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)

[ECCV'24] On the Error Analysis of 3D Gaussian Splatting and an Optimal Projection Strategy

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

Model verification, validation, and error analysis

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

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

A tool for classifying errors in coreference resolution

A package for handling numeric quantities with asymmetric uncertainties.

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

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

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.

Automatically export Windows event logs to CSV

Nobunaga: Object Detection Analyzer

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

Tests the Black-Scholes model's performance on forecasting option call prices of a selected option chain dataset. Discusses factors such as volatility and time to expiration that affect the estimations of call option prices and how this occurs within the dynamics of the model.

Built an error prediction system using LSTM and integrated the model in Flask to make it a web application. Collected logs were trained using the model, and tested on the log data without error.