// MODEL OPTIMIZATION AND PROMPT SYNTAX TERM
AI Audit
An independent review of an AI system to check if it's fair, accurate, secure, and compliant with rules.
TECHNICAL DEFINITION
An AI Audit is a systematic, independent examination of an artificial intelligence system's design, data, performance, and deployment to assess its fairness, accuracy, security, transparency, and compliance with ethical guidelines and regulatory requirements.
BACKGROUND
Generative artificial intelligence (GenAI) is a subfield of artificial intelligence (AI) that uses generative models to generate text, images, videos, audio, software code or other forms of data. These models learn the underlying patterns and structures of their training data, and use them to generate new data in response to input, which often takes the form of natural language prompts.
READ MORE ON WIKIPEDIASYNONYMS & ALIASES
- AI assessment
- AI review
- AI inspection
- ethical audit
USAGE NOTE
Regular AI audits help organizations maintain trust and identify potential issues before deployment.
DEVELOPERS
Organizations developing technology related to AI Audit.
Provides an MLOps platform for explainable AI (XAI), monitoring, and responsible AI, enabling users to audit model behavior, detect bias, and ensure fairness in production.
Offers a machine learning observability platform that helps monitor, troubleshoot, and explain AI models in production, directly supporting AI auditing by tracking performance, bias, and data drift.
Specializes in AI observability and data health monitoring, enabling users to detect and mitigate issues like bias, drift, and data quality problems in AI models, crucial for effective AI audits.
Develops a platform for AI security and robustness, providing tools to test AI models for vulnerabilities, biases, and ensuring their reliability, which constitutes a critical form of AI auditing.
Offers an ML monitoring platform focused on performance, fairness, explainability, and drift detection, providing the necessary visibility for comprehensive AI audits.
Develops open-source toolkits like AI Fairness 360 and AI Explainability 360, which are foundational components used for assessing and auditing AI systems for fairness and transparency.
Through Azure Machine Learning, provides a Responsible AI dashboard and tools for fairness assessment, interpretability, and error analysis, enabling users to audit and improve the trustworthiness of their AI systems.
Offers 'Trustworthy AI' services, including AI risk management, governance, and audit frameworks and methodologies, developing proprietary solutions to help organizations assess and ensure the reliability and ethical compliance of their AI.
Provides Responsible AI services, including the development and implementation of AI governance and audit frameworks, helping clients assess, monitor, and improve the ethical and regulatory compliance of their AI systems.