The Paris AI Summit 2025 set global commitments for ethical AI, governance, and innovation

Understand AI Governance, its importance, and how to build an effective governance framework.

Discover how Explainable AI (XAI) enhances transparency, trust, and compliance in high-stakes AI applications.

Exploring the key ethical challenges in AI and its impact on society

Top 8 Generative AI Trends Shaping 2025: Insights & Innovations

Explore the core principles of AI alignment, practical approaches to achieving AI alignment.

Get insights on AI Risk Management, strategies and lessons from Case Studies

Explore the concept of AI alignment, the risks of misalignments, and emergent behaviors—and why it is crucial for building trustworthy AI.

Understand generative AI, key models and ethical challenges

Explore the role of explainable AI in building transparent and trustworthy models across the AI lifecycle.

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.

The world's first comprehensive AI law the European Artificial Intelligence Act (AI Act) entered into force on 1st August 2024. The act establishes a comprehensive framework for the responsible and ethical development, deployment, and use of AI technologies.

As organizations navigate real-world complexities, it is essential to prioritize both MLOps and observability to create a solid foundation for building, maintaining, and scaling trustworthy ML models.

In the world of machine learning, data is the cornerstone of building robust models. Synthetic AI and Generative AI are already making waves, reshaping various industries and creative processes. In this blog, we will delve into the nuances of Synthetic AI and Generative AI highlighting their distinctions and potential applications.

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

Unlock a more effective approach to ML Observability

AryaXAI synthetics to resolve critical data gaps, test models at scale and preserve data privacy

Published at MedCity News

Published at AIM Leaders Council

Interview with Unite.AI

The concerns around AI usage have rushed governments around the world to formulate legal frameworks to control and govern AI and its impacts, including Singapore.

Regulatory and legal frameworks, although still catching up with AI, are thoroughly changing the regulatory landscapes. In October, 2022, the White House Office of Science and Technology Policy (OSTP) unveiled its Blueprint for an AI Bill of Rights.

While there has been profound focus on development of AI and its applications, the Indian Government is now speeding up to formulate laws, policies and clear guidelines for regulating and governing AI.

Report by Analytics India Magazine

Published at Analytics India Magazine

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?

While businesses want to know when a problem has arisen, they are more interested in knowing why the problem arose in the first place. This is where ML Observability comes in.

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.

Published at Analytics India Magazine

By Computer World