This thesis investigates two interrelated issues: the tendency of automated decision-making (ADM) systems to exacerbate gender bias, and the extent to which current European Data Protection...Show moreThis thesis investigates two interrelated issues: the tendency of automated decision-making (ADM) systems to exacerbate gender bias, and the extent to which current European Data Protection legislation (GDPR) both promises and delivers a right to explanation of decisions reached by those systems. The thesis has high philosophical and societal relevance, and engages fluently with a variety of important discourses: technical discussions of artificial intelligence, feminist scholarship, and commentaries on EU legal texts. After an introduction on machine learning and algorithms, the thesis moves to examinating those parts in the GDPR that address ADM, in order to clarify the way they are regulated. In the second and in the third chapter, problems such as the black box, different types of bias, technological design and neutrality are discussed. Gender bias are presented and many cases are discussed in order to provide reason of this growing phenomenon. A central topic of investigation is that of data representativeness, or how women data lack from our daily infrastructure at a point that discrimination normally occurs. This thesis ultimately seeks to provide a new framework for the introduction of a new feminist ethics of technology, that addresses bias and data collection in an intersectional way and especially that claims for new regulations to be discussed.Show less
