ODSC - Open Data Science

MARCH 11, 2024

Introducing the Topic Tracks for ODSC East 2024 — Highlighting Gen AI, LLMs, and Responsible AI ODSC East 2024 , coming up this April 23rd to 25th, is fast approaching and this year we will have even more tracks comprising hands-on training sessions, expert-led workshops, and talks from data science innovators and practitioners.

Responsible AI 52

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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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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 Automation Responsible AI Explainability 205

Google Research AI blog

JUNE 21, 2023

USM improvements over pre-USM models can be explained by USM’s relative size increase, 120M to 2B parameters, and other improvements discussed in the USM blog post. Model word error rates (WER) for each test set (lower is better).

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ODSC - Open Data Science

AUGUST 24, 2023

Evolving Trends in Prompt Engineering for Large Language Models (LLMs) with Built-in Responsible AI Practices Editor’s note: Jayachandran Ramachandran and Rohit Sroch are speakers for ODSC APAC this August 22–23. As LLMs become integral to AI applications, ethical considerations take center stage.

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Large Language Models Prompt Engineer Prompt Engineering Responsible AI 52

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.

AI 290

AI AI Responsible AI Explainability 290

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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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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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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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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IBM Journey to AI blog

FEBRUARY 22, 2024

The CDAO includes five ethical principles of responsible, equitable, traceable, reliable, and governable as part of its responsible AI toolkit. Based on the US military’s existing ethics framework, these principles are grounded in the military’s values and help uphold its commitment to responsible AI.

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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 AI Modeling Generative AI 246

IBM Journey to AI blog

JULY 26, 2023

Critical considerations for responsible AI adoption While the possibilities are endless, the explosion of use cases that employ generative AI in HR also poses questions around misuse and the potential for bias. As such, HR leaders cannot simply rely on data and AI to make decisions. HR leaders set the tone.

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Auto-complete Generative AI AI Strategy Responsible AI 181

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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Unite.AI

JULY 3, 2023

While it’s incredibly important that the White House took this first step in defining the guiding principles for responsible AI, we don’t believe it went far enough to provide guidance for organizations and not just individuals worried about appealing an AI-based decision.

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Responsible AI AI AI Explainability 162

Flipboard

AUGUST 6, 2023

Jupyter AI, an official subproject of Project Jupyter, brings generative artificial intelligence to Jupyter notebooks. It allows users to explain and generate code, fix errors, summarize content, and even generate entire notebooks from natural language prompts. Check out the GitHub and Reference Article.

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Artificial Intelligence Artificial Intelligence Metadata Large Language Models 113

IBM Journey to AI blog

OCTOBER 16, 2023

Foundation models are widely used for ML tasks like classification and entity extraction, as well as generative AI tasks such as translation, summarization and creating realistic content. The development and use of these models explain the enormous amount of recent AI breakthroughs. Increase trust in AI outcomes.

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Metadata Explainability Automation AI 203

John Snow Labs

APRIL 23, 2024

mean response) was of higher importance than reproducibility (3.91), legal and reputation risk (3.89), explainability and transparency (3.83), and cost (3.8). Finally, the survey explores the large amount of remaining work in applying responsible AI principles in healthcare GenAI projects.

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Generative AI Large Language Models Responsible AI Explainability 52

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 Developer AI Development AI AI 275

NYU Center for Data Science

DECEMBER 12, 2023

The Center for Responsible AI (NYU R/AI) is leading this charge by embedding ethical considerations into the fabric of artificial intelligence research and development. The Center for Responsible AI is a testament to NYU’s commitment to pioneering research that upholds and advances these ideals.

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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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Explainable AI Responsible AI Data Science Explainability 93

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.

Explainable AI 52

Explainable AI Responsible AI Data Science Explainability 52

ODSC - Open Data Science

OCTOBER 26, 2023

With the rapid advance of AI across industries, responsible AI has become a hot topic for decision-makers and data scientists alike. But with the advent of easy-to-access generative AI, it’s now more important than ever. There are several reasons why responsible AI is critical as the technology continues to advance.

