Exploring global trends in AI regulation, this blog highlights the growing emphasis on transparency and explainability to ensure accountability and trust in AI systems

Explore the importance of explainability in AI systems to foster trust, meet regulatory standards, and ensure ethical decision-making.

FSIs face significant obstacles due to complex regulatory environments, data privacy concerns, and the growing challenge of AI Technical Debt (TD)

Delve into key XAI techniques, their limitations, and the data-specific challenges that hinder the development of reliable, interpretable AI systems.

Discover the latest milestone in AI regulation: the European institutions' provisional agreement on the new AI Act. From initial proposal to recent negotiations, explore key insights and actions businesses can take to prepare for compliance. Get insights into actions organizations should take to get ready.

Anonymeter, details of Anonymity Tests Using AryaXAI, and case study analysis

Necessity of privacy risk metrics on synthetic data post-generation

Published at MedCity News

Explaining AI decisions after they happen is a complex issue, and without being able to interpret the way AI algorithms work, companies, including insurers, have no way to justify the AI decisions. They struggle to trust, understand and explain the decisions provided by AI. So, how can a heavily regulated industry, which has always been more inclined to conservatism than innovation, start trusting AI for core processes?