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Abstract

Widespread use of Artificial Intelligence in all areas of today’s society creates a unique problem: algorithms used in decision-making are generally not understandable to those without a background in data science. Thus, those who use out-of-the-box Machine Learning (ML) approaches in their work and those affected by these approaches are often not in a position to analyze their outcomes and applicability.

Our paper describes and evaluates our undergraduate course at the University of Minnesota Morris, which fosters understanding of the main ideas behind ML. With Communication, Media & Rhetoric and Computer Science faculty expertise, students from a variety of majors, most with no prior background in data science or computing, reviewed the scope of applicability of algorithms and became aware of possible biases, ‘politics’ and pitfalls. After discussing articles on societal attitudes towards technology, explaining key concepts behind ML algorithms (training and dependence on data), and constructing a decision tree as an example of an algorithm, we attempted to develop guidelines for ‘best practices’ for use of algorithms.

Students presented a ‘case analysis’ capstone paper on an application of machine learning in society. Paper topics included: use of algorithms by Child Protection Services, ‘deepfake’ videos, genetic testing. The level of papers was indicative of students’ strong interest in the subject and their ability to understand key terms and ideas behind algorithms, societal perception and misconceptions of use of algorithms, and their ability to identify good and problematic practices in use of algorithms.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License

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