Explainable AI as a consequence of target system ignorance in Machine Learning.

Key Points

• Explainable AI evolves from understanding target system limitations, improving trust and transparency.
• Key evidence indicates that recognizing ignorance in target systems can enhance algorithm accuracy and interpretation.
• Analysis of machine learning algorithms highlights the necessity of incorporating explanations for better decision-making.
• This suggests a critical need for further exploration of target system knowledge, ensuring applicable strategies in diverse contexts.

Abstract