Model-Audit

AI Explainability: Making Models Understandable AI systems increasingly influence hiring, lending, health care, and public services. Explainability means giving people clear reasons for a model’s decisions and making how the model works understandable. Clear explanations support trust, accountability, and safer deployment, especially when money or lives are on the line. Vetted explanations help both engineers and non experts decide what to trust. Explainability comes in two broad flavors. Built-in transparency, or ante hoc, tries to make the model simpler or more interpretable by design. Post hoc explanations describe a decision after the fact, even for complex models. The best choice depends on the domain, the data, and who will read the result. ...

Privacy-Preserving Machine Learning in Practice Privacy-preserving machine learning helps teams use data responsibly. You can build useful models without exposing individual details. The goal is to protect people while keeping value in analytics and products. Key methods are practical and often work together. Differential privacy adds controlled noise so results stay useful but protect each person. Federated learning trains models across many devices or sites and shares only updates, not raw data. Secure multiparty computation lets several parties compute a result without revealing their inputs. Homomorphic encryption is powerful but can be heavy for large tasks. Data minimization and synthetic data reduce exposure, while governance and audits keep things on track. ...