ArticlesVideosResourcesResearch PapersAI WikisPodcastsWhite papersTutorials

Whitepaper

Explore cutting-edge research and trends in AI regulations, MLOps, ML lifecycle, ML observability, and AI Governance

Stay up to date with all updates

Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.

Follow Us

AI Policies

Decoding the EU AI Act: A Deep Dive into Europe’s AI Rulebook

A strategic guide to the EU AI Act
Ketaki Joshi
March 20, 2025
AI Policies

The Evolving Landscape of AI Regulations in the US: Challenges, best practices and implementing effective AI Governance strategies

Emerging regulatory framework for AI in the US
Ketaki Joshi
May 12, 2023
AI Policies

AI Governance for Lending in India: Components & Challenges

Impact of RBI guidelines and components required for AI governance
Ketaki Joshi
February 16, 2023
AI Policies

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
Vinay Kumar
August 29, 2022
AI Explainability

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
Ketaki Joshi
August 24, 2022
AI Explainability

AryaXAI: Accelerating the path to ML transparency

AryaXAI by Arya.ai offers 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
Ketaki Joshi
August 18, 2022

Is Explainability critical for your AI solutions?

Schedule a demo with our team to understand how AryaXAI can make your mission-critical 'AI' acceptable and aligned with all your stakeholders.

Book a Demo

AryaXAI provides the most accurate explainability and alignment stack to deliver accurate, true-to-model explainability, monitoring, risk management, and alignment techniques essential for highly mission-critical or regulated AI solutions.

Address: CoWrks, 3rd Floor, Prudential Building,
Powai, Mumbai- 400076

Company
Resources
Products

Follow Us

Subscribe

Stay up to date with all updates

Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.

© Copyright 2024, Lithasa Technologies Pvt. Ltd,

Terms and ConditionsPrivacy PolicyPayments and Refunds PolicyContent Removal
Privacy Evaluation
F-Score (F1-Score)
Constant Features
High Feature Correlation
Target Drift
Stochastic Gradient Descent (SGD)
RandomForest
CatBoost (Categorical Boosting)
LightGBM (Light Gradient Boosting Machine)
XGBoost (eXtreme Gradient Boosting)
CTGAN (Conditional Tabular Generative Adversarial Network)
GPT-2 (Generative Pre-trained Transformer 2)
Internet Information Service Algorithm Recommendation Management Regulations
Generative AI Measures in China
Provisions on the Administration of Deep Synthesis of Internet-based Information Services
Artificial Intelligence and Algorithmic Fairness Initiative
The EU AI Act
Artificial Intelligence Risk Management Framework (AI RMF 1.0)
Federal Trade Commission (FTC)
President Biden's Executive Order on AI
Principles for Responsible AI
Digital India Act
Draft National Data Governance Framework Policy
National Strategy for Artificial Intelligence #AIFORALL: NITI Aayog
National Cybersecurity Reference Framework
Global Partnership on Artificial Intelligence (GPAI)
Top-k
Temperature
Low-Rank Adaptation (LoRA)
Quantization
Hallucination
Multi-modal models
Mixture of experts (MoEs)
Mamba
Opensource vs. Closed Source Models
Tokens
Diffusion Models
Transformers Models
Model safety
Vector database
Large Language Models (LLMs)
Foundation models
Synthetic AI
Generative AI
Z-Test
Chi-square test
Kolmogorov–Smirnov test (K–S test or KS test)
Wasserstein distance
Jensen-Shannon(JS) Divergence
Population Stability Index (PSI)
Kullback-Leibler (KL) divergence
ML Audit
Bias Monitoring
Data Monitoring
ML Observability
MLOps
Regression model
Classification model
Model confidence score
Model threshold
Surrogate models
What-if
Similar Cases
Feature Importance Store
LOCO
SHAP
LIME
Explainability
Fairness/ Bias Monitoring
ML Monitoring
Accuracy
Data Processing
Precision
Recall/ Sensitivity or True Positive Rate
Specificity/ True Negative Rate:
Precision-recall curve
Confusion Matrix
F score
ROC Curves and ROC AUC
Data Drift
Model Drift