1 / 2

The Foundation The Four Pillars of Operational AI Governance - Nate Patel

An effective MVG framework isn't a single document; it's an integrated system resting on four critical pillars. Neglect any one, and the structure collapses.<br><br>

Nate13
Télécharger la présentation

The Foundation The Four Pillars of Operational AI Governance - Nate Patel

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. The Foundation: The Four Pillars of Operational AI Governance | Nate Patel An effective MVG framework isn’t a single document; it’s an integrated system resting on four critical pillars. Neglect any one, and the structure collapses. 1.  Policy Pillar: The “What” and “Why” — Setting the Rules of the Road Purpose: Defines the organization’s binding commitments, standards, and expectations for responsible AI development, deployment, and use. Core Components: Risk Classification Schema: A clear system for categorizing AI applications based on potential impact (e.g., High-Risk: Hiring, Credit Scoring, Critical Infrastructure; Medium-Risk: Internal Process Automation; Low-Risk: Basic Chatbots). This dictates the level of governance scrutiny. (e.g., Align with NIST AI RMF or EU AI Act categories).

  2. Core Mandatory Requirements: Specific, non-negotiable obligations applicable to all AI projects. Examples: Human Oversight: Define acceptable levels of human-in-the-loop, on- the-loop, or review for different risk classes. Fairness & Bias Mitigation: Requirements for impact assessments, testing metrics (e.g., demographic parity difference, equal opportunity difference), and mitigation steps. Transparency & Explainability: Minimum standards for model documentation (e.g., datasheets, model cards), user notifications, and explainability techniques required based on risk. Robustness, Safety & Security: Requirements for adversarial testing, accuracy thresholds, drift monitoring, and secure Read More: From Principles to Playbook: Build an AI-Governance Framework in 30 Days Read More Articles: Building Your AI Governance Foundation AI Governance: Why It’s Your Business’s New Non-Negotiable — Nate Patel Follow Nate Patel for More on AI Strategy and Ethical Innovation: ?LinkedIn:linkedin.com/in/npofc ?X (formerly Twitter):x.com/npatelofc ?Instagram:instagram.com/natepatel.aicpto Stay connected to discover the latest in AI insights, enterprise strategy, and future-focused keynotes.

More Related