Beyond cognitive load, several other human-centric factors should be considered when assessing the interpretability of machine learning models. These include:
User Expertise: The background knowledge and experience of users can significantly influence their ability to interpret model outputs. Tailoring explanations to different expertise levels can enhance understanding.
Domain Knowledge: Familiarity with the specific domain in which the model is applied can affect how users perceive and interpret the results. Models should provide context-sensitive explanations that align with users' domain knowledge.
Trust and Confidence: Users' trust in the model's predictions can impact their willingness to rely on its outputs. Factors such as transparency in the model's decision-making process and the ability to validate predictions can enhance user confidence.
Emotional Response: The emotional reactions of users to model outputs, especially in sensitive areas like healthcare or finance, can influence their interpretation. Understanding these emotional factors can help in designing more effective communication strategies.
Cognitive Biases: Users may have inherent biases that affect their interpretation of model outputs. Recognizing and mitigating these biases can lead to more accurate understanding and decision-making.
By considering these factors, researchers and practitioners can develop a more holistic approach to interpretability that goes beyond cognitive load, ensuring that machine learning models are accessible and understandable to a diverse range of users.