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Machine learning is impacting modern society at large, thanks to its increasing potential to effciently and effectively model complex and heterogeneous phenomena. While machine learning models can achieve very accurate predictions in many applications, they are not infallible. In some cases, machine learning models can deliver unreasonable outcomes. For example, deep neural networks for self-driving cars have been found to provide wrong steering directions based on the lighting conditions of street lanes (e.g., due to cloudy weather). In other cases, models can capture and reflect unwanted biases that
were concealed in the training data. For example, deep neural networks used to predict likely jobs and social status of people based on their pictures, were found to consistently discriminate based on gender and ethnicity–this was later attributed to human bias in the labels of the training data.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
Supervisors/Advisors
Award date8 Jun 2020
Print ISBNs978-94-6384-138-2
DOIs
Publication statusPublished - 2020

    Research areas

  • evolutionary algorithms, genetic programming, machine learning, pediatric cancer, radiotherapy

ID: 72936008