding a fresh perspective on skills, tools, and applications. Career Edge: This growing niche opens up new opportunities for professionals trained in cybersecurity-focused AI and machine learning courses.

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NYU Center for Data Science

JUNE 16, 2023

In addition to working on his advanced degree, Umang is a Research Associate on the Safe and Ethical AI Team at the Alan Turing Institute. He is an Advisor at the Responsible AI Institute and has served in mentoring roles as a Thesis Co-Supervisor and Teaching Assistant at the University of Cambridge. By Meryl Phair

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Unite.AI

OCTOBER 25, 2024

In the Engineer phase, EverythingAI TM transforms concepts into scalable solutions, integrating AI into core operations through AI and Data Architecture, GenAI Development, and AI/ML Modeling. Explainability & Transparency: The company develops localized and explainable AI systems.

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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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ODSC - Open Data Science

MARCH 20, 2025

Sessions: Keynotes: Eric Xing, PhD, Professor at CMU and President of MBZUAI: Toward Public and Reproducible Foundation Models Beyond Lingual Intelligence Book Signings: Sinan Ozdemir: Quick Start Guide to Large LanguageModels Matt Harrison: Effective Pandas: Patterns for Data Manipulation Workshops: Adaptive RAG Systems with Knowledge Graphs: Building (..)

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NVIDIA

MARCH 1, 2024

Transparency in AI is a set of best practices, tools and design principles that helps users and other stakeholders understand how an AI model was trained and how it works. Explainable AI , or XAI, is a subset of transparency covering tools that inform stakeholders how an AI model makes certain predictions and decisions.

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Pickl AI

OCTOBER 8, 2024

This blog will explore the concept of XAI, its importance in fostering trust in AI systems, its benefits, challenges, techniques, and real-world applications. What is Explainable AI (XAI)? Explainable AI refers to methods and techniques that enable human users to comprehend and interpret the decisions made by AI systems.

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ODSC - Open Data Science

FEBRUARY 27, 2024

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.

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Pickl AI

FEBRUARY 17, 2025

Approximately 44% of organisations express concerns about transparency in AI adoption. The “black box” nature of many algorithms makes it difficult for stakeholders to understand how decisions are made, leading to reduced trust in AI systems.

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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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ML @ CMU

APRIL 23, 2025

Interpretability and Explainable AI Learning on Graphs and Other Geometries & Topologies Learning Theory Neurosymbolic & Hybrid AI Systems (Physics-Informed, Logic & Formal Reasoning, etc.) Optimization Other Topics in Machine Learning (i.e.,

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Unite.AI

JULY 5, 2024

Accountability and Transparency: Accountability in Gen AI-driven decisions involve multiple stakeholders, including developers, healthcare providers, and end users. Transparent, explainable AI models are necessary for informed decision-making.

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ODSC - Open Data Science

JUNE 8, 2023

Keynotes Both in-person and virtually, we had some amazing keynote speakers that told the audience about their research, expertise, use cases, or state-of-the-art developments.

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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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ODSC - Open Data Science

FEBRUARY 18, 2025

Prompt Engineers: Also known as AI Interaction Specialists, these experts craft and refine the prompts used to interact with and guide AI models, ensuring they generate high-quality, contextually relevant content and responses. Explainable AI (XAI) techniques are crucial for building trust and ensuring accountability.

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ODSC - Open Data Science

FEBRUARY 25, 2025

Topics Include: Advanced ML Algorithms & EnsembleMethods Hyperparameter Tuning & Model Optimization AutoML & Real-Time MLSystems Explainable AI & EthicalAI Time Series Forecasting & NLP Techniques Who Should Attend: ML Engineers, Data Scientists, and Technical Practitioners working on production-level ML solutions.

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Pickl AI

SEPTEMBER 17, 2024

AI in Security Automation and Incident Response AI is revolutionising security automation and incident response by enabling faster, more efficient, and more accurate responses to cyber threats.

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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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The MLOps Blog

JUNE 27, 2023

Recommended for you A Comprehensive Guide on How to Monitor Your Models in Production Responsible AI You can use responsible AI tools to deploy ML models through ethical, fair, and accountable techniques.

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