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.

Responsible AI 52

Responsible AI Black Box AI Artificial Intelligence Artificial Intelligence 52

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.

Algorithm 162

Algorithm AI AI Explainability 162

Unite.AI

MARCH 14, 2025

What inspired you to found AI Squared, and what problem in AI adoption were you aiming to solve? With my background at the NSA, where I saw firsthand that nearly 90% of AI models never made it to production, I founded AI Squared to address the critical gap between AI development and real-world deployment.

ETL 162

ETL Responsible AI AI AI 162

AI News

JULY 31, 2024

As the EU’s AI Act prepares to come into force tomorrow, industry experts are weighing in on its potential impact, highlighting its role in building trust and encouraging responsible AI adoption. “The greatest problem facing AI developers is not regulation, but a lack of trust in AI,” Wilson stated.

Big Data 343

Big Data Explainability Responsible AI AI 343

Unite.AI

JANUARY 2, 2025

The Importance of Transparency in AI Transparency is essential for ethical AI development. Without it, users must rely on AI systems without understanding how decisions are made. Transparency allows AI decisions to be explained, understood, and verified. This is particularly important in areas like hiring.

AI Development 130

AI Development AI Developer Explainability AI 130

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.

Big Data 327

Big Data AI AI Explainability 327

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.

Conversational AI 246

Conversational AI Explainability Chatbots Responsible 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.

Automation 147

Automation AI AI Artificial Intelligence 147

Unite.AI

FEBRUARY 5, 2025

As a result, server systems built for demanding AI workloads are becoming cost prohibitive or out of reach for many with capped departmental operating expenses (OpEx) budgets. In 2025, enterprise customers must level-set their AI costs and re-sync levels of AI development budget.

AI Tools 138

AI Tools AI AI Data Scientist 138

Unite.AI

JANUARY 20, 2025

Transparency = Good Business AI systems operate using vast datasets, intricate models, and algorithms that often lack visibility into their inner workings. This opacity can lead to outcomes that are difficult to explain, defend, or challengeraising concerns around bias, fairness, and accountability.

Explainability 113

Explainability Explainable AI AI AI 113

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

Explainable AI 140

Explainable AI AI AI Natural Language Processing 140

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].

Explainability 105

Explainability Explainable AI Natural Language Processing Prompt Engineer 105

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.

Composite AI 182

Composite AI Explainability Neural Network Explainable AI 182

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.

Responsible AI 111

Responsible AI ML AI AI 111

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.

Responsible AI 78

Responsible AI Natural Language Processing Explainability Large Language Models 78

Marktechpost

NOVEMBER 22, 2024

It helps developers identify and fix model biases, improve model accuracy, and ensure fairness. 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.

Automation 109

Automation Explainability Data Analysis Machine Learning 109

AI News

FEBRUARY 10, 2025

Cloke also stressed the role of organisations in ensuring AI adoption goes beyond regulatory frameworks. Modernising core systems enables organisations to better harness AI while ensuring regulatory compliance, he explained. Neff also pushed back against big techs dominance in AI development.

Big Data 311

Big Data AI AI Natural Language Processing 311

Unite.AI

JULY 26, 2024

Application Used for customized AI Tasks Ideal for complex tasks and high-quality content creation Ethical Considerations Transparency in AI development is important for building trust and accountability. To address these ethical concerns, developers and organizations should prioritize AI explainability techniques.

ChatGPT 281

ChatGPT OpenAI Natural Language Processing NLP 281

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.

Responsible AI 82

Responsible AI Chatbots AI AI 82

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.

Generative AI 293

Generative AI Explainability AI AI 293

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.

Generative AI 200

Generative AI Responsible AI Neural Network Natural Language Processing 200

Unite.AI

MARCH 18, 2025

Certain large companies have control over a vast amount of data, which creates an uneven playing field wherein only a select few have access to information necessary to train AI models and drive innovation. Public web data should remain accessible to businesses, researchers, and developers. This is not how things should be.

AI Modeling 147

AI Modeling Automation Explainable AI AI Chatbots 147

Unite.AI

MAY 22, 2024

Imagine a virtual tutor that can not only explain complex concepts through natural language but also generate visual aids and interactive simulations on the fly. Ethical Considerations and Responsible AI As with any powerful technology, the development and deployment of GPT-4o and similar AI models raise important ethical considerations.

