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 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 2900 € + VAT*
Contact us for an exact quote and to hear our latest promotions
Testimonials (2)
Structured methodologies and practical tools applicable in the daily dynamics of quality management audits. It was an enriching experience.
Mildred M. Colmenares G. - LASER Airlines
Course - Root Cause Analysis (RCA) for Internal Auditors
Machine Translated
Very suitable for supporting work tasks.
Zakie Farhan - BAF
Course - Root Cause Analysis (RCA) for Internal Audit
Machine Translated
