magyarázhatóságnál

"Magyarázhatóságnál" is a Hungarian term that combines two concepts: *magyarázhatóság* (explainability) and the preposition *nál* (at, by, or in comparison to). The phrase is often used in discussions about artificial intelligence, data science, and machine learning, particularly in relation to the interpretability or transparency of algorithms and models. It refers to the principle that a model's decisions or outputs should be understandable or explainable, ideally to both technical and non-technical stakeholders.

In the context of machine learning, explainability is crucial for building trust, ensuring fairness, and complying with regulations such as the EU’s General Data Protection Regulation (GDPR), which requires transparency in automated decision-making. Models like decision trees, linear regression, or rule-based systems are inherently more explainable than black-box models like deep neural networks, which often operate with opaque internal mechanisms.

The concept of *magyarázhatóságnál* also extends to evaluating trade-offs between model performance and interpretability. While complex models may achieve higher accuracy, their lack of transparency can hinder adoption in fields like healthcare or finance, where accountability is essential. Techniques such as SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model-agnostic Explanations), and feature importance analysis are commonly used to enhance explainability in machine learning pipelines.

Beyond AI, the term can apply broadly to any system where transparency in decision-making processes is valued, including algorithmic fairness, ethical AI development, and user-centric design. Organizations often prioritize explainability to foster accountability, mitigate bias, and ensure that automated systems align with human values and expectations.