Against Fair-Washing: A Critical Analysis of AI-Driven Decision Support Systems

This paper critically explores the assumption that fairness, here broadly understood as in automated decision support systems, as inherently ethical equates to ethical algorithms. While fairness has become a key benchmark for ethical assessment in AI applications, this work argues that fairness does not always guarantee ethical results. By drawing on consequentialist, deontological, and care ethics perspectives, the paper highlights that uncritical reliance on fairness may overlook individual uniqueness and reinforce existing power structures, as algorithms tend to reduce complex human identities to fixed categories. Additionally, the impartiality often valued in the debate over AI systems may be ethically unsuitable in contexts where recognizing individual singularity and situational nuances is essential. Finally, the paper advocates for a transparent evaluation of the ethical frameworks that shape AI systems, emphasizing the risk of a surreptitious introduction of ethical visions and purposes. The concept of “fair- washing” is adopted, a practice that risks portraying fairness in automated systems as inherently ethical because of fair, sidestepping responsibility to define and scrutinize the goals of these technologies.