Model Performance

Precision

Precision is defined as the percentage of positive or relevant outcomes.

Precision is defined as the percentage of positive or relevant outcomes. Precision is calculated as below:

Precision = True Positives / True Positives+False Positives

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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

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)

Low-Rank Adaptation (LoRA)

Multi-modal models

Mixture of experts (MoEs)

Opensource vs. Closed Source Models

Diffusion Models

Transformers Models

Vector database

Large Language Models (LLMs)

Foundation models

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

Bias Monitoring

Data Monitoring

ML Observability

Regression model

Classification model

Model confidence score

Model threshold

Surrogate models

Feature Importance Store

Fairness/Bias Monitoring

Data Processing

Recall/ Sensitivity or True Positive Rate

Specificity/ True Negative Rate:

Precision-recall curve

Confusion Matrix

ROC Curves and ROC AUC