Transparency on English AI Terms Dictionary

Source Attribution

Sat, 18 Jul 2026 10:16:04 +0000

Definition

Source attribution refers to the systematic tracking and labeling of origins for data, models, or generated outputs within AI systems. It ensures transparency by linking final results back to their foundational inputs, such as training corpora or specific authors. This practice is critical for maintaining intellectual property rights, ensuring ethical compliance, and providing users with verifiable context. By implementing robust attribution mechanisms, organizations can foster trust and accountability in AI-driven environments, particularly when dealing with copyrighted materials or sensitive information.

Operation Serenata de Amor

Sat, 18 Jul 2026 10:09:51 +0000

Definition

Operation Serenata de Amor is a pioneering open-source project that applies artificial intelligence to analyze public procurement data in Brazil. By utilizing natural language processing and anomaly detection algorithms, it identifies potential irregularities in government contracts, thereby promoting transparency and accountability. This initiative demonstrates how AI can serve democratic processes by empowering citizens and journalists to uncover corruption through data-driven insights.

Summary

A civic tech initiative using machine learning to detect fraud in Brazilian public spending.

Interpretability

Sat, 18 Jul 2026 09:41:13 +0000

Definition

Interpretability, or explainability, involves making the internal workings and decision-making processes of AI models transparent and understandable to humans. This is crucial for debugging, ensuring fairness, and building trust in high-stakes applications. Techniques include feature importance analysis, SHAP values, and attention visualization. Unlike black-box models, interpretable systems allow stakeholders to audit decisions, identify biases, and verify that the model relies on relevant features rather than spurious correlations.

Summary

The degree to which a human can understand the cause of a decision made by an AI model.

black-box

Sat, 18 Jul 2026 09:38:06 +0000

Definition

In AI, a black-box model refers to complex systems like deep neural networks where the internal decision-making logic is opaque and difficult for humans to interpret. While these models often achieve high predictive accuracy, their lack of transparency poses challenges for debugging, regulatory compliance, and trust, leading to the field of Explainable AI (XAI) which seeks to uncover their internal reasoning processes.

Summary

A system where internal mechanisms are hidden, and only inputs and outputs are observable.