Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.

Propagation of distributions by Monte-Carlo sampling: Real number types with uncertainty represented by samples.

Analysis of digital elevation models (DEMs)

Model verification, validation, and error analysis

Helping AI practitioners better understand their datasets and models in text classification. From ServiceNow.

OlliePy is a python package which can help data scientists in exploring their data and evaluating and analysing their machine learning experiments by utilising the power and structure of modern web applications. The data scientist only needs to provide the data and any required information and OlliePy will generate the rest.

Process global-scale satellite and airborne elevation data into time series of glacier mass change: Hugonnet et al. (2021).

A tool for classifying mistakes in the output of parsers

A tool for classifying errors in coreference resolution

Responsible AI Workshop: a series of tutorials & walkthroughs to illustrate how put responsible AI into practice

A package for handling numeric quantities with asymmetric uncertainties.

Error analysis, diagnostic tests and plots for nested sampling calculations.

Process case studies on DEM uncertainty analysis at the Mont-Blanc massif and Northern Patagonian Icefield: Hugonnet et al. (2022).

CECS (C/C++ Error Control System) is a pure C libary, with C++ support, for textual informative error control. Errors, Warnings or Info that is recording during runtime can be traced back to the file and line where the error occured.

Nobunaga: Object Detection Analyzer

Pyshifts: A Pymol Plugin for Chemical Shift-Based Analysis of Biomolecular Ensembles

Machine learning techniques, such as Linear Regression, Logistic Regression, Neural Networks (feedforward propagation, backpropagation algorithms), Diagnosing Bias/Variance, Evaluating a Hypothesis, Learning Curves, Error Analysis, Support Vector Machines, K-Means Clustering, PCA, Anomaly Detection System, and Recommender System.

Code for modeling and data analysis of PHYS128 AL@UC Santa Barbara. For Demonstration.

Toolkit for studying numerical analysis and floating point algebra round-off

Automatically export Windows event logs to CSV