AI Modeling 260

AI Modeling OpenAI Responsible AI Artificial Intelligence 260

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.

LLM 264

LLM Machine Learning Explainability Algorithm 264

Unite.AI

JULY 1, 2024

It doesn’t matter if you are an online consumer or a business using that information to make key decisions – responsible AI systems allow all of us to fully and better understand information, as you need to ensure what is coming out of Generative AI is accurate and reliable.

Explainability 173

Explainability Software Engineer AI Modeling Responsible AI 173

AWS Machine Learning Blog

MARCH 14, 2025

This post focuses on RAG evaluation with Amazon Bedrock Knowledge Bases, provides a guide to set up the feature, discusses nuances to consider as you evaluate your prompts and responses, and finally discusses best practices. Jesse Manders is a Senior Product Manager on Amazon Bedrock, the AWS Generative AI developer service.

Generative AI 96

Generative AI Responsible AI Automation LLM 96

Unite.AI

JANUARY 28, 2025

Can you explain how Cognigy's AI Copilot has changed the landscape for human agents in contact centers? Cognigy's AI Copilot has fundamentally transformed the role of human agents in contact centers by acting as a real-time assistant that empowers agents to deliver faster, more accurate, and empathetic customer interactions.

Conversational AI 147

Conversational AI Generative AI Automation Large Language Models 147

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.

Metadata 220

Metadata Explainability Automation Explainable AI 220

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.

Responsible AI 52

Responsible AI ML Data Scientist Machine Learning 52

Marktechpost

MAY 30, 2024

Introduction to AI and Machine Learning on Google Cloud This course introduces Google Cloud’s AI and ML offerings for predictive and generative projects, covering technologies, products, and tools across the data-to-AI lifecycle. It also introduces Google’s 7 AI principles.

Artificial Intelligence 131

Artificial Intelligence Artificial Intelligence BERT Computer Vision 131

Unite.AI

MARCH 27, 2024

Likewise, ethical considerations, including bias in AI algorithms and transparency in decision-making, demand multifaceted solutions to ensure fairness and accountability. Addressing bias requires diversifying AI development teams, integrating ethics into algorithmic design, and promoting awareness of bias mitigation strategies.

Natural Language Processing 278

Natural Language Processing AI AI Algorithm 278

AWS Machine Learning Blog

DECEMBER 16, 2024

At re:Invent, we announced Amazon Bedrock Guardrails Automated Reasoning checks the first and only generative AI safeguard that helps prevent factual errors due to hallucinations. All by using logically accurate and verifiable reasoning that explains why generative AI responses are correct.

Generative AI 91

Generative AI Automation AI AI 91

Flipboard

APRIL 1, 2025

Explainable validation results Each validation check produces detailed findings that indicate whether content is Valid, Invalid, or No Data. The system creates logical representations of both the input question and the applications response, evaluating them against the established policy rules.

Automation 164

Automation Generative AI AI AI 164

Unite.AI

MAY 3, 2024

Here are some critical areas where offensive AI demands our immediate attention: Urgent Need for Regulations: The rise of offensive AI calls for developing stringent regulations and legal frameworks to govern its use. Having clear rules for responsible AI development can stop bad actors from using it for harm.

AI 162

AI AI Responsible AI Artificial Intelligence 162

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.

Chatbots 204

Chatbots Neural Network BERT OpenAI 204

Towards AI

JULY 27, 2024

Dedicated to safety and security It is a well-known fact that Anthropic prioritizes responsible AI development the most, and it is clearly seen in Claude’s design. This generative AI model is trained on a carefully curated dataset thus it minimizes biases and factual errors to a large extent.

ChatGPT 119

ChatGPT Prompt Engineer Prompt Engineering Explainability 119

Snorkel AI

FEBRUARY 22, 2023

At Snorkel AI’s 2022 Future of Data-Centric AI virtual conference, Eisenberg gave a short presentation on the way he and his colleagues are working to operationalize the assessment of responsible AI systems using a Credo AI tool called Lens. My name is Ian Eisenberg, and I head the data science team at Credo AI.

Responsible AI 52

Responsible AI AI AI Data Science 52