NYU Center for Data Science

JUNE 16, 2023

A PhD candidate in the Machine Learning Group at the University of Cambridge advised by Adrian Weller , Umang will continue to pursue research in trustworthy machine learning, responsible artificial intelligence, and human-machine collaboration at NYU. His work has been covered in press (e.g.,

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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?

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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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ODSC - Open Data Science

MARCH 20, 2025

Day 1: Tuesday, May13th The first official day of ODSC East 2025 will be chock-full of hands-on training sessions and workshops from some of the leading experts in LLMs, Generative AI, Machine Learning, NLP, MLOps, and more.

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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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The MLOps Blog

JUNE 27, 2023

How to evaluate MLOps tools and platforms Like every software solution, evaluating MLOps (Machine Learning Operations) tools and platforms can be a complex task as it requires consideration of varying factors. Pay-as-you-go pricing makes it easy to scale when needed.

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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.

Data Science 52

Data Science Explainable AI Responsible AI Machine Learning 52

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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Pickl AI

SEPTEMBER 17, 2024

Summary : AI is transforming the cybersecurity landscape by enabling advanced threat detection, automating security processes, and adapting to new threats. It leverages Machine Learning, natural language processing, and predictive analytics to identify malicious activities, streamline incident response, and optimise security measures.

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ODSC - Open Data Science

FEBRUARY 25, 2025

At ODSC East 2025 , were excited to present 12 curated tracks designed to equip data professionals, machine learning engineers, and AI practitioners with the tools they need to thrive in this dynamic landscape. Machine Learning TrackDeepen Your ML Expertise Machine learning remains the backbone of AI innovation.

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ODSC - Open Data Science

JUNE 8, 2023

We had bigger sessions on getting started with machine learning or SQL, up to advanced topics in NLP, and how to make deepfakes. You can find our photo album here and more pictures will be added as we develop them. Top Sessions With sessions both online and in-person in Boston, there was something for everyone.

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Pickl AI

JULY 14, 2023

However, symbolic AI faced limitations in handling uncertainty and dealing with large-scale data. Machine Learning and Neural Networks (1990s-2000s): Machine Learning (ML) became a focal point, enabling systems to learn from data and improve performance without explicit 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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Pickl AI

AUGUST 1, 2024

This blog covers their job roles, essential tools and frameworks, diverse applications, challenges faced in the field, and future directions, highlighting their critical contributions to the advancement of Artificial Intelligence and machine learning.

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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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Snorkel AI

MARCH 19, 2024

Developing AI applications has traditionally been a linear, “model-centric” process in which machine learning models could be optimized by tuning specific parameters. Enterprises that build and use LLMs for complex, business-critical AI applications must continuously monitor, evaluate, and update their models using their data.

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Snorkel AI

MARCH 19, 2024

Developing AI applications has traditionally been a linear, “model-centric” process in which machine learning models could be optimized by tuning specific parameters. Enterprises that build and use LLMs for complex, business-critical AI applications must continuously monitor, evaluate, and update their models using their data.

Data Scientist 52

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Pickl AI

DECEMBER 29, 2024

Summary: Responsible AI ensures AI systems operate ethically, transparently, and accountably, addressing bias and societal risks. Through ethical guidelines, robust governance, and interdisciplinary collaboration, organisations can harness AI’s transformative power while safeguarding fairness and inclusivity.

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Unite.AI

NOVEMBER 29, 2023

AI watchdogs employ state-of-the-art technologies, particularly machine learning and deep learning algorithms, to combat the ever-increasing amount of election-related false information. The Bottom Line In conclusion, AI watchdogs are indispensable in safeguarding elections and adapting to evolving disinformation tactics.

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IBM Journey to AI blog

OCTOBER 30, 2024

Through the integrated suite of tools offered by watsonx.governance™, users can expedite the implementation of responsible, transparent and explainable AI workflows tailored to both generative AI and machine learning models.

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Pickl AI

DECEMBER 10, 2024

As the global AI market, valued at $196.63 from 2024 to 2030, implementing trustworthy AI is imperative. This blog explores how AI TRiSM ensures responsible AI adoption. Key Takeaways AI TRiSM embeds fairness, transparency, and accountability in AI systems, ensuring ethical decision-making.

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