Inclusivity and Transparency in Machine Learning Model Auditing
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2021
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Haverford College. Department of Computer Science
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Thesis
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eng
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Open Access
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Abstract
The goal of this literature review is to highlight the importance of inclusivity in auditing algorithms. Machine Learning (ML) models affect many aspects of our lives such as providing us with relevant ads or predicting our movie preferences. Thus, their auditing and critique is integral to ensuring they are done in a holistic manner. Many papers who discuss and research auditing have good intentions. There is an important focus on gender bias in these models in the world of Human Computer Interaction (HCI). However, many of these papers fail to take transgender and non-binary people into account. Many methods used to determine if a model is biased end up using biased methods themselves. They often have no access to self reported gender and, therefore, default to outdated methods such as visual cues and stereotypes. This paper will highlight the importance of transparency on the part of ML models as it aids these audits as well as how the audits themselves can be improved in an inclusive way.