Unite.AI

JUNE 26, 2023

Who is responsible when AI mistakes in healthcare cause accidents, injuries or worse? Depending on the situation, it could be the AI developer, a healthcare professional or even the patient. Liability is an increasingly complex and serious concern as AI becomes more common in healthcare. Not necessarily.

Black Box AI 297

Black Box AI Explainable AI Explainability AI 297

Unite.AI

SEPTEMBER 13, 2023

The remarkable speed at which text-based generative AI tools can complete high-level writing and communication tasks has struck a chord with companies and consumers alike. In this context, explainability refers to the ability to understand any given LLM’s logic pathways.

Explainability 278

Explainability Generative AI AI Tools AI 278

Unite.AI

MARCH 18, 2024

This content often fills the gap when data is scarce or diversifies the training material for AI models, sometimes without full recognition of its implications. While this expansion enriches the AI development landscape with varied datasets, it also introduces the risk of data contamination.

AI 189

AI AI Data Integration Generative AI 189

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. External audits will also grow in popularity to provide an impartial perspective.

Automation 147

Automation AI AI Artificial Intelligence 147

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?

Metadata 220

Metadata Explainability Automation Explainable AI 220

Unite.AI

DECEMBER 17, 2024

Humans can validate automated decisions by, for example, interpreting the reasoning behind a flagged transaction, making it explainable and defensible to regulators. Financial institutions are also under increasing pressure to use Explainable AI (XAI) tools to make AI-driven decisions understandable to regulators and auditors.

Automation 130

Automation AI AI Explainability 130

ODSC - Open Data Science

OCTOBER 21, 2024

Fortunately, there are many tools for ML evaluation and frameworks designed to support responsible AI development and evaluation. This topic is closely aligned with the Responsible AI track at ODSC West  — an event where experts gather to discuss innovations and challenges in AI.

Responsible AI 52

Responsible AI ML Data Scientist Machine Learning 52