@proceedings {1536,
	title = {A Value Analysis of Machine Learning-based Usable Privacy},
	year = {2022},
	publisher = {IFIP Working Group 8.2},
	address = {Copenhagen, Denmark},
	abstract = {With the ubiquitous presence of machine learning based decision making in the digitalization of various processes, workflows and solutions across every functional area of our society, it is important to understand the value of such systems. Especially, to understand the value of them in the light of end user{\textquoteright}s privacy because machine learning programs learn accurate inferences by inductively extracting information from various pieces of data. We aim to study the relationship of a machine learning based detection of anti-privacy user interface design patterns (dark patterns) and self-determination of the end users, in order to analyze the value of the machine learning technology. To this end, we are in the process of i) investigating the perspectives of practitioners in the field of machine learning-based usable privacy and ii) developing a machine learning program that detects dark patterns in website{\textquoteright}s consent form designs. Following which, we plan to conduct user studies to empirically study end users perspectives for example on machine learning assisted decision on one{\textquoteright}s online privacy.},
	author = {Jenni Reuben and Ala Sarah Alaqra}
}
