Evaluating AI-Generated Insights, Information and Resources

This standard defines the competencies required to evaluate insights produced by generative AI (GenAI) tools effectively. It includes the ability to assess the relevance, applicability, and accuracy of GenAI outputs, while identifying potential biases or limitations. It also highlights the importance of applying critical thinking to guide strategic and operational decision-making processes informed by GenAI to maximise the value of generative AI applications while maintaining reliability and precision in their work.

This standard is designed for professionals who use GenAI insights in their roles.

1. Analyse generative AI (GenAI) produced insights to confirm their relevance to specific tasks or objectives.
2. Evaluate the context of generated insights to determine their applicability to real-world scenarios.
3. Apply critical thinking to evaluate the accuracy of GenAI produced data and conclusions.
4. Identify limitations or bias in GenAI outputs that may affect accuracy or validity.
5. Evaluate security risks associated with the use and interpretation of GenAI insights, ensuring compliance with organisational policies and industry best practice.
6. Present findings from GenAI outputs to stakeholders to facilitate informed decision-making.
7. Monitor the ongoing behaviour and outcomes of GenAI systems to identify potential accuracy, security or ethical issues and maintain adherence to organisational requirements.
8. Revise evaluation criteria and processes based on feedback and outcomes to improve future assessments.
9. Develop guidelines for collaborating with GenAI, incorporating clear criteria for when and how decisions based on AI-generated insights should be escalated for human review or higher-level approval.

1. Fundamental concepts in data analysis and evaluation, including the principles of relevance, accuracy, and bias.
2. Best practices for verifying the credibility and integrity of data sources used by GenAI models to produce insights.
3. Techniques for evaluating the quality, reliability, and applicability of data generated by GenAI tools.
4. Statistical methods for assessing the reliability and precision of GenAI outputs, including error margins and confidence intervals.
5. The role of error margins, confidence intervals, and data variability in interpreting and validating GenAI outputs.
6. The significance of contextual understanding when interpreting insights generated by GenAI tools.
7. The basic processes underpinning the generation of outputs by GenAI models.
8. Common sources of bias in GenAI outputs and methods for identifying and mitigating them.
9. How to address ethical considerations related to the use of GenAI insights, including fairness, accountability, and transparency in how AI outputs are generated, assessed, and applied in decision-making processes.
10. Industry standards, guidelines, and frameworks for the responsible use of GenAI in decision-making processes.
11. Best practices for monitoring GenAI system behaviour and implementing regular updates to maintain security and reliability.
12. Techniques for determining when to prioritise GenAI insights versus human judgment, including the use of escalation protocols.
13. Strategies for effectively integrating GenAI insights into organisational decision-making frameworks.
14. Methods for collecting and analysing feedback on the outcomes of decisions informed by GenAI insights.
15. Adaptive strategies for refining and improving GenAI evaluation processes based on performance outcomes and organisational goals.