
Contents
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A. Introduction A. Introduction
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B. Affective Features of the Challenge B. Affective Features of the Challenge
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1. Checks, balances, and compliance 1. Checks, balances, and compliance
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2. Varying standards of fairness 2. Varying standards of fairness
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C. Investigating the Features—Inaccuracy and E-AI C. Investigating the Features—Inaccuracy and E-AI
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1. Fairness checks and balances 1. Fairness checks and balances
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(a) Delineating the role(s) of fairness (a) Delineating the role(s) of fairness
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(b) Fair processing and fair choices (b) Fair processing and fair choices
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2. Embedding (un)fairness 2. Embedding (un)fairness
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(a) Impact assessments and design (a) Impact assessments and design
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(b) Risking lawfulness and legitimacy (b) Risking lawfulness and legitimacy
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D. Limits of the Challenge D. Limits of the Challenge
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E. Conclusion E. Conclusion
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Cite
Abstract
Data protection law is inherently intertwined with the notion of fairness. Determining what is ‘fair’ however is context-dependent. In addition, those processing personal data are not invested in compliance and, perhaps more fundamentally, the complexities of the framework require a level of commitment to ensuring fair processing given the vagueness of what is considered fair and/or unfair. The challenge then is that although data protection law aims to provide an environment in which data subjects can make subjective choices, it affords controllers significant latitude and arguably allows them scope not to comply in a meaningful manner. The purpose of this chapter is to explore this disconnect via the fairness and accountability mechanisms provided for in data protection law. To demonstrate this disconnect, the analysis in part explores the development of Emotional Artificial Intelligence as a case study through the lens of the EU’s General Data Protection Regulation.
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