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.

Published at MedCity News

Published at AIM Leaders Council

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?

As organizations scale their AI and ML efforts, they are now reaching an impasse - explaining and justifying the decisions by AI models. Also, the formation of various regulatory compliance and accountability systems, legal frameworks and requirements of Ethics and Trustworthiness, mandate making AI systems adhere to transparency and traceability

With packaged AI APIs in the market, more people are using AI than ever before, without the constraint of compute, data or R&D. This provides an easy entry point to use AI and gets users hooked for more. However, the first legal framework for AI is here! One of the many mandates in the proposal is to make AI systems adhere to transparency and traceability. These additional requirements highlight the ever-increasing need for Explainable AI.