When to Trust the Algorithm? Understanding Model Limitations Training Course

Course Overview

Learning Objectives

• By the end of the course, participants will be able to:
• Understand how algorithms and predictive models support organizational decision-making.
• Identify the key assumptions and limitations underlying algorithmic outputs.
• Evaluate the reliability and relevance of model-generated recommendations.
• Assess how data quality affects algorithmic performance.
• Recognize different forms of bias in data, models, and automated decisions.
• Interpret uncertainty, confidence, prediction errors, and model performance indicators.
• Distinguish between correlation, prediction, and causation.
• Identify situations where algorithmic outputs should not be accepted without human review.
• Evaluate model performance when business conditions or data patterns change.
• Recognize warning signs of unreliable or inappropriate algorithmic recommendations.
• Assess explainability, transparency, and accountability requirements.
• Establish appropriate levels of human oversight and intervention.
• Apply governance principles to algorithm-supported business decisions.
• Improve communication between business leaders, data professionals, and technical teams.
• Develop a practical framework for deciding when to trust, challenge, or reject algorithmic outputs.

Who Should Attend

Learning Outcomes

• By the end of the course, participants will be able to:
• Explain the role of algorithms and models in modern organizational decision-making.
• Assess whether an algorithmic output is relevant to a specific business context.
• Identify the main assumptions behind model-generated recommendations.
• Evaluate the impact of data quality and data limitations on model reliability.
• Recognize potential sources of bias in algorithmic systems.
• Interpret model accuracy, confidence, uncertainty, and error indicators.
• Distinguish reliable predictions from potentially misleading outputs.
• Identify model limitations caused by changing markets, behaviors, or operating conditions.
• Detect situations where human judgment should override or challenge an algorithm.
• Evaluate the transparency and explainability of automated recommendations.
• Assess risks associated with excessive reliance on automated decision-making.
• Establish appropriate validation, monitoring, and escalation mechanisms.
• Apply responsible AI and algorithm governance principles.
• Communicate effectively with technical teams when questioning model outputs.
• Develop a practical Trust, Challenge & Escalation Framework for algorithm-supported decisions.

Course Outline