Interpretability refers to how easily a model or system’s results can be understood and explained, making it clear how decisions or predictions are made.
To ensure AI systems are transparent and fair, we implement various techniques such as explainability, interpretability, fairness, and bias detection.…
Ensuring transparency and explainability in AI algorithms is crucial for building trust and addressing concerns related to algorithmic biases, decision-making,…
AI in natural language understanding has made significant progress, but it still has limitations. These limitations include semantic ambiguity, complex…