@inproceedings{Hoag23icse-demo,
  author = {Austin Hoag and James E. Kostas and Bruno Castro da Silva and 
  Philip S. Thomas and Yuriy Brun},
  title =
  {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 = {DOI: 10.1109/ICSE-Companion58688.2023.00035}, 
  doi = {10.1109/ICSE-Companion58688.2023.00035},

  abstract = {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/.},

  fundedBy = {NSF CCF-1763423, NSF CCF-2018372, NSF CCF-2210243, Berkeley Existential Risk Initiative.},
}