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Generative AI Responsible AI AI AI 98

Unite.AI

MARCH 27, 2024

Moreover, emphasizing human-AI collaboration highlights AI's enhancement of human capabilities rather than replacement, resulting in improved outcomes. Organizations must also give precedence to responsible AI practices, ensuring transparency, explainability, and accountability in AI systems.

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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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AI AI Metadata Explainable AI 216

NYU Center for Data Science

JUNE 16, 2023

Motivated by applications in healthcare and criminal justice, Umang studies how to create algorithmic decision-making systems endowed with the ability to explain their behavior and adapt to a stakeholder’s expertise to improve human-machine team performance. His work has been covered in press (e.g., UK Parliament POSTnote , NIST ).

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Machine Learning Explainability Artificial Intelligence Artificial Intelligence 107

ODSC - Open Data Science

FEBRUARY 12, 2024

It’s often hard to extract value from predictive models because they lack explainability and can be challenging to implement effectively. Augmented analytics — which automates more of the analytics journey through AI — can address conventional obstacles to make it easier to turn data into relevant, accurate and actionable insights.

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Automation Data Science Explainability Business Intelligence 98

ODSC - Open Data Science

NOVEMBER 21, 2023

Though there are great potential benefits, it is important to ensure this technology is developed and used responsibly. In her keynote speech at ODSC West, Sarah Bird, Global Lead for Responsible AI Engineering at Microsoft, discussed Microsoft’s journey in building and using generative AI responsibly.

Generative AI 52

Generative AI Responsible AI Data Science AI 52

Unite.AI

APRIL 30, 2024

Ethical Considerations and Challenges Ethical considerations and challenges are significant in the development of self-reflective AI systems. Transparency and accountability are at the forefront, necessitating explainable systems that can justify their decisions.

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Chatbots Neural Network BERT OpenAI 204

Flipboard

NOVEMBER 28, 2023

An important next step of the AI system risk assessment is to identify potentially harmful events associated with the use case. In considering these events, it can be helpful to reflect on different dimensions of responsible AI, such as fairness and robustness, for example.

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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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Aiiot Talk

NOVEMBER 30, 2023

“Across all industries, ethical AI has quickly become the focus of attention.” The Role of Ethical AI in Business Ethical AI is unbiased, fair and transparent. Its output is easily explainable and traceable, meaning you can hold it accountable and verify its conclusions.

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Algorithm Artificial Intelligence Artificial Intelligence AI 233

Becoming Human

APRIL 19, 2024

Transparency and Explainability Transparency in AI systems is crucial for building trust among users and stakeholders. This lack of explainability raises concerns about accountability and the potential for unintended consequences. AI consultants must prioritize transparency by adopting models and techniques for interpretability.

AI 102

AI AI Responsible AI Artificial Intelligence 102

NYU Center for Data Science

JANUARY 3, 2024

We wanted to develop a framework for understanding the toxicity of language that would take into account more than just what’s shown explicitly in the text,” Gabriel explains. This focus on the implicit meanings and the social context of language in AI models is crucial in an era where digital communication is omnipresent.

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ODSC - Open Data Science

JUNE 27, 2023

Who Are AI Builders, AI Users, and Other Key Players? AI Builders AI builders are the data scientists, data engineers, and developers who design AI models. The goals and priorities of responsible AI builders are to design trustworthy, explainable, and human-centered AI.

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Data Scientist AI AI Responsible AI 98

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.

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Marktechpost

NOVEMBER 23, 2023

This technical prowess positions Leonardo as a crucial asset for advancing AI applications across Europe​​. REAL AI’s unique opportunity- “We aim to provide Europe’s answer to responsible AI development. Together with UNINA and AI supercomputer cluster ‘Leonardo’, we can make this possible”.

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