Скачать или смотреть 1.4 AI Life Cycle - AAIA Domain 1 - Part A - AI Models, Considerations and Requirements

00:00 1.4 AI Life Cycle - AAIA Domain 1 - Part A - AI Models, Considerations & Requirements

00:02 AI Life Cycle - Based on the OECD Framework

00:27 The Building Metaphor - Comparing the AI lifecycle to the lifespan of a corporate building (plan, gather, build, inspect, occupy, maintain, demolish).

00:43 Risk Perspectives (Latent Risk) - Discussing risk changing over time, specifically "latent risk" which is sleeping/hidden initially but wakes up and increases later as the system evolves.

01:04 Developer vs. Deployer (The Car Analogy) - Contrasting the perspective of the AI Developer (car engine manufacturer) versus the AI Deployer (driver using the car as a taxi in snow). Shared responsibility.

01:44 Phase 1 - Plan and Design (The Foundation) - It is the foundation. Need to assess risk here to avoid ethical issues, bias, and lack of accountability.

02:06 Phase 1 Activities (Three Major Steps) - Three major activities: 1. Document Business Use Case (stakeholders, accountability). 2. Initial Risk Assessment (ethics, bias, Data Governance Framework). 3. Prototyping (fail fast, fix early).

03:01 Phase 2 - Collect and Process Data (Fuel Metaphor) - Data is fuel. Dirty fuel breaks the engine. Quality impacts reliability. Risks regarding accuracy, bias, and privacy.

03:21 Data Management Steps - Steps for managing data: 1. Gathering and Consent (litigation risk). 2. Cleaning and Quality Checking (remove errors, check completeness/timeliness). 3. Documentation (auditing, characteristics, intended uses).

04:21 Phase 3 - Build/Adapt Models - The math happens. Algorithm selection, calibration, training (feeding large volumes of data). Key concept: 'Explainability' (understanding how the model arrived at a decision).

04:52 Phase 3 Controls (Audit & HITL) - Two specific risk controls: 1. Automated Event Recording (system logs its own actions, audit trail). 2. HITL (Human in the Loop - expert oversees AI).

05:16 Phase 4 - Test, Evaluate, Verify, Validate

05:22 Explaining the difference between Verification (Did we build the product correctly? Meets specs?) and Validation (Did we build the right product? Solves the business problem?).

05:45 Five Testing Techniques - Listing five specific testing techniques: Model Testing, Stress Testing, Comparative Analysis, Bias and Fairness Checks, Scenario Analysis.

06:33 Phase 5 - Deploy - Phase 5: Go-live. Piloting (proof of concept). Challenge of Compatibility with legacy systems. Downstream Deployers (documentation). Organizational Change Management (training staff).

07:20 Phase 6 - Operate and Monitor - AI is live, continuous phase. Constantly assess for consequences. Use a Quality Management System (documentation, audits, compliance).

07:47 Phase 7 - Retire and Decommission - End of life. Obsolete or high risk. Decommissioning Plan. Crucially involves secure Data Migration or Deletion. Communicate with stakeholders.

08:22 Wrap up of the AI Life Cycle. Reminder that risk exists at every single stage.

08:35 Go to Roocloud.com for MCQs on this section.