Data Transparency

Data transparency in artificial intelligence focuses on making the processes and data used to train AI models more accessible and understandable, aiming to improve accountability, trustworthiness, and fairness. Current research emphasizes developing methods for auditing models and data without compromising intellectual property, using techniques like zero-knowledge proofs and cryptographic commitments, and establishing robust data provenance tracking systems, often incorporating database approaches and data sketching for efficient querying. This work is crucial for addressing ethical concerns, ensuring compliance with regulations, and fostering greater confidence in AI systems across various applications.