🎊Remarks and Conclusion

Remarks

Looking back, our experience using ChatGPT for building the ontology brought up some tough challenges that really shaped how we worked. One big issue was that ChatGPT tends to generate a lot of information, making it hard to know when to stop adding new classes and properties. We often found ourselves struggling to decide what to include and what to leave out, making the whole process of creating a clear and organized ontology pretty tough.

We observed an inconsistency in the suggestions provided by ChatGPT when creating the ontology for different cognitive biases. Specifically, while some biases prompted the inclusion of a "BiasedAgent" class, others did not. This lack of uniformity in the suggested format across biases introduced confusion and complexity into the ontology creation process. As a result, we found ourselves grappling with inconsistencies in the structure and organization of the ontology, as certain biases lacked essential components that were present in others. This inconsistency added an additional layer of challenge to the task of crafting a cohesive and comprehensive ontology, requiring us to carefully evaluate and reconcile the variations in ChatGPT's suggestions to ensure coherence and accuracy in the final product.

Another issue that we need to underline is the overlapping meanings of biases. We encountered several cases where the concepts of biases overlapped, making it difficult to differentiate them because they essentially conveyed the same idea. For example, we had the Frequency Illusion Bias and the Baader-Meinhof Phenomenon, which were mentioned in the Bias Codex as different concepts. This presented a challenge in distinguishing between them. While some sources and tools suggest they are the same concept, our task required us to emphasize and model their differences. This highlights the importance of clear definitions and distinctions in bias modeling to avoid confusion and ensure accurate representation.

Additionally, there were instances where we encountered doubts regarding the reliability of the information provided by ChatGPT. As a result, we found ourselves in the position of needing to dedicate considerable time to thoroughly verifying and cross-referencing the information generated by the model. This meticulous double-checking process was essential to ensure the accuracy and credibility of the ontology's content.

Conclusion

Our collaboration with ChatGPT involved a process of refinement and enhancement. We carefully reviewed and iteratively worked on the suggestions provided by ChatGPT, making necessary alterations and improvements to ensure the ontology's coherence and completeness. Through our collective efforts and interventions, this collaboration culminated in a successful outcome, where the final ontology reflects a synthesis of AI-generated insights and human expertise. This iterative process highlights the value of combining AI capabilities with human oversight, ultimately resulting in a more robust and effective knowledge representation of cognitive biases.