The Black Box in AI: Unveiling the Mystery within Intelligent Systems
The Black Box in AI refers to the lack of transparency and interpretability in intelligent systems. AI algorithms learn from data, making it difficult to trace decision-making. This poses implications for critical applications and raises ethical concerns. Approaches like Explainable AI, model transparency, regulatory frameworks, and open-source initiatives aim to address the Black Box problem. These efforts provide human-understandable explanations, improve model interpretability, ensure compliance, and foster transparency. Unveiling the secrets within the Black Box is vital for building trust and deploying AI responsibly.