In many societies, algorithmic systems are increasingly being developed to support or even replace social decisions. Examples range from the allocation of social benefits to the prioritization of aid services to the evaluation of social behavior. The goal is to make decisions faster, more objective, and more efficient in order to distribute societal resources more fairly.
At first, these systems appear to be promising tools: they analyze large amounts of data, recognize patterns, and make decisions based on established criteria. But over time, complex challenges emerge: algorithms can reinforce existing social inequalities by adopting biases from the training data or systematically disadvantaging certain groups. Furthermore, the decision-making processes are often opaque, which makes trust and acceptance in society more difficult. The reduction of complex social contexts to measurable data leads to a dehumanization of important decisions and can exacerbate conflicts over justice and fairness. Developers, policymakers, and society face the task of understanding the technical and social causes of these effects and assessing the impact on social participation, transparency, and democratic control.
Question:
Which technical and societal factors can cause algorithmic systems for social justice to reinforce existing inequalities despite their potential, and how do these factors influence the requirements for data quality, transparency, participation, and ethical design of such systems in the future?
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