Responsible AI dashboard tool, based on Fairlearn, that measures gaps in model performance and selection rate between groups formed from sensitive features like age, ethnicity or gender.
Read more: Microsoft Learn
In the Ultra Transcenders books
Each book explains Fairness assessment in context, with comparison tables and the common traps.
Terms in this definition
- Responsible AI dashboard
Interface in Azure Machine Learning that brings together error analysis, fairness, interpretability, data analysis and counterfactual and causal analysis, helping you debug and assess a model ahead of deployment.
- Chat message roles
Labels on chat messages: instructions go under system, the person's input under user, the model's previous answers under assistant, and results returned by a called tool under tool (or function).
- Fairlearn
Open-source library underpinning fairness assessment in Azure Machine Learning, offering disparity metrics plus reduction and post-processing mitigation algorithms.
- Performance
Routing method in Traffic Manager that directs users to whichever endpoint offers the lowest latency.
- Document Intelligence add-on capabilities
Extra Document Intelligence options switched on with the features query parameter: barcodes, formulas, keyValuePairs, languages, ocrHighResolution, queryFields and styleFont.
- LIKE
Compares strings with a pattern that can contain the % and _ wildcards. Because it only understands character patterns, searching big volumes of text this way is much slower than using full-text search.