Teaching Responsible Machine Learning to Engineers

Hilde Jacoba Petronella Weerts, Mykola Pechenizkiy
Proceedings of the Second Teaching Machine Learning and Artificial Intelligence Workshop, PMLR 170:40-45, 2022.

Abstract

With the increasing application of machine learning models in practice, there is a growing need to incorporate ethical considerations in engineering curricula. In this paper, we reflect upon the development of a course on responsible machine learning for undergraduate engineering students. We found that technical material was relatively easy to grasp when it was directly linked to prior knowledge on machine learning. However, it was non-trivial for engineering students to make a deeper connection between real-world outcomes and ethical considerations such as fairness. Moving forward, we call upon educators to focus on the development of realistic case studies that invite students to interrogate the role of an engineer.

Cite this Paper

BibTeX
@InProceedings{pmlr-v170-weerts22a, title = {Teaching Responsible Machine Learning to Engineers}, author = {Weerts, Hilde Jacoba Petronella and Pechenizkiy, Mykola}, booktitle = {Proceedings of the Second Teaching Machine Learning and Artificial Intelligence Workshop}, pages = {40--45}, year = {2022}, editor = {Kinnaird, Katherine M. and Steinbach, Peter and Guhr, Oliver}, volume = {170}, series = {Proceedings of Machine Learning Research}, month = {08--13 Sep}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v170/weerts22a/weerts22a.pdf}, url = {https://proceedings.mlr.press/v170/weerts22a.html}, abstract = {With the increasing application of machine learning models in practice, there is a growing need to incorporate ethical considerations in engineering curricula. In this paper, we reflect upon the development of a course on responsible machine learning for undergraduate engineering students. We found that technical material was relatively easy to grasp when it was directly linked to prior knowledge on machine learning. However, it was non-trivial for engineering students to make a deeper connection between real-world outcomes and ethical considerations such as fairness. Moving forward, we call upon educators to focus on the development of realistic case studies that invite students to interrogate the role of an engineer.} }
Endnote
%0 Conference Paper %T Teaching Responsible Machine Learning to Engineers %A Hilde Jacoba Petronella Weerts %A Mykola Pechenizkiy %B Proceedings of the Second Teaching Machine Learning and Artificial Intelligence Workshop %C Proceedings of Machine Learning Research %D 2022 %E Katherine M. Kinnaird %E Peter Steinbach %E Oliver Guhr %F pmlr-v170-weerts22a %I PMLR %P 40--45 %U https://proceedings.mlr.press/v170/weerts22a.html %V 170 %X With the increasing application of machine learning models in practice, there is a growing need to incorporate ethical considerations in engineering curricula. In this paper, we reflect upon the development of a course on responsible machine learning for undergraduate engineering students. We found that technical material was relatively easy to grasp when it was directly linked to prior knowledge on machine learning. However, it was non-trivial for engineering students to make a deeper connection between real-world outcomes and ethical considerations such as fairness. Moving forward, we call upon educators to focus on the development of realistic case studies that invite students to interrogate the role of an engineer.
APA
Weerts, H.J.P. & Pechenizkiy, M.. (2022). Teaching Responsible Machine Learning to Engineers. Proceedings of the Second Teaching Machine Learning and Artificial Intelligence Workshop, in Proceedings of Machine Learning Research 170:40-45 Available from https://proceedings.mlr.press/v170/weerts22a.html.

Related Material