We present the Seldonian Toolkit, which enables software engineers to integrate provably safe and fair machine learning algorithms into their systems. Software systems that use data and machine learning are routinely deployed in a wide range of settings from medical applications, autonomous vehicles, the criminal justice system, and hiring processes. These systems, however, can produce unsafe and unfair behavior, such as suggesting potentially fatal medical treatments, making racist or sexist predictions, or facilitating radicalization and polarization. To reduce these undesirable behaviors, software engineers need the ability to easily integrate their machine-learning-based systems with domain-specific safety and fairness requirements defined by domain experts, such as doctors and hiring managers. The Seldonian Toolkit provides special machine learning algorithms that enable software engineers to incorporate such expert-defined requirements of safety and fairness into their systems, while provably guaranteeing those requirements will be satisfied. A video demonstrating the Seldonian Toolkit is available at https://youtu.be/wHR-hDm9jX4/.
@inproceedings{Hoag23icse-demo,
author = {Austin Hoag and James E. Kostas and Bruno Castro da Silva and
Philip S. Thomas and Yuriy Brun},
title =
{\href{http://people.cs.umass.edu/brun/pubs/pubs/Hoag23icse-demo.pdf}{Seldonian Toolkit:
{Building} Software with Safe and Fair Machine Learning}},
booktitle = {Proceedings of the Demonstrations Track at the 45th International Conference on Software Engineering (ICSE)},
venue = {ICSE Demo},
address = {Melbourne, Australia},
month = {May},
date = {14--20},
pages = {107--111},
year = {2023},
accept = {$\frac{38}{80} \approx 48\%$},
note = {\href{https://doi.org/10.1109/ICSE-Companion58688.2023.00035}{DOI: 10.1109/ICSE-Companion58688.2023.00035}},
doi = {10.1109/ICSE-Companion58688.2023.00035},
abstract = {<p>We present the Seldonian Toolkit, which enables software engineers to
integrate provably safe and fair machine learning algorithms into their
systems. Software systems that use data and machine learning are routinely
deployed in a wide range of settings from medical applications, autonomous
vehicles, the criminal justice system, and hiring processes. These systems,
however, can produce unsafe and unfair behavior, such as suggesting
potentially fatal medical treatments, making racist or sexist predictions, or
facilitating radicalization and polarization. To reduce these undesirable
behaviors, software engineers need the ability to easily integrate their
machine-learning-based systems with domain-specific safety and fairness
requirements defined by domain experts, such as doctors and hiring managers.
The Seldonian Toolkit provides special machine learning algorithms that
enable software engineers to incorporate such expert-defined requirements of
safety and fairness into their systems, while provably guaranteeing those
requirements will be satisfied. A video demonstrating the Seldonian Toolkit
is available at https://youtu.be/wHR-hDm9jX4/.</p>},
fundedBy = {NSF CCF-1763423, NSF CCF-2018372, NSF CCF-2210243, Berkeley Existential Risk Initiative.},
}