Fair or Unfair Differentiation? Reconsidering the Concept of Equality for the Regulation of Algorithmically Guided Decision-Making

Algorithms are increasingly relied upon to help decision-makers automate, streamline, structure and guide a variety of decision-making processes, both trivial and critical, in both the public and private sector. In this data-driven environment, people and groups of people are continuously classified, categorised, ranked and scored on a variety of features or attributes, such as their characteristics, interests, behaviour and preferences. For decision-subjects, the consequences of these classification acts can be significant: they affect the choices and options they are presented, the interactions and relationships they hold with others and themselves, the opportunities they are given, and the burdens and benefits they carry. Furthermore, when applied on a large enough scale, these instances of differentiation may also initiate social change. This dissertation is concerned with one particular type of injustice that may emerge from the deployment of algorithmic decision-making system: the introduction of unjustifiable (in)equality brought about by differentiation acts that take place within and as part of these systems. Due to the complexity of the digital environment and the distinctive characteristics algorithmically guided decisions exhibit however, it has become increasingly difficult to assess whether the decisions these knowledge and data-driven systems inform, and the (in)equalities they produce, can be justified. In this dissertation, I examine whether we can revitalise the concept of equality to guide the evaluation and regulation of algorithmically guided decision-making processes in light of the inequalities they produce. In my dissertation, I reposition and operationalise the notion of equality as a practicable and interpretative lens to strengthen the evaluation and regulation of algorithmically guided decision-making practices in light of the inequalities they (risk to) produce. In a first step, I define the algorithmic research context in which I want to operationalise the notion of equality. I explore a series of characteristics algorithmic systems exhibit that render the inequalities they generate distinctive in terms of their form and scope. Due to these unique characteristics, algorithmic inequalities have the potential to restructure the fabric of society alongside new and existing dimensions: they may not only reinforce existing social injustice, but they may also introduce new forms of (non-representational) injustice. Drawing inspirati