FABR Framework Checklist: For Responsible Adoption of AI

A practical set of questions that helps business analysts assess the five key business risks in AI adoption defined by the FABR Framework.

Artificial Intelligence is rapidly becoming an essential part of business operations. Yet, alongside its innovation potential come risks that must be understood and monitored.
As part of the #BA4AI movement, the FABR Framework (Five AI Business Risks Framework) was designed to help business analysts, leaders, and technology professionals identify, evaluate, and mitigate risks in AI initiatives.

More than a theoretical model, FABR serves as a practical governance tool — a critical lens for mapping requirements and ensuring that AI adoption remains aligned with human values and business objectives.

Business analyst applying the FABR Framework Checklist – generated with ChatGPT
Business analyst applying the FABR Framework Checklist – generated with ChatGPT

This is the third and final article I’m sharing about this guide.

In the first one, FABR Framework: A Guide to Lead AI Adoption with Safety and Value, I presented the origin of each of the five business risks associated with AI, based on the way artificial intelligence makes decisions through probabilistic models versus rule-based systems.

The second article, FABR Framework Applied Case Studies, shows how to use it in four realistic scenarios across different industries (Government, Education, Finance, and Tourism), where analysts must anticipate risks to ensure the safe adoption of AI.

Now, I’m sharing a checklist of questions that can be incorporated into any organization’s methodology for AI projects, helping to identify risks and anticipate preventive actions within the five key dimensions of the framework.

🧮 Decision Errors

  • Are mathematical calculations or logical decisions being made through deterministic, rule-based processes instead of being inferred from statistical models?
  • In processes that rely on statistical models, have the training data been validated and proven reliable?
  • Are there periodic audits of automated decisions to verify the model’s consistency and accuracy?
  • Is there a regular process in place to fine-tune the model and improve its results?
  • Are sensitive decisions reviewed by humans?

⚖️ Bias & Discrimination

  • Do the training data accurately and proportionally represent the various categories within the context in which the model will be applied?
  • Do the training data include any historical bias that could reproduce injustice or harm certain groups?
  • Have fairness and equity metrics been defined to test the model’s performance in sensitive scenarios?
  • Is there a guarantee that AI-generated data will not be reused for retraining (which could amplify bias exponentially)?
  • Have logical constraints (guardrails) been implemented to prevent unfair or harmful behavior?
  • Are outlier cases identified and handled through separate processes with human supervision?

🔐 Privacy Violation

  • Is there authorization to use the data that trains the model?
  • Have training data been anonymized to prevent the AI from learning private information?
  • Do systems that store or process sensitive data include proper security, encryption, and restricted access policies?
  • Are privacy laws, consent requirements, and governance policies being followed in data collection, storage, and use?
  • Are users’ data being used for purposes other than those originally authorized?

🤖 Over-Reliance on AI

  • Are there established testing and validation procedures to verify the accuracy of system outputs?
  • In cases where automation cannot provide 100% certainty, is human oversight required to confirm or correct the AI’s response?
  • Are people trained and encouraged to critically evaluate AI-generated responses?
  • Do AI systems clearly communicate their limitations and potential for errors when applicable?
  • Are there mechanisms for manual data correction and process adjustment when failures occur?

🧩 Explainability & Accountability

  • Can the process be audited to identify events and outcomes?
  • Can the reasoning behind a decision or result be presented clearly and transparently, using explicit criteria that are relevant and business-oriented (not purely technical)?
  • Given the same input data, does the model consistently produce the same output (within expected variance)?
  • Is accountability clearly defined in case of errors or harm?
  • Have hybrid models (combining rule-based and statistical approaches) been evaluated to achieve the best context-specific results?
  • Are decision rules and processes properly documented?

🚀 Conclusion – From Questions to Good Decisions with the FABR Framework

The FABR Framework is not just a conceptual model but a reflection and diagnostic tool.
Asking the right questions is the first step toward reducing risks and creating ethical, safe, and outcome-driven AI solutions.

Business analysts must embrace frameworks like this to lead their organizations through the responsible and transparent adoption of AI, ensuring fairness, accountability, and real business value.

💡 Technology can make decisions; it’s our job to ensure they’re the right ones.

Further Reading and References