Resources to learn more about AI Governance.
Get up-to-date details about the ideas, discussions, and processes around AI Governance, ML Observability and ML Ops.
AryaXAI Synthetics: Using synthetic ‘AI’ to compliment ‘ML Observability’
AryaXAI synthetics to resolve critical data gaps, test models at scale and preserve data privacy
Singapore Guidelines on Artificial Intelligence: How Singapore Policies Impact the future of 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.
Artificial Intelligence and Its Regulatory Landscape: US Readies for a New AI Bill of Rights and Regulations
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.
AI Regulations & Laws In India: A Step Towards Ethical AI Use
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.
The AI black box problem - an adoption hurdle in insurance
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?
ML Observability: Redesigning the ML lifecycle
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.
Deep dive into Explainable AI: Current methods and challenges
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
Whitepaper: AI Governance for Lending in India: Components & Challenges
Impact of RBI guidelines and components required for AI Governance
Policies and regulations around AI usage: Interpretation and impact
Overview of global Policies and regulations around AI usage, their sufficiency and impact on our future
AI Explainability Framework in Financial Services: The Trust Imperative
Explore the current AI adoption in financial services, the ‘black box’ problem with AI and how explainability helps resolve the trade-off between accuracy, automation and being compliant
AryaXAI: Accelerating the path to ML transparency
Arya.ai has innovated a state-of-the-art framework, ‘Arya-xAI’ to offer transparency, control and Interpretability on Deep learning models. This whitepaper explores the explainability imperative, tangible business benefits of XAI, overview of current XAI methods and their challenges and details on the functioning of Arya-XAI framework
This MLOps wiki offers a collection of clear explanations of the various MLOps concepts, their significance, and how they are managed throughout the ML lifecycle.
Kolmogorov–Smirnov test (K–S test or KS test)
Population Stability Index (PSI)
Kullback-Leibler (KL) divergence
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