Course Outline
Module 1: The Evolution of AI Oversight
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Moving past static predictions (fraud flags) to action-oriented, autonomous Agentic AI
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The hidden cost of full autonomy: Financial, legal, and operational risks of AI edge cases
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Defining the three vectors of valid oversight: Context, Authority, and Rationale
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Finding the equilibrium: Balancing business throughput with necessary human friction
Module 2: The Oversight Taxonomy (HITL vs. HOTL vs. HOOTL)
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Human-in-the-Loop (HITL): Halting the system for human authorization before execution (appropriate for high-risk, irreversible financial or legal actions)
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Human-on-the-Loop (HOTL): Allowing autonomous execution with a human supervisor maintaining continuous veto/abort capabilities
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Human-out-of-the-Loop (HOOTL): Full system autonomy paired with automated guardrails and asynchronous post-event human auditing
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Dynamic Loop Shifting: Designing architectures that automatically switch between loops based on risk profiles and changing environments
Module 3: Architectural Design & Risk Routing Pipelines
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Confidence-Based Routing: Implementing software gateways that automatically intercept low-confidence model outputs and route them to human queues
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Designing Decision Lanes: Matching response SLAs to transaction risk (e.g., 30 seconds for low-risk access vs. 15 minutes for high-value disbursements)
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Fail-Safe Defaults: Establishing deterministic system behavior when a human supervisor fails to respond within the SLA window
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Two-Factor Judgment: Engineering dual independent human reviews or counter-model sanity checks for ultra-critical system commands
Module 4: Managing the Human Factor & Overcoming Complacency
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The psychology of Automation Complacency: Why humans stop questioning reliable machines and how to combat it
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Managing human cognitive load and decision fatigue in high-volume review queues
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Structuring communication protocols: Utilizing standardized, unambiguous phraseology for human-AI escalations and overrides
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Diversity in the loop: Structuring review cohorts to actively discover and mitigate cultural, demographic, and algorithmic bias
Module 5: Continuous Improvement & Feedback Telemetry
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Data loop economics: Turning human overrides into valuable training data
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Active Learning Frameworks: Structuring the system to programmatically identify and request human clarification on its own data "blind spots"
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Operationalizing feedback loops: Integrating human review outputs into fine-tuning, RLHF (Reinforcement Learning from Human Feedback), and DPO pipelines
Module 6: Compliance, Governance, and Defensibility
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Aligning HITL workflows with global AI policy mandates
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Audit Trail Engineering: Designing cryptographically sound logs that capture what context the human saw, what authority they possessed, and their explicit rationale for every intervention
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Creating unambiguous Human-AI Accountability Models using modified RACI matrices
Module 7: "The Flight Simulator" Operational Workshop
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Scenario Briefing: Analyzing major historical system failures caused by broken human-automation handoffs (Aviation, FinTech, Autonomous Driving)
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Design Exercise: Mapping an end-to-end human oversight pipeline for an enterprise workflow (e.g., automated underwriting or autonomous procurement)
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Adversarial Run: Simulating system drift, edge-case cascades, and adversarial attacks to test if the delegates' designed escalation paths hold up under pressure
Format of the Course
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Interactive lectures and real-world system architecture breakdowns.
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Adversarial simulation exercises where delegates practice managing simulated system failures, rogue AI agents, and critical handoff scenarios.
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Hands-on blueprinting design workshops to map out an enterprise HITL operational workflow.
Course Customisation Options
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This course can be technical (focusing on code-level confidence routing, active learning triggers, and database logging) or operational/managerial (focusing on workforce management, compliance, UI/UX design, and business risk frameworks). Please specify your preference upon booking.
Requirements
Audience
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AI Product Managers and Business Analysts
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Operations Directors and Customer Experience (CX) Leads
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Systems Architects and AI/ML Engineers
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Risk Officers, Compliance Managers, and Legal Counsel
Requirements
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General familiarity with how enterprise AI solutions or automated workflows function at a high level.
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No background in machine learning mathematics or programming is necessary for the standard operational track.
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 2600 € + VAT*
Contact us for an exact quote and to hear our latest promotions
Testimonials (2)
Fun to talk
Jihan Fadila - BAF
Course - Root Cause Analysis (RCA) for Internal Audit
casual conversation supported by specific examples
